This document summarizes feature selection techniques that have been used for intrusion detection systems (IDS), including both bio-inspired and non-bio-inspired algorithms. It first provides background on IDS and the importance of feature selection for handling large amounts of network traffic data. It then describes common feature selection methods like wrapper, filter, and hybrid approaches. The document focuses on bio-inspired optimization algorithms that have been applied for feature selection, including evolutionary algorithms like genetic algorithms and genetic programming as well as swarm-based algorithms. It provides an overview of how these bio-inspired techniques work and their advantages for feature selection in IDS.
Evaluation of Integrated Digital Forensics Investigation Framework for the In...IJECEIAES
The handling of digital evidence can become an evidence of a determination that crimes have been committed or may give links between crime and its victims or crime and the culprit. Soft System Methodology (SSM) is a method of evaluation to compare a conceptual model with a process in the real world, so deficiencies of the conceptual model can be revealed thus it can perform corrective action against the conceptual model, thus there is no difference between the conceptual model and the real activity. Evaluation on the IDFIF stage is only done on a reactive and proactive process stages in the process so that the IDFIF model can be more flexible and can be applied on the investigation process of a smartphone.
Data Mining Techniques for Providing Network Security through Intrusion Detec...IJAAS Team
Intrusion Detection Systems are playing major role in network security in this internet world. Many researchers have been introduced number of intrusion detection systems in the past. Even though, no system was detected all kind of attacks and achieved better detection accuracy. Most of the intrusion detection systems are used data mining techniques such as clustering, outlier detection, classification, classification through learning techniques. Most of the researchers have been applied soft computing techniques for making effective decision over the network dataset for enhancing the detection accuracy in Intrusion Detection System. Few researchers also applied artificial intelligence techniques along with data mining algorithms for making dynamic decision. This paper discusses about the number of intrusion detection systems that are proposed for providing network security. Finally, comparative analysis made between the existing systems and suggested some new ideas for enhancing the performance of the existing systems.
A PROPOSED MODEL FOR DIMENSIONALITY REDUCTION TO IMPROVE THE CLASSIFICATION C...IJNSA Journal
Over the past few years, intrusion protection systems have drawn a mature research area in the field of computer networks. The problem of excessive features has a significant impact on
intrusion detection performance. The use of machine learning algorithms in many previous researches has been used to identify network traffic, harmful or normal. Therefore, to obtain the accuracy, we must reduce the dimensionality of the data used. A new model design based on a combination of feature selection and machine learning algorithms is proposed in this paper. This model depends on selected genes from every feature to increase the accuracy of intrusion detection systems. We selected from features content only ones which impact in attack detection. The performance has been evaluated based on a comparison of several known algorithms. The NSL-KDD dataset is used for examining classification. The proposed model outperformed the other learning approaches with accuracy 98.8 %.
Automatic Insider Threat Detection in E-mail System using N-gram TechniqueIRJET Journal
This document summarizes research on automatic insider threat detection in email systems using n-gram techniques. It discusses how n-grams can be used to classify documents and detect potential data leakage through email. The system would verify emails sent outside the network using SHA, n-grams and thresholds. If a threat is detected, the user would be blocked. The document also provides a literature review on 10 other papers related to insider threat detection using methods like user profiling, activity logs, data mining and visualization techniques. It describes how n-grams work by breaking words into character sequences and creating profiles based on frequency to classify documents.
Critical analysis of genetic algorithm based IDS and an approach for detecti...IOSR Journals
This document discusses and analyzes different approaches to intrusion detection systems (IDS), including genetic algorithm approaches, data mining approaches, and intrusion detection in mobile ad hoc networks (MANETs). It provides an overview of genetic algorithm based IDS and data mining techniques for IDS. It also proposes a hybrid approach that combines anomaly detection and signature-based IDS to detect both known and unknown attacks. The document concludes that a critical analysis of various IDS techniques was performed and a hybrid approach is promising for intrusion detection in MANETs.
This document discusses feature selection techniques for intrusion detection systems. It begins with background on intrusion detection and challenges related to large datasets. It then describes three common feature selection algorithms: Correlation-based Feature Selection (CFS), Information Gain (IG), and Gain Ratio (GR). The document proposes a fusion model that applies these three algorithms to select features, then uses genetic algorithm and naive Bayes classification to evaluate performance. It conducted experiments on the KDD Cup 99 intrusion detection dataset to compare the proposed fusion method to the individual feature selection algorithms.
COMPUTER INTRUSION DETECTION BY TWOOBJECTIVE FUZZY GENETIC ALGORITHMcscpconf
This document describes a computer intrusion detection system that uses a two-objective fuzzy genetic algorithm. The system aims to (1) maximize detection rate and (2) minimize the number of rules while maintaining a low false rate. It generates fuzzy rules from training data to classify known attack patterns. A genetic algorithm is then applied to find non-dominated sets of rules that optimize the two objectives. The best performing rule set is used as signatures to detect known intrusions in test data.
1.[1 9]a genetic algorithm based elucidation for improving intrusion detectio...Alexander Decker
This document summarizes a research paper that proposes using a genetic algorithm to improve intrusion detection. The paper aims to reduce features from the KDD Cup 99 dataset and generate a rule set using genetic algorithms to detect intrusions with a condensed feature set. The genetic algorithm is used to evolve rules from the reduced training data, with a fitness function evaluating rule quality. Experiments and evaluations are conducted on the KDD Cup 99 dataset to test the proposed method.
Evaluation of Integrated Digital Forensics Investigation Framework for the In...IJECEIAES
The handling of digital evidence can become an evidence of a determination that crimes have been committed or may give links between crime and its victims or crime and the culprit. Soft System Methodology (SSM) is a method of evaluation to compare a conceptual model with a process in the real world, so deficiencies of the conceptual model can be revealed thus it can perform corrective action against the conceptual model, thus there is no difference between the conceptual model and the real activity. Evaluation on the IDFIF stage is only done on a reactive and proactive process stages in the process so that the IDFIF model can be more flexible and can be applied on the investigation process of a smartphone.
Data Mining Techniques for Providing Network Security through Intrusion Detec...IJAAS Team
Intrusion Detection Systems are playing major role in network security in this internet world. Many researchers have been introduced number of intrusion detection systems in the past. Even though, no system was detected all kind of attacks and achieved better detection accuracy. Most of the intrusion detection systems are used data mining techniques such as clustering, outlier detection, classification, classification through learning techniques. Most of the researchers have been applied soft computing techniques for making effective decision over the network dataset for enhancing the detection accuracy in Intrusion Detection System. Few researchers also applied artificial intelligence techniques along with data mining algorithms for making dynamic decision. This paper discusses about the number of intrusion detection systems that are proposed for providing network security. Finally, comparative analysis made between the existing systems and suggested some new ideas for enhancing the performance of the existing systems.
A PROPOSED MODEL FOR DIMENSIONALITY REDUCTION TO IMPROVE THE CLASSIFICATION C...IJNSA Journal
Over the past few years, intrusion protection systems have drawn a mature research area in the field of computer networks. The problem of excessive features has a significant impact on
intrusion detection performance. The use of machine learning algorithms in many previous researches has been used to identify network traffic, harmful or normal. Therefore, to obtain the accuracy, we must reduce the dimensionality of the data used. A new model design based on a combination of feature selection and machine learning algorithms is proposed in this paper. This model depends on selected genes from every feature to increase the accuracy of intrusion detection systems. We selected from features content only ones which impact in attack detection. The performance has been evaluated based on a comparison of several known algorithms. The NSL-KDD dataset is used for examining classification. The proposed model outperformed the other learning approaches with accuracy 98.8 %.
Automatic Insider Threat Detection in E-mail System using N-gram TechniqueIRJET Journal
This document summarizes research on automatic insider threat detection in email systems using n-gram techniques. It discusses how n-grams can be used to classify documents and detect potential data leakage through email. The system would verify emails sent outside the network using SHA, n-grams and thresholds. If a threat is detected, the user would be blocked. The document also provides a literature review on 10 other papers related to insider threat detection using methods like user profiling, activity logs, data mining and visualization techniques. It describes how n-grams work by breaking words into character sequences and creating profiles based on frequency to classify documents.
Critical analysis of genetic algorithm based IDS and an approach for detecti...IOSR Journals
This document discusses and analyzes different approaches to intrusion detection systems (IDS), including genetic algorithm approaches, data mining approaches, and intrusion detection in mobile ad hoc networks (MANETs). It provides an overview of genetic algorithm based IDS and data mining techniques for IDS. It also proposes a hybrid approach that combines anomaly detection and signature-based IDS to detect both known and unknown attacks. The document concludes that a critical analysis of various IDS techniques was performed and a hybrid approach is promising for intrusion detection in MANETs.
This document discusses feature selection techniques for intrusion detection systems. It begins with background on intrusion detection and challenges related to large datasets. It then describes three common feature selection algorithms: Correlation-based Feature Selection (CFS), Information Gain (IG), and Gain Ratio (GR). The document proposes a fusion model that applies these three algorithms to select features, then uses genetic algorithm and naive Bayes classification to evaluate performance. It conducted experiments on the KDD Cup 99 intrusion detection dataset to compare the proposed fusion method to the individual feature selection algorithms.
COMPUTER INTRUSION DETECTION BY TWOOBJECTIVE FUZZY GENETIC ALGORITHMcscpconf
This document describes a computer intrusion detection system that uses a two-objective fuzzy genetic algorithm. The system aims to (1) maximize detection rate and (2) minimize the number of rules while maintaining a low false rate. It generates fuzzy rules from training data to classify known attack patterns. A genetic algorithm is then applied to find non-dominated sets of rules that optimize the two objectives. The best performing rule set is used as signatures to detect known intrusions in test data.
1.[1 9]a genetic algorithm based elucidation for improving intrusion detectio...Alexander Decker
This document summarizes a research paper that proposes using a genetic algorithm to improve intrusion detection. The paper aims to reduce features from the KDD Cup 99 dataset and generate a rule set using genetic algorithms to detect intrusions with a condensed feature set. The genetic algorithm is used to evolve rules from the reduced training data, with a fitness function evaluating rule quality. Experiments and evaluations are conducted on the KDD Cup 99 dataset to test the proposed method.
11.a genetic algorithm based elucidation for improving intrusion detection th...Alexander Decker
This document summarizes a research paper that proposes using a genetic algorithm to improve intrusion detection. The paper aims to reduce features from the KDD Cup 99 dataset and generate a rule set using genetic algorithms to detect intrusions. The genetic algorithm evolves rules over generations to maximize fitness. Experiments show this approach can improve detection rates and reduce false alarms compared to existing intrusion detection systems.
