In network security framework, intrusion detection is one of a benchmark part and is a fundamental way to protect PC from many threads. The huge issue in intrusion detection is presented as a huge number of false alerts; this issue motivates several experts to discover the solution for minifying false alerts according to data mining that is a consideration as analysis procedure utilized in a large data e.g. KDD CUP 99. This paper presented various data mining classification for handling false alerts in intrusion detection as reviewed. According to the result of testing many procedure of data mining on KDD CUP 99 that is no individual procedure can reveal all attack class, with high accuracy and without false alerts. The best accuracy in Multilayer Perceptron is 92%; however, the best Training Time in Rule based model is 4 seconds . It is concluded that ,various procedures should be utilized to handle several of network attacks.
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.
An intrusion detection system for packet and flow based networks using deep n...IJECEIAES
Study on deep neural networks and big data is merging now by several aspects to enhance the capabilities of intrusion detection system (IDS). Many IDS models has been introduced to provide security over big data. This study focuses on the intrusion detection in computer networks using big datasets. The advent of big data has agitated the comprehensive assistance in cyber security by forwarding a brunch of affluent algorithms to classify and analysis patterns and making a better prediction more efficiently. In this study, to detect intrusion a detection model has been propounded applying deep neural networks. We applied the suggested model on the latest dataset available at online, formatted with packet based, flow based data and some additional metadata. The dataset is labeled and imbalanced with 79 attributes and some classes having much less training samples compared to other classes. The proposed model is build using Keras and Google Tensorflow deep learning environment. Experimental result shows that intrusions are detected with the accuracy over 99% for both binary and multiclass classification with selected best features. Receiver operating characteristics (ROC) and precision-recall curve average score is also 1. The outcome implies that Deep Neural Networks offers a novel research model with great accuracy for intrusion detection model, better than some models presented in the literature.
A SURVEY ON THE USE OF DATA CLUSTERING FOR INTRUSION DETECTION SYSTEM IN CYBE...IJNSA Journal
In the present world, it is difficult to realize any computing application working on a standalone computing device without connecting it to the network. A large amount of data is transferred over the network from one device to another. As networking is expanding, security is becoming a major concern. Therefore, it has become important to maintain a high level of security to ensure that a safe and secure connection is established among the devices. An intrusion detection system (IDS) is therefore used to differentiate between the legitimate and illegitimate activities on the system. There are different techniques are used for detecting intrusions in the intrusion detection system. This paper presents the different clustering techniques that have been implemented by different researchers in their relevant articles. This survey was carried out on 30 papers and it presents what different datasets were used by different researchers and what evaluation metrics were used to evaluate the performance of IDS. This paper also highlights the pros and cons of each clustering technique used for IDS, which can be used as a basis for future work.
Current issues - International Journal of Network Security & Its Applications...IJNSA Journal
nternational Journal of Network Security & Its Applications (IJNSA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer Network Security & its applications. The journal focuses on all technical and practical aspects of security and its applications for wired and wireless networks. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern security threats and countermeasures, and establishing new collaborations in these areas.
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.
HYBRID ARCHITECTURE FOR DISTRIBUTED INTRUSION DETECTION SYSTEM IN WIRELESS NE...IJNSA Journal
This document proposes a hybrid architecture for a distributed intrusion detection system using multiple agents. The key aspects of the architecture include:
- Using multiple independent tracker agents that monitor hosts and generate reports sent to monitors and storage.
- Monitors analyze activity and compare to signatures to detect known attacks, or send data to anomaly detectors.
- Anomaly and misuse detectors use classification and pattern matching to detect known and unknown attacks.
- An inference module coordinates entities across hosts to classify new attacks using a knowledge base and signature generator.
- A countermeasure module alerts administrators and can take actions like dropping packets in response to detected attacks.
The document discusses using machine learning algorithms like Random Forest and k-Nearest Neighbors for intrusion detection. It analyzes the KDD Cup 1999 intrusion detection dataset to classify network traffic as normal or different types of attacks. The proposed model uses Random Forest for feature selection and k-Nearest Neighbors for classification to more accurately detect known and unknown attacks. Experimental results show the combined approach achieves better detection rates than other algorithms alone, especially for novel attacks not present in training data. Further combining the algorithms into a two-stage process may yield even higher accuracy.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
The document proposes a signature-based intrusion detection system using multithreading. It captures network packets and analyzes them for intrusions by comparing signatures to databases of known attacks. A multithreaded design is suggested to improve performance by processing packets in parallel threads. Agents would be deployed on the network with detection modules that use caching of frequent signatures to speed up analysis. An update module would transfer new frequent signatures to the caches.
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.
An intrusion detection system for packet and flow based networks using deep n...IJECEIAES
Study on deep neural networks and big data is merging now by several aspects to enhance the capabilities of intrusion detection system (IDS). Many IDS models has been introduced to provide security over big data. This study focuses on the intrusion detection in computer networks using big datasets. The advent of big data has agitated the comprehensive assistance in cyber security by forwarding a brunch of affluent algorithms to classify and analysis patterns and making a better prediction more efficiently. In this study, to detect intrusion a detection model has been propounded applying deep neural networks. We applied the suggested model on the latest dataset available at online, formatted with packet based, flow based data and some additional metadata. The dataset is labeled and imbalanced with 79 attributes and some classes having much less training samples compared to other classes. The proposed model is build using Keras and Google Tensorflow deep learning environment. Experimental result shows that intrusions are detected with the accuracy over 99% for both binary and multiclass classification with selected best features. Receiver operating characteristics (ROC) and precision-recall curve average score is also 1. The outcome implies that Deep Neural Networks offers a novel research model with great accuracy for intrusion detection model, better than some models presented in the literature.
A SURVEY ON THE USE OF DATA CLUSTERING FOR INTRUSION DETECTION SYSTEM IN CYBE...IJNSA Journal
In the present world, it is difficult to realize any computing application working on a standalone computing device without connecting it to the network. A large amount of data is transferred over the network from one device to another. As networking is expanding, security is becoming a major concern. Therefore, it has become important to maintain a high level of security to ensure that a safe and secure connection is established among the devices. An intrusion detection system (IDS) is therefore used to differentiate between the legitimate and illegitimate activities on the system. There are different techniques are used for detecting intrusions in the intrusion detection system. This paper presents the different clustering techniques that have been implemented by different researchers in their relevant articles. This survey was carried out on 30 papers and it presents what different datasets were used by different researchers and what evaluation metrics were used to evaluate the performance of IDS. This paper also highlights the pros and cons of each clustering technique used for IDS, which can be used as a basis for future work.
Current issues - International Journal of Network Security & Its Applications...IJNSA Journal
nternational Journal of Network Security & Its Applications (IJNSA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer Network Security & its applications. The journal focuses on all technical and practical aspects of security and its applications for wired and wireless networks. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern security threats and countermeasures, and establishing new collaborations in these areas.
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.
HYBRID ARCHITECTURE FOR DISTRIBUTED INTRUSION DETECTION SYSTEM IN WIRELESS NE...IJNSA Journal
This document proposes a hybrid architecture for a distributed intrusion detection system using multiple agents. The key aspects of the architecture include:
- Using multiple independent tracker agents that monitor hosts and generate reports sent to monitors and storage.
- Monitors analyze activity and compare to signatures to detect known attacks, or send data to anomaly detectors.
- Anomaly and misuse detectors use classification and pattern matching to detect known and unknown attacks.
- An inference module coordinates entities across hosts to classify new attacks using a knowledge base and signature generator.
- A countermeasure module alerts administrators and can take actions like dropping packets in response to detected attacks.