Correlation of artificial neural network classification and nfrs attribute fi...eSAT Journals
Abstract
Mostly 5 to 15% of the women in the stage of reproduction face the disease called Polycystic Ovarian Syndrome (PCOS) which is the multifaceted, heterogeneous and complex. The long term consequences diseases like endometrial hyperplasia, type 2 diabetes mellitus and coronary disease are caused by the polycystic ovaries, chronic anovulation and hyperandrogenism are characterized with the resistance of insulin and the hypertension, abdominal obesity and dyslipidemia and hyperinsulinemia are called as Metabolic syndrome (frequent metabolic traits) The above cause the common disease called Anovulatory infertility. Computer based information along with advanced Data mining techniques are used for appropriate results. Classification is a classic data mining task, with roots in machine learning. Naïve Bayesian, Artificial Neural Network, Decision Tree, Support Vector Machines are the classification tasks in the data mining. Feature selection methods involve generation of the subset, evaluation of each subset, criteria for stopping the search and validation procedures. The characteristics of the search method used are important with respect to the time efficiency of the feature selection methods. PCA (Principle Component Analysis), Information gain Subset Evaluation, Fuzzy rough set evaluation, Correlation based Feature Selection (CFS) are some of the feature selection techniques, greedy first search, ranker etc are the search algorithms that are used in the feature selection. In this paper, a new algorithm which is based on Fuzzy neural subset evaluation and artificial neural network is proposed which reduces the task of classification and feature selection separately. This algorithm combines the neural fuzzy rough subset evaluation and artificial neural network together for the better performance than doing the tasks separately.
Keywords: ANN, SVM, PCA, CFS
INTRUSION DETECTION USING FEATURE SELECTION AND MACHINE LEARNING ALGORITHM WI...ijcsit
This document describes a proposed hybrid intrusion detection model that uses feature selection and machine learning algorithms with misuse detection. The model first selects important features from the NSL-KDD dataset and generates rules based on the behaviors of those features using J48 and CART algorithms. These rules are then used to build an intrusion detection framework that is tested on the NSL-KDD dataset, achieving an accuracy of 88.23%, outperforming other models that require prior learning of attacks. The proposed model works on the concept of misuse detection and can detect intrusions based on feature behaviors without any previous training.
Fuzzy Analytic Hierarchy Based DBMS Selection In Turkish National Identity Ca...Ferhat Ozgur Catak
Database Management Systems (DBMS) play an important role to support
enterprise application developments. Selection of the right DBMS is a crucial decision for
software engineering process. This selection requires optimizing a number of criteria.
Evaluation and selection of DBMS among several candidates tend to be very complex. It
requires both quantitative and qualitative issues. Wrong selection of DBMS will have a
negative effect on the development of enterprise application. It can turn out to be costly and adversely affect business process. The following study focuses on the evaluation of a multi criteria
decision problem by the usage of fuzzy logic. We will demonstrate the methodological considerations
regarding to group decision and fuzziness based on the DBMS selection problem. We developed a new
Fuzzy AHP based decision model which is formulated and proposed to select a DBMS easily. In this
decision model, first, main criteria and their sub criteria are determined for the evaluation. Then these
criteria are weighted by pair-wise comparison, and then DBMS alternatives are evaluated by assigning a
rating scale.
Improving the performance of Intrusion detection systemsyasmen essam
Intrusion detection systems (IDS) are widely studied by
researchers nowadays due to the dramatic growth in
network-based technologies. Policy violations and
unauthorized access is in turn increasing which makes
intrusion detection systems of great importance. Existing
approaches to improve intrusion detection systems focus on feature selection or reduction since some features are
irrelevant or redundant which when removed improve the
accuracy as well as the learning time.
IRJET- An Intrusion Detection Framework based on Binary Classifiers Optimized...IRJET Journal
This document proposes an intrusion detection framework that uses multiple binary classifiers optimized by a genetic algorithm. It analyzes decision trees, naive Bayes, and support vector machines to classify network connections as normal or attacks based on the NSL-KDD dataset. The classifiers are aggregated and a genetic algorithm is used to generate high-quality solutions. Experimental results show that the proposed method achieves 99% accuracy in intrusion detection, outperforming single classification techniques. The goal is to develop an application that can efficiently process network data and identify intrusion risks.
This document presents a proposed hybrid intrusion detection system that combines k-means clustering, k-nearest neighbor classification, and decision table majority rule-based approaches. The system is evaluated on the KDD-99 dataset to detect intrusions and classify them into four categories: R2L, DoS, Probe, and U2R. The goal is to decrease the false alarm rate and increase accuracy and detection rate compared to existing intrusion detection systems. The proposed system applies k-means clustering first, then k-nearest neighbor classification, and finally decision table majority rules. Results show the proposed hybrid approach improves performance metrics compared to existing techniques.
Software Bug Detection Algorithm using Data mining TechniquesAM Publications
The main aim of software development is to develop high quality software and high quality software is
developed using enormous amount of software engineering data. The software engineering data can be used to gain
empirically based understanding of software development. The meaning full information can be extracted using
various data mining techniques. As Data Mining for Secure Software Engineering improves software productivity and
quality, software engineers are increasingly applying data mining algorithms to various software engineering tasks.
However mining software engineering data poses several challenges, requiring various algorithms to effectively mine
sequences, graphs and text from such data. Software engineering data includes code bases, execution traces,
historical code changes, mailing lists and bug data bases. They contains a wealth of information about a projectsstatus,
progress and evolution. Using well established data mining techniques, practitioners and researchers can
explore the potential of this valuable data in order to better manage their projects and do produce higher-quality
software systems that are delivered on time and within budget
IRJET- Improving Cyber Security using Artificial IntelligenceIRJET Journal
This document discusses using artificial intelligence techniques like machine learning algorithms to improve cyber security. It proposes a methodology that uses Splunk to extract relevant fields from cybersecurity data, feeds that into a K-means clustering algorithm to form attack clusters, then sends those clusters to individual artificial neural networks (ANNs). The aggregated ANN results are then fed into a support vector machine (SVM) which classifies attacks as malicious, non-malicious, or benign. Testing this approach on a dataset achieved a classification accuracy of over 92% when using Splunk, K-means, ANNs, and SVM together.
This document discusses information technology and database concepts. It covers relational, hierarchical, and network database models. It also discusses two-tier and three-tier architecture. The document then discusses system analysis and design, including defining a system, the software development life cycle, and the different phases of system analysis, design, coding, testing, implementation, and maintenance.
Software Effort Estimation using Neuro Fuzzy Inference System: Past and Presentrahulmonikasharma
Most important reason for project failure is poor effort estimation. Software development effort estimation is needed for assigning appropriate team members for development, allocating resources for software development, binding etc. Inaccurate software estimation may lead to delay in project, over-budget or cancellation of the project. But the effort estimation models are not very efficient. In this paper, we are analyzing the new approach for estimation i.e. Neuro Fuzzy Inference System (NFIS). It is a mixture model that consolidates the components of artificial neural network with fuzzy logic for giving a better estimation.
A Survey On Genetic Algorithm For Intrusion Detection SystemIJARIIE JOURNAL
The Internet has become a part of daily life and an essential tool today. Internet has been used as an important component of
business models. Therefore, It is very important to maintain a high level security to ensure safe and trusted communication of
information between various organizations.
Intrusion Detection Systems have become a needful component in terms of computer and network security. Intrusion detection is
one of the important security constraints for maintaining the integrity of information. Intrusion detection systems are the tools
used for prevention and detection of threats to computer systems. Various approaches have been applied in past that are less
effective to curb the menace of intrusion.
In this paper, a survey on applications of genetic algorithms in intrusion detection systems is carried out.
Applications of Pattern Recognition Algorithms in Agriculture: A ReviewEswar Publications
Pattern recognition has its roots in artificial intelligence and is a branch of machine learning that focuses on the recognition of patterns and regularities in data. Data can be in the form of image, text, video or any other format. Under normal scenario, pattern recognition is implemented by first formalizing a problem, explain and at last visualize the pattern. In contrast to pattern matching, pattern recognition algorithms generally provide a fair result for all possible inputs by considering statistical variations. Probabilistic classifiers have supported
Agricultural statistical inference for decades. Potential applications of this technique in agriculture are numerous
like pattern recognition from satellite imagery, identifying the type of disease from leaf image, weed detection etc.
This paper explores employment of pattern recognition in an agricultural domain.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Meta Classification Technique for Improving Credit Card Fraud Detection IJSTA
This document summarizes a research paper that proposes an ensemble meta classification technique using D-TREE, SVM, KNN and FA algorithms to improve credit card fraud detection. The researchers tested their proposed method on credit card transaction data and found that the ensemble method of FA-D-TREE, SVM, KNN outperformed using the individual algorithms alone. It achieved a lower error rate compared to other approaches. The document provides background on credit card fraud detection and reviews related work applying neural networks, decision trees, SVM and frequent item set mining methods.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
This document discusses feature selection techniques for intrusion detection systems. It begins with background on intrusion detection and challenges related to large datasets. It then describes three common feature selection algorithms: Correlation-based Feature Selection (CFS), Information Gain (IG), and Gain Ratio (GR). The document proposes a fusion model that applies these three algorithms to select features, then uses genetic algorithm and naive Bayes classification to evaluate performance. It conducted experiments on the KDD Cup 99 intrusion detection dataset to compare the proposed fusion method to the individual feature selection algorithms.
Outstanding to the promotion of the Internet and local networks, interruption occasions to computer
systems are emerging. Intrusion detection systems are becoming progressively vital in retaining
appropriate network safety. IDS is a software or hardware device that deals with attacks by gathering
information from a numerous system and network sources, then evaluating signs of security complexities.
Enterprise networked systems are unsurprisingly unprotected to the growing threats posed by hackers as
well as malicious users inside to a network. IDS technology is one of the significant tools used now-a-days,
to counter such threat. In this research we have proposed framework by using advance feature selection
and dimensionality reduction technique we can reduce IDS data then applying Fuzzy ARTMAP classifier
we can find intrusions so that we get accurate results within less time. Feature selection, as an active
research area in decreasing dimensionality, eliminating unrelated data, developing learning correctness,
and improving result unambiguousness.
Feature Selection using the Concept of Peafowl Mating in IDSIJCNCJournal
Cloud computing has high applicability as an Internet based service that relies on sharing computing resources. Cloud computing provides services that are Infrastructure based, Platform based and Software based. The popularity of this technology is due to its superb performance, high level of computing ability, low cost of services, scalability, availability and flexibility. The obtainability and openness of data in cloud environment make it vulnerable to the world of cyber-attacks. To detect the attacks Intrusion Detection System is used, that can identify the attacks and ensure information security. Such a coherent and proficient Intrusion Detection System is proposed in this paper to achieve higher certainty levels regarding safety in cloud environment. In this paper, the mating behavior of peafowl is incorporated into an optimization algorithm which in turn is used as a feature selection algorithm. The algorithm is used to reduce the huge size of cloud data so that the IDS can work efficiently on the cloud to detect intrusions. The proposed model has been experimented with NSL-KDD dataset as well as Kyoto dataset and have proved to be a better as well as an efficient IDS.
Feature Selection using the Concept of Peafowl Mating in IDSIJCNCJournal
Cloud computing has high applicability as an Internet based service that relies on sharing computing resources. Cloud computing provides services that are Infrastructure based, Platform based and Software based. The popularity of this technology is due to its superb performance, high level of computing ability, low cost of services, scalability, availability and flexibility. The obtainability and openness of data in cloud environment make it vulnerable to the world of cyber-attacks. To detect the attacks Intrusion Detection System is used, that can identify the attacks and ensure information security. Such a coherent and proficient Intrusion Detection System is proposed in this paper to achieve higher certainty levels regarding safety in cloud environment. In this paper, the mating behavior of peafowl is incorporated into an optimization algorithm which in turn is used as a feature selection algorithm. The algorithm is used to reduce the huge size of cloud data so that the IDS can work efficiently on the cloud to detect intrusions. The proposed model has been experimented with NSL-KDD dataset as well as Kyoto dataset and have proved to be a better as well as an efficient IDS.