The document discusses using machine learning algorithms like Random Forest and k-Nearest Neighbors for intrusion detection. It analyzes the KDD Cup 1999 intrusion detection dataset to classify network traffic as normal or different types of attacks. The proposed model uses Random Forest for feature selection and k-Nearest Neighbors for classification to more accurately detect known and unknown attacks. Experimental results show the combined approach achieves better detection rates than other algorithms alone, especially for novel attacks not present in training data. Further combining the algorithms into a two-stage process may yield even higher accuracy.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
The document proposes a signature-based intrusion detection system using multithreading. It captures network packets and analyzes them for intrusions by comparing signatures to databases of known attacks. A multithreaded design is suggested to improve performance by processing packets in parallel threads. Agents would be deployed on the network with detection modules that use caching of frequent signatures to speed up analysis. An update module would transfer new frequent signatures to the caches.
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.
Preemptive modelling towards classifying vulnerability of DDoS attack in SDN ...IJECEIAES
Software-Defined Networking (SDN) has become an essential networking concept towards escalating the networking capabilities that are highly demanded future internet system, which is immensely distributed in nature. Owing to the novel concept in the field of network, it is still shrouded with security problems. It is also found that the Distributed Denial-of-Service (DDoS) attack is one of the prominent problems in the SDN environment. After reviewing existing research solutions towards resisting DDoS attack in SDN, it is found that still there are many open-end issues. Therefore, these issues are identified and are addressed in this paper in the form of a preemptive model of security. Different from existing approaches, this model is capable of identifying any malicious activity that leads to a DDoS attack by performing a correct classification of attack strategy using a machine learning approach. The paper also discusses the applicability of best classifiers using machine learning that is effective against DDoS attack.
An Investigation into the Effectiveness of Machine Learning Techniques for In...Oyeniyi Samuel
The document investigates the effectiveness of machine learning techniques for intrusion detection. It evaluates six machine learning algorithms (Naive Bayes, Multi-Layer Perceptron Neural Networks, Support Vector Machine, Random Forests, Logistic Model Tree Induction, and Decision Tree) on the NSL-KDDTrain+ dataset. The experimental results show that the Logistic Model Tree Induction method performs best with a classification accuracy of 99.40%, F-measure of 0.991, and lowest false positive rate of 0.32%.
Machine learning in network security using knime analyticsIJNSA Journal
Machine learning has more and more effect on our every day’s life. This field keeps growing and expanding into new areas. Machine learning is based on the implementation of artificial intelligence that gives systems the capability to automatically learn and enhance from experiments without being explicitly
programmed. Machine Learning algorithms apply mathematical equations to analyze datasets and predict values based on the dataset. In the field of cybersecurity, machine learning algorithms can be utilized to train and analyze the Intrusion Detection Systems (IDSs) on security-related datasets. In this paper, we tested different machine learning algorithms to analyze NSL-KDD dataset using KNIME analytics.
MACHINE LEARNING IN NETWORK SECURITY USING KNIME ANALYTICSIJNSA Journal
Machine learning has more and more effect on our every day’s life. This field keeps growing and expanding into new areas. Machine learning is based on the implementation of artificial intelligence that gives systems the capability to automatically learn and enhance from experiments without being explicitly programmed. Machine Learning algorithms apply mathematical equations to analyze datasets and predict values based on the dataset. In the field of cybersecurity, machine learning algorithms can be utilized to train and analyze the Intrusion Detection Systems (IDSs) on security-related datasets. In this paper, we tested different machine learning algorithms to analyze NSL-KDD dataset using KNIME analytics.
IRJET- An Intrusion Detection and Protection System by using Data Mining ...IRJET Journal
This document proposes an Internal Intrusion Detection and Protection System (IIDPS) to detect insider attacks by analyzing system calls (SCs) using data mining and forensic techniques. The IIDPS creates personal profiles for each user to track their computer usage behaviors over time. When a user logs in, the IIDPS compares their current behaviors to the patterns in their personal profile to determine if they are the legitimate account holder or an unauthorized insider attacker. The IIDPS aims to more accurately authenticate users and detect insider threats compared to existing systems that rely only on usernames and passwords.
Survey of Clustering Based Detection using IDS Technique IRJET Journal
This document discusses intrusion detection systems (IDS) and different techniques used for IDS, including clustering-based detection. It first provides background on IDS, describing their purpose of detecting intruders and protecting systems. It then outlines various IDS types, including mobile agent-based, cluster-based, cryptography-based, and others. The document also summarizes related work from other papers applying data mining techniques like clustering to improve IDS detection rates and reduce false alarms. Finally, it discusses problems with current and traditional IDS, such as threshold detection leading to false positives, and false negatives where attacks are missed.
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 %.
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.
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.
This document summarizes a research paper on adaptive personalized web search with safety seclusion. It discusses how personalized web search has improved search quality but user privacy concerns have limited its adoption. The paper proposes a system called UPS that can dynamically generalize user profiles during searches while respecting indicated privacy requirements. UPS uses greedy algorithms to balance personalization utility and privacy risk from exposing generalized profiles. The system aims to address limitations in existing personalized search regarding user security and accuracy needs.
- Wireless sensor networks are vulnerable to security attacks due to their distributed nature, multi-hop communication, and lack of resources. Intrusion detection systems play an important role in detecting attacks.
- There are three main types of intrusion detection systems: signature-based, anomaly-based, and specification-based (a hybrid of the two). Signature-based systems detect known attacks but miss new ones, while anomaly-based systems can detect new attacks but have high false positives.
- The paper compares these intrusion detection systems for wireless sensor networks and finds that anomaly-based systems have the lowest resource usage but may miss known attacks, while signature-based systems detect known attacks but use more resources. The best approach
LSTM deep learning method for network intrusion detection system IJECEIAES
The security of the network has become a primary concern for organizations. Attackers use different means to disrupt services, these various attacks push to think of a new way to block them all in one manner. In addition, these intrusions can change and penetrate the devices of security. To solve these issues, we suggest, in this paper, a new idea for Network Intrusion Detection System (NIDS) based on Long Short-Term Memory (LSTM) to recognize menaces and to obtain a long-term memory on them, in order to stop the new attacks that are like the existing ones, and at the same time, to have a single mean to block intrusions. According to the results of the experiments of detections that we have realized, the Accuracy reaches up to 99.98 % and 99.93 % for respectively the classification of two classes and several classes, also the False Positive Rate (FPR) reaches up to only 0,068 % and 0,023 % for respectively the classification of two classes and several classes, which proves that the proposed model is effective, it has a great ability to memorize and differentiate between normal traffic and attacks, and its identification is more accurate than other Machine Learning classifiers.
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.
Constructing a predictive model for an intelligent network intrusion detectionAlebachew Chiche
This document presents a study that constructs a predictive model for network intrusion detection using data mining techniques. The study uses the KDD Cup 99 intrusion detection dataset to build classification models using J48 decision tree, JRip rule induction, Naive Bayes, and multilayer perceptron algorithms. The J48 decision tree algorithm achieved the highest accuracy of 99.91% and was selected to build the predictive model. This model was then integrated with a knowledge-based system to build an intelligent network intrusion detection system capable of automatically detecting network attacks, mapping detections to attack categories, and updating the training data over time. Experimental evaluation found the integrated system achieved 91.43% accuracy and 83% user acceptance in detecting network intrusions
Machine learning-based intrusion detection system for detecting web attacksIAESIJAI
The increasing use of smart devices results in a huge amount of data, which raises concerns about personal data, including health data and financial data. This data circulates on the network and can encounter network traffic at any time. This traffic can either be normal traffic or an intrusion created by hackers with the aim of injecting abnormal traffic into the network. Firewalls and traditional intrusion detection systems detect attacks based on signature patterns. However, this is not sufficient to detect advanced or unknown attacks. To detect different types of unknown attacks, the use of intelligent techniques is essential. In this paper, we analyse some machine learning techniques proposed in recent years. In this study, several classifications were made to detect anomalous behaviour in network traffic. The models were built and evaluated based on the Canadian Institute for Cybersecurity-intrusion detection systems dataset released in 2017 (CIC-IDS-2017), which includes both current and historical attacks. The experiments were conducted using decision tree, random forest, logistic regression, gaussian naïve bayes, adaptive boosting, and their ensemble approach. The models were evaluated using various evaluation metrics such as accuracy, precision, recall, F1-score, false positive rate, receiver operating characteristic curve, and calibration curve.
Three level intrusion detection system based on conditional generative advers...IJECEIAES
Security threat protection is important in the internet of things (IoT) applications since both the connected device and the captured data can be hacked or hijacked or both at the same time. To tackle the above-mentioned problem, we proposed three-level intrusion detection system conditional generative adversarial network (3LIDS-CGAN) model which includes four phases such as first-level intrusion detection system (IDS), second-level IDS, third-level IDS, and attack type classification. In first-level IDS, features of the incoming packets are extracted by the firewall. Based on the extracted features the packets are classified into three classes such as normal, malicious, and suspicious using support vector machine and golden eagle optimization. Suspicious packets are forwarded to the second-level IDS which classified the suspicious packets as normal or malicious. Here, signature-based intrusions are detected using attack history information, and anomaly-based intrusions are detected using event-based semantic mapping. In third-level IDS, adversary packets are detected using CGAN which automatically learns the adversarial environment and detects adversary packets accurately. Finally, proximal policy optimization is proposed to detect the attack type. Experiments are conducted using the NS-3.26 network simulator and performance is evaluated by various performance metrics which results that the proposed 3LIDS-CGAN model outperforming other existing works.
HYBRID ARCHITECTURE FOR DISTRIBUTED INTRUSION DETECTION SYSTEM IN WIRELESS NE...IJNSA Journal
In order to the rapid growth of the network application, new kinds of network attacks are emerging endlessly. So it is critical to protect the networks from attackers and the Intrusion detection technology becomes popular. Therefore, it is necessary that this security concern must be articulate right from the beginning of the network design and deployment. The intrusion detection technology is the process of identifying network activity that can lead to a compromise of security policy. Lot of work has been done in detection of intruders. But the solutions are not satisfactory. In this paper, we propose a novel Distributed Intrusion Detection System using Multi Agent In order to decrease false alarms and manage misuse and anomaly detects.
Detecting network attacks model based on a convolutional neural network IJECEIAES
Due to the increasing use of networks at present, Internet systems have raised many security problems, and statistics indicate that the rate of attacks or intrusions has increased excessively annually, and in the event of any malicious attack on network vulnerabilities or information systems, it may lead to serious disasters, violating policies on network security, i.e., “confidentiality, integrity, and availability” (CIA). Therefore, many detection systems, such as the intrusion detection system, appeared. In this paper, we built a system that detects network attacks using the latest machine learning algorithms and a convolutional neural network based on a dataset of the CSE-CIC-IDS2018. It is a recent dataset that contains a set of common and recent attacks. The detection rate is 99.7%, distinguishing between aggressive attacks and natural assertiveness.
FORTIFICATION OF HYBRID INTRUSION DETECTION SYSTEM USING VARIANTS OF NEURAL ...IJNSA Journal
Intrusion Detection Systems (IDS) form a key part of system defence, where it identifies abnormal
activities happening in a computer system. In recent years different soft computing based techniques have
been proposed for the development of IDS. On the other hand, intrusion detection is not yet a perfect
technology. This has provided an opportunity for data mining to make quite a lot of important
contributions in the field of intrusion detection. In this paper we have proposed a new hybrid technique
by utilizing data mining techniques such as fuzzy C means clustering, Fuzzy neural network / Neurofuzzy and radial basis function(RBF) SVM for fortification of the intrusion detection system. The
proposed technique has five major steps in which, first step is to perform the relevance analysis, and then
input data is clustered using Fuzzy C-means clustering. After that, neuro-fuzzy is trained, such that each
of the data point is trained with the corresponding neuro-fuzzy classifier associated with the cluster.
Subsequently, a vector for SVM classification is formed and in the last step, classification using RBF-
SVM is performed to detect intrusion has happened or not. Data set used is the KDD cup 1999 dataset
and we have used precision, recall, F-measure and accuracy as the evaluation metrics parameters. Our
technique could achieve better accuracy for all types of intrusions. The results of proposed technique are
compared with the other existing techniques. These comparisons proved the effectiveness of our
technique.
ATTACK DETECTION AVAILING FEATURE DISCRETION USING RANDOM FOREST CLASSIFIERCSEIJJournal
This document discusses using a random forest classifier with feature selection to improve intrusion detection. It begins with background on intrusion detection systems and challenges. It then proposes using genetic algorithms for feature selection to identify the most important features from a dataset. A random forest classifier is used for classification, which combines decision trees to improve accuracy. The methodology involves feature selection, classification with random forest, and detection. Feature weights are calculated and cross-validation is used to analyze detection rates for individual attacks. The goal is to improve accuracy, reduce training time, and better detect minority attacks through this approach.
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.
Preemptive modelling towards classifying vulnerability of DDoS attack in SDN ...IJECEIAES
Software-Defined Networking (SDN) has become an essential networking concept towards escalating the networking capabilities that are highly demanded future internet system, which is immensely distributed in nature. Owing to the novel concept in the field of network, it is still shrouded with security problems. It is also found that the Distributed Denial-of-Service (DDoS) attack is one of the prominent problems in the SDN environment. After reviewing existing research solutions towards resisting DDoS attack in SDN, it is found that still there are many open-end issues. Therefore, these issues are identified and are addressed in this paper in the form of a preemptive model of security. Different from existing approaches, this model is capable of identifying any malicious activity that leads to a DDoS attack by performing a correct classification of attack strategy using a machine learning approach. The paper also discusses the applicability of best classifiers using machine learning that is effective against DDoS attack.
An Investigation into the Effectiveness of Machine Learning Techniques for In...Oyeniyi Samuel
The document investigates the effectiveness of machine learning techniques for intrusion detection. It evaluates six machine learning algorithms (Naive Bayes, Multi-Layer Perceptron Neural Networks, Support Vector Machine, Random Forests, Logistic Model Tree Induction, and Decision Tree) on the NSL-KDDTrain+ dataset. The experimental results show that the Logistic Model Tree Induction method performs best with a classification accuracy of 99.40%, F-measure of 0.991, and lowest false positive rate of 0.32%.
Machine learning in network security using knime analyticsIJNSA Journal
Machine learning has more and more effect on our every day’s life. This field keeps growing and expanding into new areas. Machine learning is based on the implementation of artificial intelligence that gives systems the capability to automatically learn and enhance from experiments without being explicitly
programmed. Machine Learning algorithms apply mathematical equations to analyze datasets and predict values based on the dataset. In the field of cybersecurity, machine learning algorithms can be utilized to train and analyze the Intrusion Detection Systems (IDSs) on security-related datasets. In this paper, we tested different machine learning algorithms to analyze NSL-KDD dataset using KNIME analytics.
MACHINE LEARNING IN NETWORK SECURITY USING KNIME ANALYTICSIJNSA Journal
Machine learning has more and more effect on our every day’s life. This field keeps growing and expanding into new areas. Machine learning is based on the implementation of artificial intelligence that gives systems the capability to automatically learn and enhance from experiments without being explicitly programmed. Machine Learning algorithms apply mathematical equations to analyze datasets and predict values based on the dataset. In the field of cybersecurity, machine learning algorithms can be utilized to train and analyze the Intrusion Detection Systems (IDSs) on security-related datasets. In this paper, we tested different machine learning algorithms to analyze NSL-KDD dataset using KNIME analytics.