Intrusion Detection System (IDS) Development Using Tree-Based Machine Learnin...IJCNCJournal
The paper proposes a two-phase classification method for detecting anomalies in network traffic, aiming to tackle the challenges of imbalance and feature selection. The study uses Information Gain to select relevant features and evaluates its performance on the CICIDS-2018 dataset with various classifiers. Results indicate that the ensemble classifier achieved the highest accuracy, precision, and recall. The proposed method addresses challenges in intrusion detection and highlights the effectiveness of ensemble classifiers in improving anomaly detection accuracy. Also, the quantity of pertinent characteristics chosen by Information Gain has a considerable impact on the F1-score and detection accuracy. Specifically, the Ensemble Learning achieved the highest accuracy of 98.36% and F1-score of 97.98% using the relevant selected features.
Intrusion Detection System(IDS) Development Using Tree-Based Machine Learning...IJCNCJournal
The paper proposes a two-phase classification method for detecting anomalies in network traffic, aiming to tackle the challenges of imbalance and feature selection. The study uses Information Gain to select relevant features and evaluates its performance on the CICIDS-2018 dataset with various classifiers. Results indicate that the ensemble classifier achieved the highest accuracy, precision, and recall. The proposed method addresses challenges in intrusion detection and highlights the effectiveness of ensemble classifiers in improving anomaly detection accuracy. Also, the quantity of pertinent characteristics chosen by Information Gain has a considerable impact on the F1-score and detection accuracy. Specifically, the Ensemble Learning achieved the highest accuracy of 98.36% and F1-score of 97.98% using the relevant selected features.
11.a genetic algorithm based elucidation for improving intrusion detection th...Alexander Decker
This document summarizes a research paper that proposes using a genetic algorithm to improve intrusion detection. The paper aims to reduce features from the KDD Cup 99 dataset and generate a rule set using genetic algorithms to detect intrusions. The genetic algorithm evolves rules over generations to maximize fitness. Experiments show this approach can improve detection rates and reduce false alarms compared to existing intrusion detection systems.
Correlation of artificial neural network classification and nfrs attribute fi...eSAT Journals
Abstract
Mostly 5 to 15% of the women in the stage of reproduction face the disease called Polycystic Ovarian Syndrome (PCOS) which is the multifaceted, heterogeneous and complex. The long term consequences diseases like endometrial hyperplasia, type 2 diabetes mellitus and coronary disease are caused by the polycystic ovaries, chronic anovulation and hyperandrogenism are characterized with the resistance of insulin and the hypertension, abdominal obesity and dyslipidemia and hyperinsulinemia are called as Metabolic syndrome (frequent metabolic traits) The above cause the common disease called Anovulatory infertility. Computer based information along with advanced Data mining techniques are used for appropriate results. Classification is a classic data mining task, with roots in machine learning. Naïve Bayesian, Artificial Neural Network, Decision Tree, Support Vector Machines are the classification tasks in the data mining. Feature selection methods involve generation of the subset, evaluation of each subset, criteria for stopping the search and validation procedures. The characteristics of the search method used are important with respect to the time efficiency of the feature selection methods. PCA (Principle Component Analysis), Information gain Subset Evaluation, Fuzzy rough set evaluation, Correlation based Feature Selection (CFS) are some of the feature selection techniques, greedy first search, ranker etc are the search algorithms that are used in the feature selection. In this paper, a new algorithm which is based on Fuzzy neural subset evaluation and artificial neural network is proposed which reduces the task of classification and feature selection separately. This algorithm combines the neural fuzzy rough subset evaluation and artificial neural network together for the better performance than doing the tasks separately.
Keywords: ANN, SVM, PCA, CFS
INTRUSION DETECTION USING FEATURE SELECTION AND MACHINE LEARNING ALGORITHM WI...ijcsit
This document describes a proposed hybrid intrusion detection model that uses feature selection and machine learning algorithms with misuse detection. The model first selects important features from the NSL-KDD dataset and generates rules based on the behaviors of those features using J48 and CART algorithms. These rules are then used to build an intrusion detection framework that is tested on the NSL-KDD dataset, achieving an accuracy of 88.23%, outperforming other models that require prior learning of attacks. The proposed model works on the concept of misuse detection and can detect intrusions based on feature behaviors without any previous training.
Fuzzy Analytic Hierarchy Based DBMS Selection In Turkish National Identity Ca...Ferhat Ozgur Catak
Database Management Systems (DBMS) play an important role to support
enterprise application developments. Selection of the right DBMS is a crucial decision for
software engineering process. This selection requires optimizing a number of criteria.
Evaluation and selection of DBMS among several candidates tend to be very complex. It
requires both quantitative and qualitative issues. Wrong selection of DBMS will have a
negative effect on the development of enterprise application. It can turn out to be costly and adversely affect business process. The following study focuses on the evaluation of a multi criteria
decision problem by the usage of fuzzy logic. We will demonstrate the methodological considerations
regarding to group decision and fuzziness based on the DBMS selection problem. We developed a new
Fuzzy AHP based decision model which is formulated and proposed to select a DBMS easily. In this
decision model, first, main criteria and their sub criteria are determined for the evaluation. Then these
criteria are weighted by pair-wise comparison, and then DBMS alternatives are evaluated by assigning a
rating scale.
Improving the performance of Intrusion detection systemsyasmen essam
Intrusion detection systems (IDS) are widely studied by
researchers nowadays due to the dramatic growth in
network-based technologies. Policy violations and
unauthorized access is in turn increasing which makes
intrusion detection systems of great importance. Existing
approaches to improve intrusion detection systems focus on feature selection or reduction since some features are
irrelevant or redundant which when removed improve the
accuracy as well as the learning time.
IRJET- An Intrusion Detection Framework based on Binary Classifiers Optimized...IRJET Journal
This document proposes an intrusion detection framework that uses multiple binary classifiers optimized by a genetic algorithm. It analyzes decision trees, naive Bayes, and support vector machines to classify network connections as normal or attacks based on the NSL-KDD dataset. The classifiers are aggregated and a genetic algorithm is used to generate high-quality solutions. Experimental results show that the proposed method achieves 99% accuracy in intrusion detection, outperforming single classification techniques. The goal is to develop an application that can efficiently process network data and identify intrusion risks.
This document presents a proposed hybrid intrusion detection system that combines k-means clustering, k-nearest neighbor classification, and decision table majority rule-based approaches. The system is evaluated on the KDD-99 dataset to detect intrusions and classify them into four categories: R2L, DoS, Probe, and U2R. The goal is to decrease the false alarm rate and increase accuracy and detection rate compared to existing intrusion detection systems. The proposed system applies k-means clustering first, then k-nearest neighbor classification, and finally decision table majority rules. Results show the proposed hybrid approach improves performance metrics compared to existing techniques.
Software Bug Detection Algorithm using Data mining TechniquesAM Publications
The main aim of software development is to develop high quality software and high quality software is
developed using enormous amount of software engineering data. The software engineering data can be used to gain
empirically based understanding of software development. The meaning full information can be extracted using
various data mining techniques. As Data Mining for Secure Software Engineering improves software productivity and
quality, software engineers are increasingly applying data mining algorithms to various software engineering tasks.
However mining software engineering data poses several challenges, requiring various algorithms to effectively mine
sequences, graphs and text from such data. Software engineering data includes code bases, execution traces,
historical code changes, mailing lists and bug data bases. They contains a wealth of information about a projectsstatus,
progress and evolution. Using well established data mining techniques, practitioners and researchers can
explore the potential of this valuable data in order to better manage their projects and do produce higher-quality
software systems that are delivered on time and within budget
IRJET- Improving Cyber Security using Artificial IntelligenceIRJET Journal
This document discusses using artificial intelligence techniques like machine learning algorithms to improve cyber security. It proposes a methodology that uses Splunk to extract relevant fields from cybersecurity data, feeds that into a K-means clustering algorithm to form attack clusters, then sends those clusters to individual artificial neural networks (ANNs). The aggregated ANN results are then fed into a support vector machine (SVM) which classifies attacks as malicious, non-malicious, or benign. Testing this approach on a dataset achieved a classification accuracy of over 92% when using Splunk, K-means, ANNs, and SVM together.
This document discusses information technology and database concepts. It covers relational, hierarchical, and network database models. It also discusses two-tier and three-tier architecture. The document then discusses system analysis and design, including defining a system, the software development life cycle, and the different phases of system analysis, design, coding, testing, implementation, and maintenance.
Software Effort Estimation using Neuro Fuzzy Inference System: Past and Presentrahulmonikasharma
Most important reason for project failure is poor effort estimation. Software development effort estimation is needed for assigning appropriate team members for development, allocating resources for software development, binding etc. Inaccurate software estimation may lead to delay in project, over-budget or cancellation of the project. But the effort estimation models are not very efficient. In this paper, we are analyzing the new approach for estimation i.e. Neuro Fuzzy Inference System (NFIS). It is a mixture model that consolidates the components of artificial neural network with fuzzy logic for giving a better estimation.
A Survey On Genetic Algorithm For Intrusion Detection SystemIJARIIE JOURNAL
The Internet has become a part of daily life and an essential tool today. Internet has been used as an important component of
business models. Therefore, It is very important to maintain a high level security to ensure safe and trusted communication of
information between various organizations.
Intrusion Detection Systems have become a needful component in terms of computer and network security. Intrusion detection is
one of the important security constraints for maintaining the integrity of information. Intrusion detection systems are the tools
used for prevention and detection of threats to computer systems. Various approaches have been applied in past that are less
effective to curb the menace of intrusion.
In this paper, a survey on applications of genetic algorithms in intrusion detection systems is carried out.
Applications of Pattern Recognition Algorithms in Agriculture: A ReviewEswar Publications
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This document discusses feature selection techniques for intrusion detection systems. It begins with background on intrusion detection and challenges related to large datasets. It then describes three common feature selection algorithms: Correlation-based Feature Selection (CFS), Information Gain (IG), and Gain Ratio (GR). The document proposes a fusion model that applies these three algorithms to select features, then uses genetic algorithm and naive Bayes classification to evaluate performance. It conducted experiments on the KDD Cup 99 intrusion detection dataset to compare the proposed fusion method to the individual feature selection algorithms.
Outstanding to the promotion of the Internet and local networks, interruption occasions to computer
systems are emerging. Intrusion detection systems are becoming progressively vital in retaining
appropriate network safety. IDS is a software or hardware device that deals with attacks by gathering
information from a numerous system and network sources, then evaluating signs of security complexities.
Enterprise networked systems are unsurprisingly unprotected to the growing threats posed by hackers as
well as malicious users inside to a network. IDS technology is one of the significant tools used now-a-days,
to counter such threat. In this research we have proposed framework by using advance feature selection
and dimensionality reduction technique we can reduce IDS data then applying Fuzzy ARTMAP classifier
we can find intrusions so that we get accurate results within less time. Feature selection, as an active
research area in decreasing dimensionality, eliminating unrelated data, developing learning correctness,
and improving result unambiguousness.