IRJET- An Intrusion Detection and Protection System by using Data Mining ...IRJET Journal
This document proposes an Internal Intrusion Detection and Protection System (IIDPS) to detect insider attacks by analyzing system calls (SCs) using data mining and forensic techniques. The IIDPS creates personal profiles for each user to track their computer usage behaviors over time. When a user logs in, the IIDPS compares their current behaviors to the patterns in their personal profile to determine if they are the legitimate account holder or an unauthorized insider attacker. The IIDPS aims to more accurately authenticate users and detect insider threats compared to existing systems that rely only on usernames and passwords.
Survey of Clustering Based Detection using IDS Technique IRJET Journal
This document discusses intrusion detection systems (IDS) and different techniques used for IDS, including clustering-based detection. It first provides background on IDS, describing their purpose of detecting intruders and protecting systems. It then outlines various IDS types, including mobile agent-based, cluster-based, cryptography-based, and others. The document also summarizes related work from other papers applying data mining techniques like clustering to improve IDS detection rates and reduce false alarms. Finally, it discusses problems with current and traditional IDS, such as threshold detection leading to false positives, and false negatives where attacks are missed.
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 %.
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.
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.
This document summarizes a research paper on adaptive personalized web search with safety seclusion. It discusses how personalized web search has improved search quality but user privacy concerns have limited its adoption. The paper proposes a system called UPS that can dynamically generalize user profiles during searches while respecting indicated privacy requirements. UPS uses greedy algorithms to balance personalization utility and privacy risk from exposing generalized profiles. The system aims to address limitations in existing personalized search regarding user security and accuracy needs.
- Wireless sensor networks are vulnerable to security attacks due to their distributed nature, multi-hop communication, and lack of resources. Intrusion detection systems play an important role in detecting attacks.
- There are three main types of intrusion detection systems: signature-based, anomaly-based, and specification-based (a hybrid of the two). Signature-based systems detect known attacks but miss new ones, while anomaly-based systems can detect new attacks but have high false positives.
- The paper compares these intrusion detection systems for wireless sensor networks and finds that anomaly-based systems have the lowest resource usage but may miss known attacks, while signature-based systems detect known attacks but use more resources. The best approach
LSTM deep learning method for network intrusion detection system IJECEIAES
The security of the network has become a primary concern for organizations. Attackers use different means to disrupt services, these various attacks push to think of a new way to block them all in one manner. In addition, these intrusions can change and penetrate the devices of security. To solve these issues, we suggest, in this paper, a new idea for Network Intrusion Detection System (NIDS) based on Long Short-Term Memory (LSTM) to recognize menaces and to obtain a long-term memory on them, in order to stop the new attacks that are like the existing ones, and at the same time, to have a single mean to block intrusions. According to the results of the experiments of detections that we have realized, the Accuracy reaches up to 99.98 % and 99.93 % for respectively the classification of two classes and several classes, also the False Positive Rate (FPR) reaches up to only 0,068 % and 0,023 % for respectively the classification of two classes and several classes, which proves that the proposed model is effective, it has a great ability to memorize and differentiate between normal traffic and attacks, and its identification is more accurate than other Machine Learning classifiers.
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.
Constructing a predictive model for an intelligent network intrusion detectionAlebachew Chiche
This document presents a study that constructs a predictive model for network intrusion detection using data mining techniques. The study uses the KDD Cup 99 intrusion detection dataset to build classification models using J48 decision tree, JRip rule induction, Naive Bayes, and multilayer perceptron algorithms. The J48 decision tree algorithm achieved the highest accuracy of 99.91% and was selected to build the predictive model. This model was then integrated with a knowledge-based system to build an intelligent network intrusion detection system capable of automatically detecting network attacks, mapping detections to attack categories, and updating the training data over time. Experimental evaluation found the integrated system achieved 91.43% accuracy and 83% user acceptance in detecting network intrusions
Machine learning-based intrusion detection system for detecting web attacksIAESIJAI
The increasing use of smart devices results in a huge amount of data, which raises concerns about personal data, including health data and financial data. This data circulates on the network and can encounter network traffic at any time. This traffic can either be normal traffic or an intrusion created by hackers with the aim of injecting abnormal traffic into the network. Firewalls and traditional intrusion detection systems detect attacks based on signature patterns. However, this is not sufficient to detect advanced or unknown attacks. To detect different types of unknown attacks, the use of intelligent techniques is essential. In this paper, we analyse some machine learning techniques proposed in recent years. In this study, several classifications were made to detect anomalous behaviour in network traffic. The models were built and evaluated based on the Canadian Institute for Cybersecurity-intrusion detection systems dataset released in 2017 (CIC-IDS-2017), which includes both current and historical attacks. The experiments were conducted using decision tree, random forest, logistic regression, gaussian naïve bayes, adaptive boosting, and their ensemble approach. The models were evaluated using various evaluation metrics such as accuracy, precision, recall, F1-score, false positive rate, receiver operating characteristic curve, and calibration curve.
Three level intrusion detection system based on conditional generative advers...IJECEIAES
Security threat protection is important in the internet of things (IoT) applications since both the connected device and the captured data can be hacked or hijacked or both at the same time. To tackle the above-mentioned problem, we proposed three-level intrusion detection system conditional generative adversarial network (3LIDS-CGAN) model which includes four phases such as first-level intrusion detection system (IDS), second-level IDS, third-level IDS, and attack type classification. In first-level IDS, features of the incoming packets are extracted by the firewall. Based on the extracted features the packets are classified into three classes such as normal, malicious, and suspicious using support vector machine and golden eagle optimization. Suspicious packets are forwarded to the second-level IDS which classified the suspicious packets as normal or malicious. Here, signature-based intrusions are detected using attack history information, and anomaly-based intrusions are detected using event-based semantic mapping. In third-level IDS, adversary packets are detected using CGAN which automatically learns the adversarial environment and detects adversary packets accurately. Finally, proximal policy optimization is proposed to detect the attack type. Experiments are conducted using the NS-3.26 network simulator and performance is evaluated by various performance metrics which results that the proposed 3LIDS-CGAN model outperforming other existing works.
HYBRID ARCHITECTURE FOR DISTRIBUTED INTRUSION DETECTION SYSTEM IN WIRELESS NE...IJNSA Journal
In order to the rapid growth of the network application, new kinds of network attacks are emerging endlessly. So it is critical to protect the networks from attackers and the Intrusion detection technology becomes popular. Therefore, it is necessary that this security concern must be articulate right from the beginning of the network design and deployment. The intrusion detection technology is the process of identifying network activity that can lead to a compromise of security policy. Lot of work has been done in detection of intruders. But the solutions are not satisfactory. In this paper, we propose a novel Distributed Intrusion Detection System using Multi Agent In order to decrease false alarms and manage misuse and anomaly detects.
Detecting network attacks model based on a convolutional neural network IJECEIAES
Due to the increasing use of networks at present, Internet systems have raised many security problems, and statistics indicate that the rate of attacks or intrusions has increased excessively annually, and in the event of any malicious attack on network vulnerabilities or information systems, it may lead to serious disasters, violating policies on network security, i.e., “confidentiality, integrity, and availability” (CIA). Therefore, many detection systems, such as the intrusion detection system, appeared. In this paper, we built a system that detects network attacks using the latest machine learning algorithms and a convolutional neural network based on a dataset of the CSE-CIC-IDS2018. It is a recent dataset that contains a set of common and recent attacks. The detection rate is 99.7%, distinguishing between aggressive attacks and natural assertiveness.