Feature Selection using the Concept of Peafowl Mating in IDSIJCNCJournal
Cloud computing has high applicability as an Internet based service that relies on sharing computing resources. Cloud computing provides services that are Infrastructure based, Platform based and Software based. The popularity of this technology is due to its superb performance, high level of computing ability, low cost of services, scalability, availability and flexibility. The obtainability and openness of data in cloud environment make it vulnerable to the world of cyber-attacks. To detect the attacks Intrusion Detection System is used, that can identify the attacks and ensure information security. Such a coherent and proficient Intrusion Detection System is proposed in this paper to achieve higher certainty levels regarding safety in cloud environment. In this paper, the mating behavior of peafowl is incorporated into an optimization algorithm which in turn is used as a feature selection algorithm. The algorithm is used to reduce the huge size of cloud data so that the IDS can work efficiently on the cloud to detect intrusions. The proposed model has been experimented with NSL-KDD dataset as well as Kyoto dataset and have proved to be a better as well as an efficient IDS.
Feature Selection using the Concept of Peafowl Mating in IDSIJCNCJournal
Cloud computing has high applicability as an Internet based service that relies on sharing computing resources. Cloud computing provides services that are Infrastructure based, Platform based and Software based. The popularity of this technology is due to its superb performance, high level of computing ability, low cost of services, scalability, availability and flexibility. The obtainability and openness of data in cloud environment make it vulnerable to the world of cyber-attacks. To detect the attacks Intrusion Detection System is used, that can identify the attacks and ensure information security. Such a coherent and proficient Intrusion Detection System is proposed in this paper to achieve higher certainty levels regarding safety in cloud environment. In this paper, the mating behavior of peafowl is incorporated into an optimization algorithm which in turn is used as a feature selection algorithm. The algorithm is used to reduce the huge size of cloud data so that the IDS can work efficiently on the cloud to detect intrusions. The proposed model has been experimented with NSL-KDD dataset as well as Kyoto dataset and have proved to be a better as well as an efficient IDS.
Intrusion Detection System (IDS) Development Using Tree-Based Machine Learnin...IJCNCJournal
The paper proposes a two-phase classification method for detecting anomalies in network traffic, aiming to tackle the challenges of imbalance and feature selection. The study uses Information Gain to select relevant features and evaluates its performance on the CICIDS-2018 dataset with various classifiers. Results indicate that the ensemble classifier achieved the highest accuracy, precision, and recall. The proposed method addresses challenges in intrusion detection and highlights the effectiveness of ensemble classifiers in improving anomaly detection accuracy. Also, the quantity of pertinent characteristics chosen by Information Gain has a considerable impact on the F1-score and detection accuracy. Specifically, the Ensemble Learning achieved the highest accuracy of 98.36% and F1-score of 97.98% using the relevant selected features.
Intrusion Detection System(IDS) Development Using Tree-Based Machine Learning...IJCNCJournal
The paper proposes a two-phase classification method for detecting anomalies in network traffic, aiming to tackle the challenges of imbalance and feature selection. The study uses Information Gain to select relevant features and evaluates its performance on the CICIDS-2018 dataset with various classifiers. Results indicate that the ensemble classifier achieved the highest accuracy, precision, and recall. The proposed method addresses challenges in intrusion detection and highlights the effectiveness of ensemble classifiers in improving anomaly detection accuracy. Also, the quantity of pertinent characteristics chosen by Information Gain has a considerable impact on the F1-score and detection accuracy. Specifically, the Ensemble Learning achieved the highest accuracy of 98.36% and F1-score of 97.98% using the relevant selected features.
Intrusion Detection System (IDS): Anomaly Detection using Outlier Detection A...Drjabez
This document describes a proposed approach for anomaly detection in intrusion detection systems using outlier detection. It begins with background on intrusion detection systems and issues with existing approaches. It then presents the proposed two-stage approach using outlier detection: 1) Training with large normal datasets in a distributed storage environment, and 2) Testing intrusion datasets to compute an error value compared to the trained model. If the error value exceeds a threshold, the test data is flagged as anomalous. Experimental results on network packet datasets demonstrate the approach can effectively identify anomalies.
The Practical Data Mining Model for Efficient IDS through Relational DatabasesIJRES Journal
Enterprise network information system is not only the platform for information sharing and information exchanging, but also the platform for enterprise production automation system and enterprise management system working together. As a result, the security defense of enterprise network information system does not only include information system network security and data security, but also include the security of network business running on information system network, which is the confidentiality, integrity, continuity and real-time of network business. Network security technology has become crucial in protecting government and industry computing infrastructure. Modern intrusion detection applications face complex requirements – they need to be reliable, extensible, easy to manage, and have low maintenance cost. In recent years, data mining-based intrusion detection systems (IDSs) have demonstrated high accuracy, good generalization to novel types of intrusion, and robust behavior in a changing environment. Still, significant challenges exist in the design and implementation of production quality IDSs. Incrementing components such as data transformations, model deployment, and cooperative distributed detection remain a labor intensive and complex engineering endeavor. This paper describes DAID, a database-centric architecture that leverages data mining within the Relational RDBMS to address these challenges. DAID also offers numerous advantages in terms of scheduling capabilities, alert infrastructure, data analysis tools, security, scalability, and reliability. DAID is illustrated with an Intrusion Detection Center application prototype that leverages existing functionality in Relational Database 10g. Intrusion detection system work at many levels in the network fabric and are taking the concept of security to a whole new sphere by incorporating intelligence as a tool to protect networks against un-authorized intrusions and newer forms of attack. We have described formal model for the construction of network security situation measurement based on d-s evidence theory, frequent mode, and sequence model extracted from the data on network security situation based on the knowledge found method and convert the pattern on the related rules of the network security situation, and automatic generation of network security situation.
Survey of Clustering Based Detection using IDS Technique IRJET Journal
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This document provides an overview of machine learning approaches for intrusion detection systems (IDS). It discusses how IDS use data mining techniques like classification, clustering, and association rule mining to detect network intrusions based on patterns in data. The document reviews several papers applying methods like ant colony optimization, support vector machines, genetic algorithms, and convolutional neural networks to classify network activities as normal or intrusive. It compares the strengths and limitations of different machine learning algorithms for IDS and identifies areas for potential improvement in future research.
2 14-1346479656-1- a study of feature selection methods in intrusion detectio...Dr. Amrita .
This document provides a survey of feature selection methods for intrusion detection systems. It discusses three categories of feature selection approaches: filter, wrapper, and hybrid. The KDD Cup 99 dataset is commonly used to evaluate feature selection methods, which contains 41 features divided into four categories. Performance is typically evaluated based on metrics like detection rate, false alarm rate, and accuracy rates derived from a confusion matrix. Feature selection aims to select a minimal subset of relevant features to improve detection performance while reducing computational requirements.
Implementation of Secured Network Based Intrusion Detection System Using SVM ...IRJET Journal
This document discusses the implementation of a secured network-based intrusion detection system using the support vector machine (SVM) algorithm. It begins with an abstract that outlines hardening different intrusion detection implementations and proposals. The paper then discusses using naive Bayes, a classification method for intrusion detection, to analyze transmitted data for malicious content and block transmissions from corrupted hosts. It also discusses using flow correlation information to improve classification accuracy while minimizing effects on network performance.
New Hybrid Intrusion Detection System Based On Data Mining Technique to Enhan...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
New Hybrid Intrusion Detection System Based On Data Mining Technique to Enhan...ijceronline
This document proposes a new hybrid intrusion detection system based on data mining techniques to improve performance over traditional IDS. The system combines hierarchical clustering, C5.0 classification, and CHAID classification. First, hierarchical clustering is used to split network data into normal and anomalous clusters. Then C5.0 classification labels the clusters, and any misclassified data is classified again with CHAID classification. Experimental results on the KDD 99 dataset show the proposed system has higher accuracy and efficiency than existing IDS approaches.
AN EFFICIENT DEEP LEARNING APPROACH FOR NETWORK INTRUSION DETECTION SYSTEM ON...IJNSA Journal
This document describes a proposed deep learning approach for network intrusion detection in software defined networks. The approach uses five deep learning classifiers (DNN, CNN, RNN, LSTM, GRU) trained on the NSL-KDD dataset. 12 features are extracted from the dataset using feature selection. The classifiers are evaluated based on various metrics and compared to related work to identify attacks efficiently for use in SDN environments. The CNN classifier achieved the highest results in most evaluation metrics. The approach provides a robust intrusion detection system for SDN through the use of multiple deep learning techniques.
IRJET- Review on Network Intrusion Detection using Recurrent Neural Network A...IRJET Journal
This document presents a review of using recurrent neural networks for network intrusion detection. It begins with an introduction to intrusion detection systems and the types of attacks they aim to detect. It then discusses previous research on machine learning approaches for intrusion detection, including the use of autoencoders, support vector machines, and other classifiers. The proposed approach uses a recurrent neural network for feature selection and classification of network data. The framework involves data collection, preprocessing including feature selection, training the recurrent neural network classifier, and then using the trained model to detect attacks in new data. Experimental results on benchmark intrusion detection datasets are presented and compared to other machine learning methods.
A SECURE SCHEMA FOR RECOMMENDATION SYSTEMSIJCI JOURNAL
Recommender systems have become an important tool for personalization of online services. Generating
recommendations in online services depends on privacy-sensitive data collected from the users. Traditional
data protection mechanisms focus on access control and secure transmission, which provide security only
against malicious third parties, but not the service provider. This creates a serious privacy risk for the
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significantly more efficient than previously known implementations of such systems.
A Survey on Various Data Mining Technique in Intrusion Detection SystemIOSRjournaljce
The intrusion detection plays an essential role in computer security. Data Mining refers to the process of extracting hidden, previously unknown and useful information from large databases. Thus data mining techniques help to detect patterns in the data set and use these patterns to detect future intrusions. Data Mining based Intrusion Detection System is combined with Multi-Agent System to improve the performance of the IDS. This paper concerned with the brief review of comparative study on applied data mining based intrusion detection techniques with their merit and demerits. This paper relay more number of applications of the data mining and also focuses extent of the data mining which will useful in the further research.
The main goal of Intrusion Detection Systems (IDSs) is
to detect intrusions. This kind of detection system represents a
significant tool in traditional computer based systems for ensuring
cyber security. IDS model can be faster and reach more accurate
detection rates, by selecting the most related features from the
input dataset. Feature selection is an important stage of any IDs to
select the optimal subset of features that enhance the process of the
training model to become faster and reduce the complexity while
preserving or enhancing the performance of the system. In this
paper, we proposed a method that based on dividing the input
dataset into different subsets according to each attack. Then we
performed a feature selection technique using information gain
filter for each subset. Then the optimal features set is generated by
combining the list of features sets that obtained for each attack.
Experimental results that conducted on NSL-KDD dataset shows
that the proposed method for feature selection with fewer features,
make an improvement to the system accuracy while decreasing the
complexity. Moreover, a comparative study is performed to the
efficiency of technique for feature selection using different
classification methods. To enhance the overall performance,
another stage is conducted using Random Forest and PART on
voting learning algorithm. The results indicate that the best
accuracy is achieved when using the product probability rule.
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1. Journal of Communications and Information Networks, Vol.2, No.4, Dec. 2017
DOI: 10.1007s41650-017-0033-7
c Posts & Telecom Press and Springer Singapore 2017
Review paper
Feature selection techniques for intrusion
detection using non-bio-inspired and
bio-inspired optimization algorithms
Veeran Ranganathan Balasaraswathi*, Muthukumarasamy Sugumaran, Yasir Hamid
Department of Computer Science Engineering, Pondicherry Engineering College, Pondicherry University,
Pondicherry 605014, India
* Corresponding author, Email: vrbala80@pec.edu
Abstract: As Internet access widens, IDS (Intrusion Detection System) is becoming a very important
component of network security to prevent unauthorized use and misuse of data. An IDS routinely handles
massive amounts of data traffic that contain redundant and irrelevant features, which impact the performance
of the IDS negatively. Feature selection methods play an important role in eliminating unrelated and
redundant features in IDS. Statistical analysis, neural networks, machine learning, data mining techniques,
and support vector machine models are employed in some such methods. Good feature selection leads to
better classification accuracy. Recently, bio-inspired optimization algorithms have been used for feature
selection. This work provides a survey of feature selection techniques for IDS, including bio-inspired algorithms.
Keywords: IDS, feature selection, optimization, classification, bio-inspired algorithm
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Citation: V. R. Balasaraswathi, M. Sugumaran, Y. Hamid. Feature selection techniques for intrusion de-
tection using non-bio-inspired and bio-inspired optimization algorithms [J]. Journal of communications and
information networks, 2017, 2(4): 107-119.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
1 Introduction
In the late 1970’s, the Internet came into existence
as a result of ARPANET. The rapid advancement
of the Internet has made it a potent platform for
communication, business, entertainment, research,
job researching, investing, and other uses. With
the increase in Internet connectivity, however, there
has been a corresponding increase in data breaches
and security threats. Internet-based security attacks
have multiplied in the last few years. Efforts to se-
cure networks from destructive users have resulted
in the development of protective software, including
firewalls (designed to prevent intrusions) and IDS
(to identify and operate against unauthorized data
use in the case of an intrusion). IDSs are designed
to test and analyze network traffic against a given
set of parameters[1]
to uncover potentially harmful
network transactions. An IDS is a software applica-
tion that examines all activities happening over the
network and identifies suspicious patterns that may
indicate that there has been a system or network at-
tack by someone attempting to bypass the security
mechanisms in place. An IDS can be classified in
many ways, based on a collection of data, analysis of
data, and actions needed to be taken. It can also be
Manuscript received Nov. 28, 2016; accepted Jun. 18, 2017
2. 108 Journal of Communications and Information Networks
classified, based on the position of installation in the
network, into two types: NIDS (Network-based IDS)
and HIDS (Host-based IDS)[2,3]
. An NIDS monitors
and evaluates the individual packets passing through
a network to detect malicious activities. An HIDS
inspects such activities as login attempts, process
scheduling, and system call tracing on an individ-
ual computer. Based on the detection methodol-
ogy, an IDS can be classified as one of two types,
namely misuse detection and anomaly detection[4]
.
Misuse detection is based on signatures, and is ef-
fective only in the detection of known attacks; it
cannot detect unknown attacks. Anomaly detection
is based on the behavior of an attacker when com-
pared with that of a normal user. Unknown attacks
can be detected by anomaly detection, but there will
be high false positive rates. An IDS operates in dif-
ferent phases such as data collection, preprocessing,
FS (Feature Selection), and classification. The FS
phase is a challenging one, given that the IDS must
deal with a huge amount of data. FS is the process
of selecting significant features; only the selected fea-
tures forming a subset of the total features are con-
sidered for classification. The major issue with an
IDS is the significant computation overhead[5]
. Ac-
curacy becomes a primary concern[6]
, because IDS
can identify a large variety of intrusions in real time.
The foremost problem in designing an IDS is rank-
ing and selecting the subset of highly discriminating
features[7]
. In recent years, FS methods and opti-
mization techniques have received considerable at-
tention for selecting the most significant features. A
detailed survey of IDS can be found in our previous
work[8]
. To classify the dataset, FS plays an impor-
tant role in selecting the most relevant and signifi-
cant features[9]
. Since classification is based on the
class label, removal of redundant and unnecessary
information is required[10]
. The objective of this pa-
per is to give an exhaustive survey of bio-inspired
and non-bio-inspired techniques used for FS of IDS
in the recent past. To the best of our knowledge, only
a few review papers in the field are available. What
differentiates this paper from existing papers is that
the techniques are segregated into various groups.
This survey helps researchers understand how bio-
inspired and non-bio-inspired techniques have been
effectively used in IDS.
The rest of the paper is organized as follows. Sec-
tion 2 is a brief introduction to FS. Section 3 dis-
cusses FS using bio-inspired algorithms. Section 4
provides a brief discussion about non-bio-inspired
techniques used for FS of IDS. Section 5 provides
the validation methods and performance metrics of
an IDS. Section 6 gives the conclusion.
2 Feature selection
In machine learning, FS is the process of selecting
a subset of original features based on certain crite-
ria. It is a significant and frequently used technique
in many fields for dimension reduction. The dataset
includes insignificant, redundant, and noisy features.
FS is important in improving the efficiency as well
as for reducing dimensions. This section will explain
FS, different FS techniques and their differences, and
the steps in the FS model.
2.1 Overview of FS
The FS problem involves identifying a subset of
features[11]
. An FS algorithm identifies and selects
the subset of features that are relevant to the task
to be learned. The classifier that is built with an
efficient subset of relevant features gives better pre-
dictive accuracy than a classifier built from the com-
plete set of features. Other advantages of FS in-
clude a reduction in the amount of training data
needed, a process that is simpler and easier to under-
stand, reduced computation time, and more accurate
classification. Many researchers[12,13]
have identified
that the presence of irrelevant features may have a
negative impact on the performance of learning sys-
tems. The predictive accuracy of a particular target
concept might not be affected by the irrelevant fea-
tures. Redundant features are those features that,
although relevant to a target concept, provide infor-
mation that is already provided by another feature
and therefore do not contribute toward prediction.
3. Feature selection techniques for intrusion detection using non-bio-inspired and bio-inspired optimization algorithms 109
Randomly class-correlated features are the features
that are highly correlated with the target class. Irrel-
evant and redundant features are worthless; hence,
removing them can improve the learning process. FS
is the process of identifying and removing the inap-
propriate, redundant, and randomly class-correlated
features[14]
.
2.2 FS techniques
The removal of irrelevant, redundant, and noisy fea-
tures speeds up the algorithm and reduces the error
rate. In general, there are three common methods
for FS: wrapper, filter, and hybrid.
2.2.1 Wrapper method
The wrapper method uses a learning algorithm for
evaluating the subset of features selected. The learn-
ing algorithm is used as a black box for the search[15]
.
The objective function, an equation with certain con-
straints to be optimized, is used to evaluate a subset
of features with the help of their predictive accu-
racy, which is named detection rate. This is a feed-
back method based on two components: search and
evaluation. The search component generates param-
eter settings that are then evaluated using the eval-
uation component[16]
. It uses a learning algorithm
to evaluate the usefulness of features and thus pro-
duces better feature subsets. The features are se-
lected based on the accuracy of the classifier. The
wrapper method is widely used, even though it is
slower than others.
Forward selection and backward elimination are
the two search strategies used in this method. The
forward selection strategy begins with a null set of
features and includes one feature at a time. For every
iteration, the feature that affects the major increase
of the evaluation function with respect to current set
is added[17]
. If the addition of new features does not
show any improvement of the evaluation function,
the search process is terminated. Backward elimi-
nation begins with the full feature set and removes
features one at a time. In each iteration, if removal of
any feature increases the evaluation function value,
that corresponding feature is removed from the set.
When a removal of feature results in a decrease of
the evaluation function, the search stops.
2.2.2 Filter method
As the name indicates, the filter method filters in-
significant features that have fewer options in the
analysis of data. It does not use any learning al-
gorithm to evaluate the features[18]
. The selected
features are evaluated based on the data character-
istics such as distance, consistency, correlation, and
information measures in the feature space. The fil-
ter method selects subsets with a greater number of
features, sometimes even all the features, so an ap-
propriate threshold is essential to choose a subset.
2.2.3 Hybrid method
The hybrid method is a combination of the wrap-
per and filter approaches to achieve better perfor-
mance. It implicitly or explicitly uses the FS algo-
rithm. Examples of this method are the decision tree
and the naive Bayes classifier, among others. Tab. 1
gives the advantages and disadvantages of the above-
mentioned FS methods.
2.3 Feature selection model
All original features are given as input to the FS
method, which generates a subset of features. The
selected subset is evaluated by either using a learning
algorithm or by considering the implicit characteris-
tics of the data. The steps for selecting the subset of
features are given below.
Steps: 1. Generate a subset of features from the
given set of data. 2. Evaluate the subset based on
criteria. 3. Verify whether the ensuing goals were
met. This may be an exact boundary of features
that leads to a better solution or satisfactory results
based on the error rate. 4. If the goals are met,
validate the result and terminate the process. Fig. 1
shows the steps of FS. The results can be compared
if prior output knowledge is available. Other tech-
niques, such as conducting experiments can be ap-
plied if prior knowledge is not available.
4. 110 Journal of Communications and Information Networks
Table 1 Comparison of FS techniques
model advantages disadvantages examples
filter fast, scalable, independent of classi-
fier, better computational complex-
ity
ignores interaction with classifier chisquare, Euclidean distance, in-
formation gain, correlation-based,
Markov blanket filter, etc.
wrapper simple, interacts with classifier,
models feature dependencies, good
classification accuracy, reduces com-
putational cost
computationally intensive sequential forward selection, sequen-
tial backward selection, hill climbing,
genetic algorithms, etc.
hybrid interacts with classifier, models with
feature dependencies, better compu-
tational complexity
classifier dependent selection decision tree, weighted Naive Bayes
subset generation
generates subset
of features
termination condition
validation
no
yes
original
feature
evaluation of generated subset
validation
Figure 1 Steps in FS
3 FS based on bio-inspired
algorithms
Given the fact that the volume of network traffic
data is increasing swiftly, the need for more efficient
FS methods has grown. The three above-mentioned
FS techniques use complex calculations, which makes
them relatively inefficient for large amounts of data.
Bio-inspired algorithms are those algorithms that are
inspired by nature. They solve complex problems us-
ing simple methods that exist in nature. Bio-inspired
algorithms are classified into three classes: evolution-
ary, swarm-based, and ecology-based[19]
.
3.1 Evolutionary algorithms
Evolutionary algorithms are based on Darwinian
principles of nature’s ability to evolve living beings
well adapted to their environment[20]
. Evolution-
ary algorithms can be GA (Genetic Algorithms), GP
(Genetic Programming), ES (Evolutionary Strate-
gies), or evolutionary programming. They use pro-
cedures motivated by biological evolution, such as
reproduction, mutation, recombination, and selec-
tion. A set of solutions plays the role of individu-
als in a population, and the fitness function eval-
uates the quality of the solutions. The evolution
of the population happens after the repeated ap-
plication of the mutation and crossover operators.