FORTIFICATION OF HYBRID INTRUSION DETECTION SYSTEM USING VARIANTS OF NEURAL ...IJNSA Journal
Intrusion Detection Systems (IDS) form a key part of system defence, where it identifies abnormal
activities happening in a computer system. In recent years different soft computing based techniques have
been proposed for the development of IDS. On the other hand, intrusion detection is not yet a perfect
technology. This has provided an opportunity for data mining to make quite a lot of important
contributions in the field of intrusion detection. In this paper we have proposed a new hybrid technique
by utilizing data mining techniques such as fuzzy C means clustering, Fuzzy neural network / Neurofuzzy and radial basis function(RBF) SVM for fortification of the intrusion detection system. The
proposed technique has five major steps in which, first step is to perform the relevance analysis, and then
input data is clustered using Fuzzy C-means clustering. After that, neuro-fuzzy is trained, such that each
of the data point is trained with the corresponding neuro-fuzzy classifier associated with the cluster.
Subsequently, a vector for SVM classification is formed and in the last step, classification using RBF-
SVM is performed to detect intrusion has happened or not. Data set used is the KDD cup 1999 dataset
and we have used precision, recall, F-measure and accuracy as the evaluation metrics parameters. Our
technique could achieve better accuracy for all types of intrusions. The results of proposed technique are
compared with the other existing techniques. These comparisons proved the effectiveness of our
technique.
ATTACK DETECTION AVAILING FEATURE DISCRETION USING RANDOM FOREST CLASSIFIERCSEIJJournal
This document discusses using a random forest classifier with feature selection to improve intrusion detection. It begins with background on intrusion detection systems and challenges. It then proposes using genetic algorithms for feature selection to identify the most important features from a dataset. A random forest classifier is used for classification, which combines decision trees to improve accuracy. The methodology involves feature selection, classification with random forest, and detection. Feature weights are calculated and cross-validation is used to analyze detection rates for individual attacks. The goal is to improve accuracy, reduce training time, and better detect minority attacks through this approach.
Attack Detection Availing Feature Discretion using Random Forest ClassifierCSEIJJournal
The widespread use of the Internet has an adverse effect of being vulnerable to cyber attacks. Defensive
mechanisms like firewalls and IDSs have evolved with a lot of research contributions happening in these
areas. Machine learning techniques have been successfully used in these defense mechanisms especially
IDSs. Although they are effective to some extent in identifying new patterns and variants of existing
malicious patterns, many attacks are still left as undetected. The objective is to develop an algorithm for
detecting malicious domains based on passive traffic measurements. In this paper, an anomaly-based
intrusion detection system based on an ensemble based machine learning classifier called Random Forest
with gradient boosting is deployed. NSL-KDD cup dataset is used for analysis and out of 41 features, 32
features were identified as significant using feature discretion.
DETECTION OF ATTACKS IN WIRELESS NETWORKS USING DATA MINING TECHNIQUESIAEME Publication
With the progressive increase of network application and electronic devices (computer, mobile phones, android, etc), attack and intrusion detection is becoming a very challenging task in cybercrime detection area. in this context, most of existing approaches of attack detection rely mainly on a finite set of attacks. However, these solutions are vulnerable, that is, they fail in detecting some attacks when sources of information’s are ambiguous or imperfect. But, few approaches started investigating toward this direction. Following this trends, this paper investigates the role of machine learning approach (ANN, SVM) in detecting TCP connection traffic as normal or suspicious one. But, using ANN and SVM is an expensive technique individually. In this paper, combining two classifiers has been proposed, where artificial neural network (ANN) classifier and support vector machine (SVM) were employed. Additionally, our proposed solution allows visualizing obtained classification results. Accuracy of the proposed solution has been compared with other classifier results. Experiments have been conducted with different network connection selected from NSL-KDD DARPA dataset. Empirical results show that combining ANN and SVM techniques for attack detection is a promising direction
COPYRIGHTThis thesis is copyright materials protected under the .docxvoversbyobersby
COPYRIGHT
This thesis is copyright materials protected under the Berne Convection, the copyright Act 1999 and other international and national enactments in that behalf, on intellectual property. It may not be reproduced by any means in full or in part except for short extracts in fair dealing so for research or private study, critical scholarly review or discourse with acknowledgment, with written permission of the Dean School of Graduate Studies on behalf of both the author and XXX XXX University.ABSTRACT
With Fast growing internet world the risk of intrusion has also increased, as a result Intrusion Detection System (IDS) is the admired key research field. IDS are used to identify any suspicious activity or patterns in the network or machine, which endeavors the security features or compromise the machine. IDS majorly use all the features of the data. It is a keen observation that all the features are not of equal relevance for the detection of attacks. Moreover every feature does not contribute in enhancing the system performance significantly. The main aim of the work done is to develop an efficient denial of service network intrusion classification model. The specific objectives included: to analyse existing literature in intrusion detection systems; what are the techniques used to model IDS, types of network attacks, performance of various machine learning tools, how are network intrusion detection systems assessed; to find out top network traffic attributes that can be used to model denial of service intrusion detection; to develop a machine learning model for detection of denial of service network intrusion.Methods: The research design was experimental and data was collected by simulation using NSL-KDD dataset. By implementing Correlation Feature Selection (CFS) mechanism using three search algorithms, a smallest set of features is selected with all the features that are selected very frequently. Findings: The smallest subset of features chosen is the most nominal among all the feature subset found. Further, the performances using Artificial neural networks(ANN), decision trees, Support Vector Machines (SVM) and K-Nearest Neighbour (KNN) classifiers is compared for 7 subsets found by filter model and 41 attributes. Results: The outcome indicates a remarkable improvement in the performance metrics used for comparison of the two classifiers. The results show that using 17/18 selected features improves DOS types classification accuracies as compared to using the 41 features in the NSL-KDD dataset. It was further observed that using an ensemble of three classifiers with decision fusion performs better as compared to using a single classifier for DOS type’s classification. Among machine learning tools experimented, ANN achieved best classification accuracies followed by SVM and DT. KNN registered the lowest classification accuracies. Application: The proposed work with such an improved detection rate and lesser classification time and lar.
Visualize network anomaly detection by using k means clustering algorithmIJCNCJournal
With the ever increasing amount of new attacks in today’s world the amount of data will keep increasing,
and because of the base-rate fallacy the amount of false alarms will also increase. Another problem with
detection of attacks is that they usually isn’t detected until after the attack has taken place, this makes
defending against attacks hard and can easily lead to disclosure of sensitive information.
In this paper we choose K-means algorithm with the Kdd Cup 1999 network data set to evaluate the
performance of an unsupervised learning method for anomaly detection. The results of the evaluation
showed that a high detection rate can be achieve while maintaining a low false alarm rate .This paper
presents the result of using k-means clustering by applying Cluster 3.0 tool and visualized this result by
using TreeView visualization tool .
Trust Metric-Based Anomaly Detection Via Deep Deterministic Policy Gradient R...IJCNCJournal
Addressing real-time network security issues is paramount due to the rapidly expanding IoT jargon. The erratic rise in usage of inadequately secured IoT- based sensory devices like wearables of mobile users, autonomous vehicles, smartphones and appliances by a larger user community is fuelling the need for a trustable, super-performant security framework. An efficient anomaly detection system would aim to address the anomaly detection problem by devising a competent attack detection model. This paper delves into the Deep Deterministic Policy Gradient (DDPG) approach, a promising Reinforcement Learning platform to combat noisy sensor samples which are instigated by alarming network attacks. The authors propose an enhanced DDPG approach based on trust metrics and belief networks, referred to as Deep Deterministic Policy Gradient Belief Network (DDPG-BN). This deep-learning-based approach is projected as an algorithm to provide “Deep-Defense” to the plethora of network attacks. Confidence interval is chosen as the trust metric to decide on the termination of sensor sample collection. Once an enlisted attack is detected, the collection of samples from the particular sensor will automatically cease. The evaluations and results of the experiments highlight a better detection accuracy of 98.37% compared to its counterpart conventional DDPG implementation of 97.46%. The paper also covers the work based on a contemporary Deep Reinforcement Learning (DRL) algorithm, the Actor Critic (AC). The proposed deep learning binary classification model is validated using the NSL-KDD dataset and the performance is compared to a few deep learning implementations as well.