Evolutionary algorithms often generate approximate
solutions to all types of problems, and therefore,
they are successfully applied to diverse fields such
as engineering, biology, marketing, economics, art,
operations research, physics, chemistry, social sci-
ences, and genetics. Fig. 2 represents the steps to
generate the optimized subset using an evolutionary
algorithm.
3.1.1 Genetic programming
The GP proposed by Koza[21]
in 1992 is a systematic,
domain-independent method for computers to solve
problems automatically. GP is an extension of GA,
with the difference being that in GP, the individuals
in the population are computer programs. The pro-
grams are transformed into the populations for each
generation, and solutions to problems are generated
5. Feature selection techniques for intrusion detection using non-bio-inspired and bio-inspired optimization algorithms 111
initial population evaluation population fitness assignment
reproduction selection
Figure 2 Flow diagram of evolutionary algorithm
in the form of parameterized topologies by applying
crossover and mutation.
3.1.2 Genetic algorithm
The GA proposed by Holland in 1973[22]
is an evo-
lutionary algorithm with a global search. The GA is
the most successful class of evolutionary algorithms
inspired by the evolutionary facts of natural genetics.
These algorithms generate primary individuals from
a population; each individual represents a solution
for the problem. GA solves problems in an easier
manner since it is free of mathematical derivation.
It also produces optimal solutions, making it highly
suitable for FS. The genetic operators in GA are se-
lection, crossover, and mutation.
3.1.3 Evolution strategies
ES are global optimization algorithms inspired by
the theories of adaptation and evolution. They were
developed by Bienert et al. (1964) at the Techni-
cal University in Berlin in 1964[23]
. Specifically, this
technique is inspired by the macro-level or species-
level processes of evolution (phenotype, hereditary,
variation). The main feature of ES is the employ-
ment of self-adaptive mechanisms for controlling the
application of mutation.
3.2 Swarm-based algorithms
In an SBA (Swarm-Based Algorithm), the behavior
of the system is composed of many individual swarms
interacting with the environment and also locally
with each other[24]
. Self-organization and decen-
tralized control are used by swarms to achieve their
goals. Swarm-based systems are developed to resolve
complex problems. Swarms are uncomplicated crea-
tures with narrowly focused intellectual abilities and
limited mode of communication. Swarms show intel-
ligent behavior and provide significant solutions for
complex problems such as finding the shortest path,
the “knapsack problem”, and predator evasion.
3.2.1 Ant colony optimization
ACO (Ant Colony Optimization), introduced by
Dorigo in 1990[25]
is a nature-inspired metaheuris-
tic algorithm that obtains good solutions for rigid
combinatorial optimization problems with a reason-
able computation time. The ants find the shortest
routes due to their ability to deposit pheromones as
they move; other ants follow, moving in the direc-
tion where the chemical content is richest. Since the
pheromone decays over time, the less popular paths
end up with less of the pheromone. The traversal
rate by ants is more in the shortest path[26]
.
3.2.2 Particle swarm optimization
Kennedy and Eberhart (1995) proposed another bio-
inspired algorithm called PSO (Particle Swarm Op-
timization). In this model, a fitness function is used
that measures the quality of the current solution.
It uses the metaphor of the grouping behavior of
birds to solve optimization problems. In PSO, more
entities (particles) are stochastically created in the
search space. The particles flutter through the prob-
lem space by succeeding the current optimum parti-
cles. All particles have fitness values that are evalu-
ated by the fitness function to be optimized and have
velocities that direct the flying of the particles. A
swarm consists of particles flying around in a search
space. Every particle swarm has a certain kind of
topology describing the interconnections among the
particles. The key features of PSO are speed and
inherent ability to find the best solution globally.
6. 112 Journal of Communications and Information Networks
The speed of each particle is calculated using the
findings of both that particle and rest of the swarm.
The global best solution is conveyed among all the
particles of the swarm[27]
.
3.2.3 Bee colony algorithm
The BC (Bee Colony) algorithm is based on the be-
havior of the bees in nature and is classified as for-
aging behavior and mating behavior. Proposed by
Karaboga and Basturk (2007), BC simulates the in-
telligent foraging behavior of a honey bee. A BC
contains three groups: worker bees, onlookers, and
scouts. Onlooker bees wait in the dance region
to make a decision about selecting a food source.
Worker bees visit the food source. Scout bees per-
form a random search to determine new food sources.
The position of a food source corresponds to a po-
tential solution to the optimization problem, and the
nectar quantity of a food source matches the class
of the associated solution. A swarm of virtual bees
is produced and begins to move arbitrarily in two-
dimensional search space. Bees act together when
they locate some target nectar and the solution is
attained from the intensity of bee interactions[28]
.
3.2.4 Fish swarm algorithm
Li et al. (2002)[29]
proposed an innovative swarm
intelligent algorithm inspired by the natural school-
ing behavior of fish called FSA (Fish Swarm Algo-
rithm). FSA possesses a powerful ability to keep
away from local minimums to attain global opti-
mization. FSA replicates three distinctive behaviors:
searching, swarming, and following. Searching is a
random search for food, with an inclination toward
food concentration. Swarming tries to satisfy food
intake needs, engage swarm members, and attract
new swarm members. In the following, neighboring
individuals follow the fish that locates the food. The
parameters involved in FSA are the visual distance
(visual), maximum step length (step), and a crowd
factor. FSA effectiveness is primarily influenced by
the visual and step parameters.
3.2.5 Firefly algorithm
FA (Firefly Algorithm) is an unusual swarm-based
heuristic algorithm inspired by the blinking behav-
ior of fireflies[30]
. A population-based iterative algo-
rithm with numerous agents (perceived as fireflies),
FA concurrently solves the optimization problem.
Fireflies communicate with each other through bi-
oluminescent glowing that helps them discover cost-
function space more efficiently. This technique is
based on the theory that solution of an optimization
problem can be perceived as agents (fireflies) that
glow proportionally to their excellence in a measured
problem setting. Consequently, the brighter fireflies
attract more partners, which makes the search easier
and more efficient.
3.3 Ecology-based algorithms
Difficult engineering and computer science problems
can be solved by the mechanisms of natural ecosys-
tems. EBA (Ecology-Based Algorithms) are com-
posed of populations of individuals wherein each
population grows according to an optimization strat-
egy. Hence, individuals of each population are mod-
ified according to the mechanisms of intensification
and diversification, with initial parameters specific to
each optimization strategy. The ecology-based sys-
tem can be formed in two ways, namely homogeneous
and heterogeneous. In the homogeneous model, all
populations are evolved according to the same op-
timization strategy, configured with the same pa-
rameters. In the heterogeneous model, optimization
strategies or parameters can be changed. The eco-
logical inspiration stems from the use of some eco-
logical concepts, such as habitats, ecological relation-
ships, and ecological successions. Once detached in
the search space, populations of individuals recog-
nized in the same region form an ecological habitat.
A hypersurface may have numerous habitats. Pop-
ulations can move around through the environment.
However, each population may belong to only one
habitat at a given moment of time[31]
.
7. Feature selection techniques for intrusion detection using non-bio-inspired and bio-inspired optimization algorithms 113
Table 2 Evolutionary algorithms used for FS of IDS
S.No reference technique dataset feature
length classifier measures
1 [32] GA KDD CUP’99 32 decision tree –
2 [33] GA KDD CUP’99 27 multiobjective genetic
fuzzy rule classifier
AC: 99.24
3 [34] PCA + GA KDD CUP’99 22 SVM DR: 99.60
4 [35] PCA + GA KDD CUP’99 12 multilayer perception AC: 99.00
5 [36] neural network+ GA KDD CUP’99 16 decision tree DR: 98.38
6 [37] PCA + GA KDD CUP’99 10 SVM DR: 99.51
7 [38] hybrid kernel principal
component analysis+ GA
KDD CUP’99 12 multilayer SVM classifier DR: 94.22
8 [39] hybrid method of GA and
SVM
KDD CUP’99 10 SVM true positive value: 0.97,
false positive value: 0.01
3.4 Review of works applying bio-
inspired techniques
This section provides a review of bio-inspired tech-
niques used for FS of IDS. For each work, the FS
technique used, the dataset considered, the result-
ing reduced feature length, the base classifier em-
ployed, and the performance metrics taken have been
recorded.
3.4.1 Works applying evolutionary
algorithms
A comparison of evolutionary algorithms based FS
for IDS is given in Tab. 2.
3.4.2 Works applying swarm based
algorithms
ACO, PSO, the bee algorithm, and the cuttlefish al-
gorithm were used for FS of IDS. ACO is used more
widely than the other techniques. To improve the
performance, SBAs are combined with other algo-
rithm types to create hybrid forms. In terms of de-
tection rate, the ACO and cuttlefish algorithms pro-
duced a better result. The error rate is lower in CFA
and the bee algorithm. The works using SBA are
shown in Tab. 3.
4 Review of works applying non-bio-
inspired techniques
A comparison of nonbio-inspired FS techniques used
for IDS is given in Tab. 4.
As Tab. 4 suggests, different combinations can
provide different advantages. The information the-
ory concept combined with correlation-based FS al-
gorithm eliminates irrelevant features. Correlation
analysis calculates the strength of feature to class
and feature to feature. PCA is used to find the
significant features from the high-dimensional vec-
tors of input data. PCA with support vector ma-
chine improves accuracy and also generalization abil-
ity of the classifiers. LDA (Linear Discriminant
Analysis) is also used to select significant features.
Mutual information concept combined with binary
gravitational search algorithm will select relevant
features; this hybrid results in selecting the opti-
mal subset of features and also increases the clas-
sification performance. The rough-set-based FS ap-
proach, when used, also provides problem simplifica-
tion and faster and more accurate detection rates. A
combination of PCNN (Pulse-Coupled Neural Net-
work) algorithm and Gaussian support vector ma-
chine gives better generalization ability and also suc-
cessfully detects intrusion. Using the entropy-based
FS model with layered classifier improves the preci-
8. 114 Journal of Communications and Information Networks
Table 3 SBA used for FS of IDS
S.No reference technique dataset feature
length classifier measures
1 [40] PSO +support vector ma-
chine
KDD CUP’99 41 support vector machine DR: 99.84
2 [41] particle swarm optimiza-
tion
KDD CUP’99 12 support vector machine AC: 94.49
3 [42] bees algorithm KDD CUP’99 8 support vector machine DR: 95.75
4 [43] swarm based rough set KDD CUP’99 6 SSO with weighted local
search
AC: 93.60
5 [44] bee algorithm using mem-
brane computing
KDD CUP’99 41 support vector machine DR: 89.11AC: 95.6
6 [45] ant colony optimization +
feature weighting support
vector machine
KDD CUP’99 – support vector machine DR: 98.38
False Alarm Rate: 0.004
7 [46] cuttle fish algorithm NSL KDD – C5.0+one class SVM –AC: 98.20
False Alarm Rate: 1.70
8 [47] cuttle fish algorithm KDD CUP’99 10 decision tree DR: 92.05
9 [48] ant colony optimization KDD CUP’99 14 support vector machine TPR: 98.00
10 [49] ant colony optimization
NSL KDD and
KDD CUP’99
8 ACO AC: 98.90 FPR: 2.59
11 [50] bat algorithm KDD CUP’99 – support vector machine DR: 92.94
Detection time: 0.43 s
12 [51] artificial bee colony opti-
mization
KDD CUP’99 – feed-forward neural net-
work classifier
–
sion value. Many non-bio-inspired techniques such as
mutual information, rough-set theory, clustering ap-
proach, and effect-based, PCA, LDA, PCNN, SVM,
and correlation-based techniques have been applied
to NSL and KDD CUP’99 datasets. Among all
the techniques, correlation-based technique improves
the accuracy rate the most, whereas the entropy-
based model increases the detection rate and preci-
sion value. PCA reduces the computational time.