Trust Metric-Based Anomaly Detection via Deep Deterministic Policy Gradient R...IJCNCJournal
Addressing real-time network security issues is paramount due to the rapidly expanding IoT jargon. The erratic rise in usage of inadequately secured IoT- based sensory devices like wearables of mobile users, autonomous vehicles, smartphones and appliances by a larger user community is fuelling the need for a trustable, super-performant security framework. An efficient anomaly detection system would aim to address the anomaly detection problem by devising a competent attack detection model. This paper delves into the Deep Deterministic Policy Gradient (DDPG) approach, a promising Reinforcement Learning platform to combat noisy sensor samples which are instigated by alarming network attacks. The authors propose an enhanced DDPG approach based on trust metrics and belief networks, referred to as Deep Deterministic Policy Gradient Belief Network (DDPG-BN). This deep-learning-based approach is projected as an algorithm to provide “Deep-Defense” to the plethora of network attacks. Confidence interval is chosen as the trust metric to decide on the termination of sensor sample collection. Once an enlisted attack is detected, the collection of samples from the particular sensor will automatically cease. The evaluations and results of the experiments highlight a better detection accuracy of 98.37% compared to its counterpart conventional DDPG implementation of 97.46%. The paper also covers the work based on a contemporary Deep Reinforcement Learning (DRL) algorithm, the Actor Critic (AC). The proposed deep learning binary classification model is validated using the NSL-KDD dataset and the performance is compared to a few deep learning implementations as well.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Evaluation of network intrusion detection using markov chainIJCI JOURNAL
Day today life internet threat has been increased significantly. There is a need to develop model in order to
maintain security of system. The most effective techniques are Intrusion Detection System (IDS).The
purpose of intrusion system through the security devices detect and deal with it. In this paper, a
mathematical approach is used effectively to predict and detect intrusion in the network. Here we discuss
about two algorithms ‘K-Means + Apriori’, a method which classify normal and abnormal activities in
computer network. In K-Means process, it partitions the training set into K-clusters using Euclidean
distance and introduce an outlier factor, then it build Apriori Algorithm to prune the data by removing
infrequent data in the database. Based on defined state the degree of incoming data is evaluated through
the experiment using sample DARPA2000 dataset, and achieves high detection performance in level of
attack in stages.
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.
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
An approach for ids by combining svm and ant colony algorithmeSAT Journals
This summarizes a research paper that proposes a new approach called CSVAC (Combined Support Vector with Ant Colony) for intrusion detection. The approach combines two algorithms, Support Vector Machine (SVM) and Ant Colony Optimization (ACO), to classify network data as normal or abnormal. SVM is used to generate a separating hyperplane and find support vectors, while ACO performs clustering around the support vectors. The clusters are added to the SVM training set and it is retrained in an iterative process until the detection rate exceeds a threshold. The paper evaluates this approach on the standard KDD99 dataset and finds it achieves superior results to other algorithms in terms of accuracy and efficiency.
NETWORK INTRUSION DETECTION AND COUNTERMEASURE SELECTION IN VIRTUAL NETWORK (...ijsptm
Intrusion in a network or a system is a problem today as the trend of successful network attacks continue to
rise. Intruders can explore vulnerabilities of a network system to gain access in order to deploy some virus
or malware such as Denial of Service (DOS) attack. In this work, a frequency-based Intrusion Detection
System (IDS) is proposed to detect DOS attack. The frequency data is extracted from the time-series data
created by the traffic flow using Discrete Fourier Transform (DFT). An algorithm is developed for
anomaly-based intrusion detection with fewer false alarms which further detect known and unknown attack
signature in a network. The frequency of the traffic data of the virus or malware would be inconsistent with
the frequency of the legitimate traffic data. A Centralized Traffic Analyzer Intrusion Detection System
called CTA-IDS is introduced to further detect inside attackers in a network. The strategy is effective in
detecting abnormal content in the traffic data during information passing from one node to another and
also detects known attack signature and unknown attack. This approach is tested by running the artificial
network intrusion data in simulated networks using the Network Simulator2 (NS2) software.
Similar to CLASSIFICATION PROCEDURES FOR INTRUSION DETECTION BASED ON KDD CUP 99 DATA SET (20)
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEM
CLASSIFICATION PROCEDURES FOR INTRUSION DETECTION BASED ON KDD CUP 99 DATA SET
1. International Journal of Network Security & Its Applications (IJNSA) Vol. 11, No.3, May 2019
DOI: 10.5121/ijnsa.2019.11302 21
CLASSIFICATION PROCEDURES FOR INTRUSION
DETECTION BASED ON KDD CUP 99 DATA SET
Shaker El-Sappagh, Ahmed Saad Mohammed, Tarek Ahmed AlSheshtawy
Faculty of Computers & Informatics, Benha University, Egypt.
ABSTRACT
In network security framework, intrusion detection is one of a benchmark part and is a fundamental way
to protect PC from many threads. The huge issue in intrusion detection is presented as a huge number
of false alerts; this issue motivates several experts to discover the solution for minifying false alerts
according to data mining that is a consideration as analysis procedure utilized in a large data e.g. KDD
CUP 99. This paper presented various data mining classification for handling false alerts in intrusion
detection as reviewed. According to the result of testing many procedure of data mining on KDD CUP 99
that is no individual procedure can reveal all attack class, with high accuracy and without false alerts. The
best accuracy in Multilayer Perceptron is 92%; however, the best Training Time in Rule based model is 4
seconds . It is concluded that ,various procedures should be utilized to handle several of network attacks.
KEYWORDS
Intrusion Detection, Data Mining, KDD CUP 99, False Alarms
1. INTRODUCTION
Communication system plays an inevitable role in common people’s daily life. Computer
networks are effectively used for business data processing, education and learning, collaboration,
widespread data acquisition, and entertainment [1]. With the enormous growth of computer
networks usage and internet accessibility, more organizations are becoming susceptible to a wide
variety of attacks and threats [2]. One of the main challenges in the security management of large-
scale high-speed networks is the detection of suspicious anomalies in network traffic patterns due
to distributed denial of service (DDoS) attacks or worm propagation [3].
Generally, the major focus of the network attacks is to increase the threat against the commercial
business and our daily life, so it becomes a serious problem for the researchers to find a suitable
solution for these types of attacks [4]. Network security is becoming an absolute necessity to
protect information contained in the computer systems worldwide. With the rapid expansion of
computer networks during the past decade, the network grows in size and complexity, and
computer services expansion, vulnerabilities within the local area and wide area network become
a huge problem [5]. Nowadays, network security is a world hot topic in computer security and
defense. Intrusions, attacks, or anomalies in network infrastructure lead mostly in great financial
losses and massive sensitive data leaks. Therefore, they decrease the efficiency and quality of
productivity of organizations [6]. Reliance on Internet and world wide connectivity has increased
the potential damage that can be inflicted by attacks launched over Internet against remote
systems. Successful attacks inevitably occur despite the best security precautions [7].