5 Validation and performance mea-
sures
This section describes validation methods and per-
formance metrics for an IDS.
5.1 Validation methods
The efficiency of the selected set of features has been
evaluated with the help of a test set that was not
known to the FS method. In a few cases, the data
were distributed into training and test sets. The
training set was used to carry out the FS process
and the test set was used to calculate the aptness
of the selection. All the datasets were not originally
partitioned; in many cases, to partition the data set,
several validation techniques were used. To check
whether the classifier produced the expected output,
the test set was validated against the training set.
Validation methods included k-fold cross-validation,
leave-one-outcross-validation, bootstrap validation,
and holdout validation[69]
. In k-fold cross-validation
methods, the dataset was split into k subsets, where
k−1 subsets were used for training and the remaining
partition was spared for testing. This process was re-
peated until all the sets were tested. Leave-one-out
was alternated with k-fold with k as the number of
samples. A single instance was left out every time. In
bootstrap, the number of samples was drawn equally
with replacements from the original data; thus, some
samples might appear multiple times, whereas others
might not have appeared at all. Holdout validation
randomly splits the available data, with two-thirds
for training and one-third for testing used as a com-
mon partition.
9. Feature selection techniques for intrusion detection using non-bio-inspired and bio-inspired optimization algorithms 115
Table 4 Non-bio-inspired FS techniques used for IDS
S.No Ref. technique dataset
feature
length classifier features results
1 [52]
principal component
analysis(hybrid
method)
KDD CUP’99 –
support vector
machine
improves generalization
ability of classifiers
training time: 33.3 s,
testing time: 14.4 s
2 [53]
information theory
concept+ correlation
based FS algorithm
KDD CUP’99 41
fuzzy belief
KNN
provides superior
performance
computation time: 0.25 s
3 [54]
linear discriminant
analysis
KDD CUP’99 – – detection rate is good FPR: 3.7
4 [55]
gradually feature
removal method
KDD CUP’99 19
support vector
machine
improves accuracy AC: 98.62
5 [56]
pulse-coupled neural
networks
KDD CUP’99 –
Gaussian
kernel of
support vector
machine
reduces feature subset,
increase accuracy
FPR: less than 1
6 [57]
Naive Bayes
classifier+Kruskal
Wallis statistical test
KDD CUP’99 13 C4.5
classifies significant and
relevant feature
AC: 99.95
7 [58]
intelligent agent-based
attribute selection
algorithm
KDD CUP’99 10
support vector
machine
1. minimizes
computation time
2. reduces false positive
rate
AC (DOS: 99.11
Probe: 96.16
U2R: 96.00
R2L: 96.00)
8 [59] filter based
KDD CUP’99,
NSL-KDD
dataset,
Kyoto 2006
25, 28,
15 SVM
improves
performance
AC: 99.94
9 [60]
intraclass and
interclass correlation
coefficient-based FS
algorithm
KDD CUP’99 – –
1. reduces false alarm
rate
2. reduces execution
time
–
10 [61]
correlation algorithm+
redundancy algorithm
KDD CUP’99 –
Naive Bayes
classifier
reduces time AC: 94
11 [62] attribute ratio NSL-KDD 22 decision tree improves accuracy AC: 99.79
12 [63] rough set based DD CUP’99 24 –
1. simplifies the problem
2. maximises accuracy
–
13 [64]
mutual information
concept+binary
gravitational search
algorithm
NSL-KDD 5
support vector
machine
1. selects optimal subset
of features
2. increases the classifi-
cation performance
AC: 88.36
14 [65] entropy based FS KDD CUP’99
DoS: 17,
probe: 21,
R2L: 14
layered
classifier
improves recall value
DR: 99.16 FPR: 0.74,
Recall Value: 99.03
15 [66]
enhanced correlation
based model
KDD CUP’99 12 –
1. speeds up detection
process
2. high performance rate
AC (Probe: 99.98 DOS:
99.3 R2L: 98.10 U2R:
72.20)
16 [67]
conditional random
field based
KDD CUP’99 41
layered
approach based
algorithm
reduces false alarm rate
AC (Probe: 98.83 DOS:
97.62 R2L: 32.43 U2R:
86.91)
17 [68] random forest KDD CUP’99 25 random forest performance is improved
precision (DoS: 98.94
Probe: 55.33 R2L-66.11
U2R: 17.14)
10. 116 Journal of Communications and Information Networks
Table 5 Performance measures
S.No metric definition formula
1 DR the ratio between a total number of instances detected by the
system and the total number of instances present in the dataset
-
2 TN percentage of negative samples classified correctly as negative -
3 FN percentage of positive samples incorrectly classified as negative -
4 TP percentage of positive samples classified correctly as positive -
5 FP percentage of negative samples incorrectly classified as positive -
6 sensitivity or TPR the proportion of positive samples correctly classified over a total
number of positive samples
TP/(TP+ FN).
7 specificity or TNR proportion of negative samples incorrectly classified as negative
over the total number of negative samples
TN/(TN+FP)
8 AC accuracy measures how an IDS works correctly. It is the propor-
tion of the total number of the correct predictions to the actual
dataset size
(TN+TP)/ (TN+TP+FN+FP)
9 misclassification rate percentage of input samples that are misclassified (FN+FP)/(TP+FP+FN+TN)
10 FPR the proportion of normal instances incorrectly classified as at-
tack over the total number of normal instances
FP/FP+TN
11 P the proportion of attack instances that were correctly predicted
relative to the predicted size of the attack class
TP/(TP+FP)
12 confusion matrix compares the actual class labels with predicted ones -
13 ROC ROC is a graph that illustrates the relation between TPR and
FPR
-.
5.2 Performance metrics
A good performance metric should have a quantita-
tive basis, allow accurate and detailed comparisons,
and lead to a correct conclusion. The metric set
that has been used to evaluate IDS are AC (Accu-
racy), DR (Detection Rate), TN (True Negative),
FN (False Negative), TPR (True Positive Rate),
FPR (False Positive Rate), sensitivity, specificity, er-
ror rate, recall, P (Precision), and ROC (Receiver
Operating Characteristics)[70]
. Tab. 5 gives the per-
formance measures used for IDS evaluation.
6 Conclusion
This paper provides an overview of different FS
methodologies and approaches for an IDS. Each tech-
nique was applied to the same data and measured
using the same metrics. Some techniques produced
better results for certain metrics. No single tech-
nique ranked as being the best in every metric or pro-
duced overall a better result. As this study suggests,
there is good reason for the trend in recent years to-
ward hybrid methods. The best results seem to be
derived from bio-inspired algorithms combined with
data mining techniques, soft computing techniques,
neural networks, rough set theory, and fuzzy logic.
The hybridization of bio-inspired algorithms with
other techniques improves performance and provides
effective problem solving.
References
[1] Heasman, John, S. Movle. Intrusion detection system
[M]. U.S. Patent Application No. 10/511, 775, 2003.
[2] D. Anderson, T. Frivold, A. Valdes. Next-generation
intrusion detection expert system(nides): a summary
[C]//SRI International Computer Science Laboratory
Menio Park, CA, 1995.
[3] R. Martin. Snort: lightweight intrusion detection for
networks [J]. LISA, 1999, 99 (1): 229-238.
[4] H. Debar, M. Dacier, A. Wespi. Towards taxonomy
of intrusion detection systems [J]. Computer networks
1999, 31(9), 805-822.
[5] H. Debar, M. Dacier, A. Wespi. A revised taxonomy for
intrusion-detection system [J]. Annals of telecommuni-
11. Feature selection techniques for intrusion detection using non-bio-inspired and bio-inspired optimization algorithms 117
cations, 2000, 55: 361-378.
[6] P. Garca-Teodoroa, J. Daz-Verdejoa, G. Maci-Fernndez,
et al. Anomaly-based network intrusion detection: tech-
niques, systems and challenges[J]. Computers and secu-
rity, 2009, 28(1): 18-28.
[7] T. F. Lunt. A survey of intrusion detection techniques
[J]. Computers and security, 1993, 12(4): 405-418.
[8] Y. Hamid, M. Sugumaran, V. R. Balasaraswathi. IDS
using machine learning-current state of art and future
directions [J]. British journal of applied science and tech-
nology, 2016, 15(3).
[9] S. Chebrolu, A. Abraham, J. P. Thomas. Feature deduc-
tion and ensemble design of intrusion detection systems
[J]. Computers and security, 2005, 24(4): 295-307.
[10] D. Manoranjan, H. Liu. Feature selection for classifica-
tion [J]. Intelligent data analysis, 1997, 1(3): 131-156.
[11] G. H. John, R. Kohavi, K. Pfleger. Irrelevant features
and the subset selection problem [C]//Proceedings of
ML-94, 1994: 121-129.
[12] J. Frank. Artificial intelligence and intrusion detection:
Current and future directions [C]//Proceedings of the
17th National Computer Security Conference, 1994, 10:
1-12.
[13] W. Lee, S. J. Stolfo. A framework for constructing
features and models for intrusion detection systems
[C]//ACM Transactions on Information and System Se-
curity, 2000, 3(4): 227-261.
[14] K. Kira, L. A. Rendell. The feature selection problem:
traditional methods and a new algorithm [M]. AAAI-
92.2, MIT Press, 1992: 129-134.
[15] B. Kumar, T. Swarnkar. Filter versus wrapper feature
subset selection in large dimensionality micro array: a
review [J]. International journal of computer science and
information technologies, 2011, 2(3): 1048-1053.
[16] V. Bol´on-Canedo, N. S´anchez-Maro˜no, A. Alonso-
Betanzos. A review of feature selection methods on
synthetic data [J]. Knowledge and information system,
2013, 34(3): 483-519.
[17] S Maldonado, R Weber, F Famili, et al. Feature se-
lection for high-dimensional class-imbalanced data sets
using support vector machines [J]. Information sciences,
2014, 286: 228-246.
[18] S. Binitha, S. S. Sathya. A survey of bio inspired op-
timization algorithms [J]. International journal of soft
computing and engineering, 2012, 2(2): 137-151.
[19] D. Karaboga, B. Akay. A comparative study of artifi-
cial bee colony algorithm [J]. Applied mathematics and
computation, 2009, 214(1): 108-132.
[20] T. Back. Evolutionary algorithms in theory and practice
[M]. Oxford: Oxford University Press, 1996.
[21] J. R. Koza. Genetic programming: on the programming
of computers by means of natural selection (Vol. 1) [M].
Cambridge: MIT press, 1992.
[22] J. H. Holland. Genetic algorithms and the optimal al-
location of trials [J]. SIAM journal on computing, 1973,
2(2): 88-105.
[23] H. G. Beyer, H. P. Sshwefel. Evolution strategies [J].
Natural computing, 2002: 3-52.
[24] E. Bonabeau, M. Dorigo, G. Theraulaz. Swarm intelli-
gence [M]. Oxford: Oxford University Press, 1999.
[25] M. Dorigo, C. Blum. Ant colony optimization theory: a
survey [J]. Theoretical computer science, 2005, 344(2):
243-278.
[26] M. Dorigo, V. Maniezzo, A. Colorn. Ant system: opti-
mization by a colony of cooperating agents [C]//IEEE
Transactions on Systems, Man, and Cybernetics, Part
B (Cybernetics), 1996, 26(1): 29-41.
[27] R. Poli, J. Kennedy, T. Blackwell. Particle swarm opti-
mization [J]. Swarm intelligence, 2007, 1(1): 33-57.
[28] D. Karaboga, B. Basturk. A powerful and efficient algo-
rithm for numerical function optimization: artificial bee
colony (ABC) algorithm [J]. Journal of global optimiza-
tion, 2007, 39(3): 459-471.
[29] X. L. Li, Z. J. Shao, J. X. Qian. An optimizing method
based on autonomous animats: fish-swarm algorithm [J].
System engineering theory and practice, 2002, 22(11):
32-38.
[30] X. S. Yang. Fire fly algorithm for multimodal opti-
mization [D]. Cambridge: International Symposium on
Stochastic Algorithms, 2009: 169-178.
[31] S. Goings, H. Goldsby, B. H. C. Cheng, et al. An
ecology-based evolutionary algorithm to evolve solutions
to complex problems [J]. Artificial life, 2012, 13: 171-
177.
[32] G. Stein, B. Chen, A. S. Wu, et al. Decision tree
classifier for network intrusion detection with GA-based
feature selection [C]//Proceedings of the 43rd Annual
Southeast Regional Conference. 2005, 2: 136-141.
[33] C. H. Tsang. Network-based anomaly intrusion detec-
tion using ant colony clustering model and genetic-fuzzy
rule mining approach [D]. Hong Kong: City University
of Hong Kong, 2006.
[34] I. Ahmad, A. B. Abdulla, A. S. Alghamdi, et al. Intru-
sion detection using feature subset selection based on
MLP [J]. Scientific research and essays, 2011, 24(7-8):
1671-1682.
[35] I. Ahmad, A. B. Abdulah, A. S. Alghamdi, et al. Feature
subset selection for network intrusion detection mecha-
nism [C]//Proceedings of CSIT, 2011, 5.
[36] S. S. S. Sindhu, S. Geetha, A. Kannan, et al. Deci-
sion tree based light weight intrusion detection using a
wrapper approach [J]. Expert systems with applications,
2012, 39(1): 129-141.
[37] I. Ahmad, M. Hussain, A. Alghamdi, et al. Enhancing
SVM performance in intrusion detection using optimal
feature subset selection based on genetic principal com-
ponents [J]. Neural computing and applications, 2014,
6(34): 6804-6810.
[38] F. J. Kuang, W. H. Xu, S. Zhang. A novel hybrid KPCA
and SVM with GA model for intrusion detection [J]. Ap-
12. 118 Journal of Communications and Information Networks
plied soft computing, 2014, 18, 178-184.
[39] B. M. Aslahi-Shahri, R. Rahmani, M. Chizari, et al.
A hybrid method consisting of GA and SVM for intru-
sion detection system [J]. Neural computing and appli-
cations, 2015, 1-8.
[40] J. Wang, X. Hong, R. R. Ren, et al. A real-
time intrusion detection system based on PSO-SVM
[C]//Proceedings of the International Workshop on In-
formation Security and Application, 2009: 319-321.
[41] T. Jiang, H. Gu. Anomaly detection combining one-
class SVMs and particle swarm optimization algorithms
[J]. Nonlinear dynamics, 2010, 61(1-2): 303-310.
[42] A. Osama, Z. A. Othman. Bees algorithm for feature
selection in network anomaly detection [J]. Journal of
applied sciences research, 2012, 8(3): 1748-1756.
[43] C. Chung, Y. Ying, N. Wahid. A hybrid network in-
trusion detection system using simplified swarm opti-
mization (SSO) [J]. Applied soft computing, 2012, 12(9):
3014-3022.
[44] K. I. Rufai, R. C. Muniyandi, Z. A. Othman. Improving
bee algorithm based feature selection in intrusion detec-
tion system using membrane computing [J]. Journal of
networks, 2014, 9(3): 523-529.
[45] X. Z. Wang. ACO and SVM selection feature weighting
of network [J]. International Journal of Security and Its
applications, 2015, 9(4): 129-270.
[46] M. S. Rani, S. B. Xavier. A hybrid intrusion detec-
tion system based on C5.0 decision tree and one-Class
SVM [J]. International journal of current engineering
and technology, 2015, 5(3): 2001-2007.
[47] A. S. Eesa, Z. Orman, A. M. A. Brifcani. A novel
feature-selection approach based on the cuttlefish op-
timization algorithm for intrusion detection systems [J].
Expert systems with applications, 2015, 42(5): 2670-
2679.
[48] T. Mehmod, H. B. M. Rais. Ant colony optimization
and feature selection for intrusion detection [J]. Ad-
vances in machine learning and signal processing, 2016,
305-312.
[49] M. H. Aghdam, P. Kabiri. Feature selection for intru-
sion detection system using ant colony optimisation [J].
International journal of network security, 2016, 18(3):
420-432.
[50] C. Y. Cheng, L. Y. Bao, C. H. Bao. Network
intrusion detection with bat algorithm for synchro-
nization of feature selection and support vector ma-
chines [C]//International Symposium on Neural Net-
works. Springer International Publishing Switzerland,
2016: 401-408.
[51] W. A. H. M. Ghanem, A. Jantan. Novel multi-objective
artificial bee colony optimization for wrapper based fea-
ture selection in intrusion detection [J]. International
journal of advance soft computing applications, 2016,
8(1).
[52] X. Xu, X. N. Wang. An adaptive network intrusion
detection method based on pca and support vector ma-
chines [C]//ADMA, 2005: 696-703.
[53] T. S. Chou, K. K. Yen, J. Luo. Network intrusion de-
tection design using feature selection of soft computing
paradigms [J]. International journal of computational in-
telligence, 2008, 4(3): 196-208.
[54] Z. Y. Tan, A. Jamdagni, X. He. et al. Network intru-
sion detection based on LDA for payload feature selec-
tion [C]//2010 IEEE Globecom Workshops, 2010: 1545-
1549.
[55] Y. H. Li, J. B. Xia, S. L. Zhang, et al. An efficient
intrusion detection system based on support vector ma-
chines and gradually feature removal method [J]. Expert
systems with applications, 2012, 39(1): 424-430.
[56] A. Shrivastava, M. Baghel, H. Gupta. A novel hybrid
feature selection and intrusion detection based on pcnn
and support vector machine [J]. International journal of
computer technology and applications, 2013, 4 (6), 922.
[57] P. Louvieris, N. Clewley, X. Liu. Effects-based feature
identification for network intrusion detection [J]. Neuro-
computing, 2013, 121, 265-273.
[58] S. Balakrishnan, K. Venkatalakshmi, A. Kannan. A
intrusion detection system using feature selection and
classification technique [J]. International journal of com-
puter science and application (IJCSA), 2016, 3(4).
[59] M. Ambusaidi, X. J. He, P. Nanda, et al. Building an
intrusion detection system using a filter-based feature
selection algorithm [C]//IEEE transactions on comput-
ers, 2014, 65(10): 2986-2998.
[60] A. R. Vasudevan, S. Selvakumar. Intraclass and in-
terclass correlation coefficient-based feature selection in
NIDS dataset [J]. Security and communication networks,
2015, 8(18): 3441-3458.
[61] C. Y. Yin, L. Y. Ma, L. Feng, et al. A feature selection
algorithm towards efficient intrusion [J]. International
journal of multimedia and ubiquitous engineering, 2015,
10(11): 253-264.
[62] H. S. Chae, B. O. Jo, S. H. Choi, et al. Feature selec-
tion for intrusion detection using NSL-KDD [J]. Recent
advances in computer science, 2015, 960-978.
[63] V. Rampure, A. Tiwari. A rough set based feature selec-
tion on KDD CUP 99 data set [J]. International journal
of database theory and application, 2015, 8(1): 149-156.
[64] H. Bostani, M. Sheikhan. Hybrid of binary gravitational
search algorithm and mutual information for feature se-
lection in intrusion detection systems [J]. Soft comput-
ing, 2015, 1-18.
[65] S. Ramakrishnan, S. Devaraju. Attack’s feature
selection-based network intrusion detection using fuzzy
control language [J]. International journal of fuzzy sys-
tems, 2016, 1-13.
[66] A. I. Madbouly, T. M. Barakat. Enhanced relevant fea-
ture selection model for intrusion detection systems [J].
International journal intelligent engineering informatics,
2016, 4(1): 21-45.
13. Feature selection techniques for intrusion detection using non-bio-inspired and bio-inspired optimization algorithms 119
[67] S. Ganapathy, P. Vijayakumar, et al. An intelligent
CRF based feature selection for effective intrusion de-
tection [J]. The international arab journal of information
technology, 2016, 16(2).
[68] S. Rastegari, P. Hingston, C. P. Lam. Evolving statisti-
cal rulesets for network intrusion detection [J]. Applied
soft computing, 2015, 33: 348-359.
[69] M. H. Bhuyan, D. K. Bhattacharyya, J. K. Kalita. Net-
work anomaly detection: methods, systems and tools
[J]. IEEE communications surveys and tutorials, 2014,
16(1): 303-336.
[70] C. H. Tsang, S. Kwong. Multi-agent intrusion de-
tection system in industrial network using ant colony
clustering approach and unsupervised feature extraction
[C]//IEEE International Conference on Industrial Tech-
nology, 2005: 51-56.
About the authors
Veeran Ranganathan Bal-
asaraswathi [corresponding author] re-
ceived her B.E. degree in computer sci-
ence and engineering from Madras Uni-
versity in 2002 and M.E. degree in com-
puter science and engineering in 2008
from Anna University, Chennai. She is
now a Ph.D. candidate of Pondicherry Engineering College,
India. Her research interests include information security,
data mining, data structures and algorithms. (Email: vr-
bala80@pec.edu)
Muthukumarasamy Sugumaran re-
ceived his M.Sc. degree in mathematics
from University of Madras in 1986 and
M.Tech. degree in computer science and
data processing from Indian Institute of
Technology, Karagpur in 1991 and ob-
tained his Ph.D. degree from Anna Uni-
versity, Chennai in 2008. He is currently working as a profes-
sor and head of computer science at Pondicherry Engineering
College, India. His areas of interests are theoretical computer
science, analysis of algorithms, parallel and distributed com-
puting and spatial temporal data. (Email: sugu@pec.edu)
Yasir Hamid received his Master’s de-
gree in computer applications from Uni-
versity of Kashmir in 2014. He is now
a Ph.D. candidate of Pondicherry Engi-
neering College, India. His research in-
terests include machine learning, network
security, non linear dimension reduction
and data visualization. (Email: bhatyasirhamid@pec.edu)