Intrusion detection system (IDS) is a program that tries to find indications that the computer has
been compromised [8]. It attempts to detect an intruder breaking into computer system or
2. International Journal of Network Security & Its Applications (IJNSA) Vol. 11, No.3, May 2019
22
legitimate user misuses system resources. Intrusion detection is an important issue and has
captured the attention of network administrators and security professionals. It is the art of
detecting unauthorized, inappropriate, or anomalous activity on computer systems. IDSs are
classified as network based, host based, or application based depending on their mode of
deployment and data used for analysis [9]. In addition, IDSs can also be classified as signature
based or anomaly based depending upon the attack detection method. The signature-based
systems are trained by extracting specific patterns (or signatures) from previously known attacks
while the anomaly-based systems learn from the normal data collected when there is no
anomalous activity [10, 11]. The main purpose of IDS is to detect as many attacks as possible
with the minimum number of false alarms. In other words, the system must be accurate in
detecting attacks. However, accurate systems that cannot handle large amount of network traffic
and is slow in decision making will not fulfill the purpose of an intrusion detection system [12].
The huge issue in IDs is presence of huge number of false alerts; this issue is being motivated by
several experts to discover the solution for minifying false alerts. . This research will be presented
as various data mining classification for handling this issue in IDSs as reviewed.
2. RELATED WORK
This section briefly discusses many techniques of classification used for classifying intrusion
detection datasets including decision trees, Bayesian classification, artificial neural network,
support vector machines, associative classification, and k-nearest neighbor. Classifiers have been
suggested and developed to reduce false alarm of intrusion detection in the area of network
security based on different ideas.
Warrender et al. [13] have proposed several intrusion detection methods based on system call
trace data. They tested a method that utilizes sliding windows to determine a database of normal
sequences to form a database for testing against test instances and classify instances according to
those in the normal sequence database. This requires the maintenance of a large database of
normal system call trace sequences. Wenke et al. [14] proposed the Mining Audit Data for
Automated Models for Intrusion Detection project. It is one of the best known data mining
projects in intrusion detection. It is an off-line IDS to produce anomaly and misuse intrusion
detection models for network and host systems. Association rules and frequent episodes are
applied to replace hand-coded intrusion patterns and profiles with the learned rules. Agarwal et al.
[15] proposed a two-stage general-to-specific framework for learning a rule-based model
(PNrule) to learn classifier models on KDD 99 data set. Barbara et al. [16] proposed (Audit Data
Analysis and Mining), which is an intrusion detector built to detect intrusions using data mining
techniques. It first absorbs training data known to be free of attacks. Next, it uses an algorithm to
group attacks, unknown behavior, and false alarms.
Abraham [17] proposed (Intrusion Detection using Data Mining Technique), which is a real-time
NIDS for misuse and anomaly detection. It applies association rules, Meta rules, and
characteristic rules. It employs data mining to produce a description of network data and uses this
information for deviation analysis. Zhang et al. [18] proposed a statistical neural network
classifier for anomaly detection, which can identify UDP flood attacks. Comparing different
neural network classifiers, the back propagation neural network (BPN) has shown to be more
efficient in developing IDS. . Xu et al. [19] presented a framework for adaptive intrusion
detection based on machine learning. Multi-class Support Vector Machines (SVMs) is applied to
classifier construction in IDSs. Li et al. [20] though realized the deficiencies of KDD dataset,
developed a supervised network intrusion detection method based on Transductive Confidence
Machines for K-Nearest Neighbors (TCM-KNN) machine learning algorithm and active learning
3. International Journal of Network Security & Its Applications (IJNSA) Vol. 11, No.3, May 2019
23
based training data selection method. Panda et al. [21] study performance of three well known
data mining classifier algorithms namely, ID3, J48, and Naïve Bayes are evaluated on the KDD
CUP 99 data set. Mohammed et al. [22] proposed a comprehensive analysis classification
techniques are used to predict the severity of attacks over the network. They compared zero R
classifier, Decision table classifier and Random Forest classifier with KDD CUP 99 databases
from MIT Lincoln laboratory. Sathyabama et al. [23] used clustering techniques to group user’s
behavior together depending on their similarity and to detect different behaviors and specified as
outliers.
Chihab et al. [24] presented five data mining algorithms like (ID3, Naive Bayes, Random forest,
C4.5, and multilayer perceptron) to make the comparison between them which applied on
network intrusions and get the best proposal of a hybrid classifier based on naïve Bayes and
random forest algorithms. The results shows that the hybrid system improved the prediction with
reduced, consuming time. Keerthika et al,[25] provided proposal, which focuses on the naïve
feature reduction, in addition to feature selection methods such as gain ratio and information gain
for reducing the redundant and irrelevant of features. This proposal used naïve Bayes classifier to
design intrusion detection system. Tesfahun et al. [26] suggested an effective hybrid layered
intrusion detection system by combining misuse and anomaly IDS for detecting both previously
known and unknown attacks. The first layer consisted of misuse detector, which is based on
random forest classifier for detect and stop known attacks; the second layer involved anomaly
detector was built using bagging technique with a staff of one class (SVM) classifiers. The results
showed that system can detect previously unknown attacks with a detection rate improvement of
(18.73%) by using NSL-KDD dataset. Aggarwal et al. [27] assessed several classification
algorithms like Random Forest, Naïve Bayes, C4.5, and Decision Table. They compared these
classification algorithms in WEKA with KDD99 dataset. These classifiers were resolved
according to metrics like accuracy, precision, and F-score. Random Tree displays the best
outcomes aggregate in contrast to the algorithms, which have high detection and low false alarm
rate were C4.5 and Random Forest. Mukund et al. [28] proposed the existing algorithms for
intrusion detection system to introduce an improved way of using the HDFS (Hadoop Distributed
File System). So to reduce the false alarm rate, they used decision tree technique and enhanced it
in the process with the multi-system capabilities of the HDFS. Therefore this approach reduced
the time taken by the DFS and improved the accuracy of the IDS.
Gupta et al. [29] IDSs monitors the network or malicious activities and forbidden access to
devices. IDSs used to protect the data's features and integrity. The proposal was used NSL-KDD
dataset to learn the manner of the attacks depending on the methods of data mining such as
logistic regression and K-means clustering. Hence, it generates rules for classifying network
activities. The results show that linear regression was very effective accuracy in detecting attacks
was (80%) while the K-means clustering was showed kind results with (67%) accuracy.
Akashdeep Sharma et al. [30] work proposes an intelligent system which first performs feature
ranking on the basis of information gain and correlation. Feature reduction is then done by
combining ranks obtained from both information gain and correlation using a novel approach to
identify useful and useless features. These reduced features are then fed to a feed forward neural
network for training and testing on KDD99 dataset. Kabir et al. [31]. Proposes a novel approach
for intrusion detection system based on sampling with Least Square Support Vector Machine (LS-
SVM). Decision making is performed in two stages. In the first stage, the whole dataset is divided
into some predetermined arbitrary subgroups. The proposed algorithm selects representative
samples from these subgroups such that the samples reflect the entire dataset. In the second stage,
least square support vector machine (LS-SVM) is applied to the extracted samples to detect
intrusions on KDD 99 database which is considered a de facto benchmark for evaluating the
performance of intrusions detection algorithm.
4. International Journal of Network Security & Its Applications (IJNSA) Vol. 11, No.3, May 2019
24
Liyu Duan and Youan Xiao [32] large volume of the data and unbalanced data, intrusion data
were inevitable obstacles. So, solve those issues utilizing the fuzzy c-means procedure to
reconstruct feature vectors according to central points. Nathan et al [33] This paper shows deep
learning procedure for intrusion detection, which implemented in GPU-enabled TensorFlow and
evaluated utilizing KDD 99 dataset. Osamah et al [34] In this paper, introduced learning
procedure for intrusion detection according to tree calculation on the KDD-99.
3. INTRUSION DETECTION SYSTEM
IDS can be defined as a combination of software and/or hardware components which monitors
computer systems and makes an alarm when an intrusion occurs [35]. The basic architecture of
IDS is shown in Figure 1.
Figure 1: Basic Architecture of IDS [36].
The components in this architectural framework are as follows [36].
• Data Gathering Device: responsible for collecting the data from the monitored system.
• Detector–ID Engine: processes the data collected from sensors to identify intrusive
behavior and send an alarm signal to response component if there is an intrusion.
• Knowledge Base: contains pre-processed information provided by network experts and
collected by sensors.
• Configuration Device: provides information about the current state of IDS.
• Response Component: initiates the response (active or inactive) when intrusion is
detected.
4. EVOLUTION OF INTRUSION DETECTION
There are many metrics for evaluating the IDS performance. The following is a description of
some of these metrics [37]:
• Predictive accuracy: The two measures used for evaluating the predictive performance of
IDS are: (i) detection rate and (ii) false alarm rate. Detection Rate (DR) also known as True
Positive Rate (TPR) is defined as the ratio of number of attacks correctly detected to the total
number of attacks, while the False Alarm (false positive) Rate (FAR) is the ratio of the
number of normal connections that are incorrectly classified as attacks to the total number of
normal connections.
5. International Journal of Network Security & Its Applications (IJNSA) Vol. 11, No.3, May 2019
25
• True Positive (TP): IDS producing an alarm when a legitimate attack occurs. False Positive
(FP): IDS producing an alarm when no attack occurs. False Negative (FN): IDS producing no
alarm when the actual attack occurs. True Negative (TN): IDS producing no alarm when no
attack occurs
• Receiver Operating Characteristics (ROC): Evaluation of IDS can also be performed
using Receiver Operating Characteristics (ROC). ROC graphs depicts trade-offs between
detection rate and false alarm rate. In ROC , the point that corresponds to 0% false alarm rate
and 100% detection rate represents the perfect IDS (Foster Provost, Tom Fawcett)
• performance Time: The performance time of IDS is the total time taken by IDS to detect the
intrusion
5. INTRUSION DETECTION DATASET
In this section, brief description of KDD Cup 1999 dataset which was derived from the 1998
DARPA Intrusion detection Evaluation program is provided. It is the most widespread dataset
collected over a period of nine weeks for a LAN simulating a typical U.S. Air Force LAN. The
dataset contains a collection of simulated raw TCP dump data, where, multiple intrusions attacks
was introduced and widely used in the research community from seven weeks of network traffic.
The dataset contains 4,898,430 labeled and 311,029 unlabeled connection records. The labeled
connection records consist of 41 attributes. In network data of KDD99 dataset, each instance
represents feature values of a class, where each class is categorized either normal or attack. The
classes in the dataset are characterized into one normal class and four main intrusion classes [38] :
• Normal: connections are generated by simulating user behavior.
• DoS attacks: use of resources or services is denied to authorized users.
• Probe attacks: information about the system is exposed to unauthorized entities.
• User to Remote attacks: access to account types of administrator is gained by
unauthorized entities.
• Remote to Local attacks: access to hosts is gained by unauthorized entities.
6. DATA MINING AND INTRUSION DETECTION
Data mining is the process of discovering interesting knowledge from large amounts of data
stored either in databases, data warehouses, or other information repositories [39]. Classification
is a data mining technique, which arranges data into predefined groups. The goal of predictive
classification is to predict the target class accurately for each record in a set of new data, that is,
data that is not in the historical data [40]. Intrusion detection can be defined as a classification
problem where each audit record can be classified into one of a discrete set of possible categories
(i.e. normal or a particular kind of intrusion). Intrusion detection using data mining have attracted
more and more interests in recent years by utilizing procedures programs applied to audit data to
compute misuse and anomaly detection models [41].
7. IMPLEMENTED DATA MINING METHODS BASED ON KDD CUP 99
In IDSs, there are important surveys of implemented data mining methods on KDD Cup 99 by
various experts.
6. International Journal of Network Security & Its Applications (IJNSA) Vol. 11, No.3, May 2019
26
• Multilayer perceptron for classification of KDD dataset: it is consider as the most usually
neural network procedure according to one layer for input, hidden and output [41] [42].
• Rule based model: simple procedure usually with good rules for depicting the framework in
data [43].
• Support vector machines: it is a procedure for converting the training data to a feature scope
hence getting the best splitting hyperplane [44].
• Naïve Bayes: it is a simple procedure according to probabilistic relined underlay an individual
structure [44].
• Apriori Association Rule Mining Algorithm: It is the procedure to giving frequent item sets
according to the dataset and making scan process to identify, most frequent items [45].
• K Means clustering: it is a procedure for tasking of dataset points to clusters according to the
distance between dataset points and cluster centroid [46].
• ID3, C4.5, and C5.0 decision tree algorithms: its procedure for building a decision tree for
classification dataset according to training data [47].
8. RESULTS AND DISCUSSION
The fundamental aim of this research is to decide the excellent procedure of data mining
procedures to classify KDD99 so as to has a high accuracy and low time in knowing attacks.
Furthermore, smoothing the mission of select for expert’s in the future on KDD dataset, Good
implementing cases of all the seven procedures aforementioned over were assessed. Results are
given in the Table 1 to compare the classifiers; for IDs, utilizing accuracy and Training Time for
knowing the best procedure for the classifier. Simply as predictably that no sole procedure can
reveal all attack class, with high accuracy and without false alarm ratio. The best accuracy in
Multilayer Perceptron is 92% however the best Training Time in Rule based model is 4 seconds.
Table 1: Comparison of Seven Procedures
9. CONCLUSION
Many data mining procedures have been focused by the experts in IDS scope and they purpose to
minify the great load of analyzing massive quantity of data. KDD Cup’99 data set is suffering
from a variance inter the classes which impact miserable detection and is a main issue to data
mining procedures. An important issue in designing IDS is minified false alarm ratio and
attaining high detection ratio. Utilizing various classification procedures, potential to minify false
alarm enhances the detection accuracy and many classification procedures. In this research,
utilized by the experts in the performance of IDS structure are discussed and reviewed. From the
7. International Journal of Network Security & Its Applications (IJNSA) Vol. 11, No.3, May 2019
27
tentative survey applied this paper specified that various experts suggest various procedures for
IDS scope in various group , but still, needed to search.
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AUTHORS
Shaker El-Sappagh received the bachelor's degree in computer science from the
Information Systems Department, Faculty of Computers and Information, Cairo
University, Egypt, in 1997,the master’s degree from Cairo University, in 2007,and the
Ph.D. degree in computer science from the Information Systems Department, Faculty of
Computers and Information, Mansura University,Mansura, Egypt, in 2015. In 2003, he
joined the Department of Information Systems, Faculty of Computers and Information,
Minia University, Egypt, as a Teaching Assistant. Since 2016, he has been an Assistant
Professor with the Department of Information Systems, Faculty of Computers and Information, Benha
University. He is currently a Postdoctoral Fellow with the UWB Wire-less Communications Research
Center, Department of Information and Communication Engineering, Inha University, South Korea. He has
publications in clinical decision support systems and semantic intelligence. His current research interests
include machine learning, medical informatics, (fuzzy) ontology engineering, distributed and hybrid
clinical decision support systems, semantic data modeling, fuzzy expert systems, and cloud computing. He
is very interested in the diseases diagnosis and treatment researches. He is a Reviewer for many journals
Ahmed saad Mohammed received the bachelor's degree from the Software engineering
Department, Baghdad College of Economic Sciences University, Iraq, Baghdad in 2005.
His current research interests include machine learning, data mining and artificial
intelligence
Dr. Tarek El-Shishtawy is a professor of Information System. His current work is vice
Dean of postgraduates and researches at faculty of computers and informatics. The
scientific interests in clude Information Retrieval, Data Mining, and researches related to
information systems in developing countries. Dr. Tarek published and refereed many
articles in NLP.