This document summarizes Amin Dastanpour's research comparing the use of genetic algorithms to optimize artificial neural networks and support vector machines for intrusion detection systems. The research aims to improve detection rates using machine learning algorithms with fewer features. It applies genetic algorithms to select optimal features for neural networks and support vector machines, achieving 100% detection rates using only 18 features for neural networks and 24 features for support vector machines. This outperforms other algorithms from related work and demonstrates the effectiveness of genetic algorithms for optimization.
ANALYSIS OF MACHINE LEARNING ALGORITHMS WITH FEATURE SELECTION FOR INTRUSION ...IJNSA Journal
In recent times, various machine learning classifiers are used to improve network intrusion detection. The researchers have proposed many solutions for intrusion detection in the literature. The machine learning classifiers are trained on older datasets for intrusion detection, which limits their detection accuracy. So, there is a need to train the machine learning classifiers on the latest dataset. In this paper, UNSW-NB15, the latest dataset is used to train machine learning classifiers. The selected classifiers such as K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD), Random Forest (RF), Logistic Regression (LR), and Naïve Bayes (NB) classifiers are used for training from the taxonomy of classifiers based on lazy and eager learners. In this paper, Chi-Square, a filter-based feature selection technique, is applied to the UNSW-NB15 dataset to reduce the irrelevant and redundant features. The performance of classifiers is measured in terms of Accuracy, Mean Squared Error (MSE), Precision, Recall, F1-Score, True Positive Rate (TPR) and False Positive Rate (FPR) with or without feature selection technique and comparative analysis of these machine learning classifiers is carried out.
High performance intrusion detection using modified k mean & naïve bayeseSAT Journals
Abstract
Internet Technology is growing at exponential rate day by day, making data security of computer systems more complex and critical. There has been multiple methodology implemented for the same in recent time as detailed in [1], [3]. Availability of larger bandwidth has made the multiple large computer server network connected worldwide and thus increasing the load on the necessity to secure data and Intrusion detection system (IDS) is one of the most efficient technique to maintain security of computer system. The proposed system is designed in such a way that are helpful in identifying malicious behavior and improper use of computer system. In this report we proposed a hybrid technique for intrusion detection using data mining algorithms. Our main objective is to do complete analysis of intrusion detection Dataset to test the implemented system.In This report we will propose a new methodology in which Modified k-mean is used for clustering whereas Naïve Bayes for the classification. These two data mining techniques will be used for Intrusion detection in large horizontally distributed database.
Keywords: Intrusion Detection, Modified K-Mean, Naïve Bays
Artificial Neural Content Techniques for Enhanced Intrusion Detection and Pre...AM Publications
This paper presents a novel approach for detecting network intrusions based on a competitive training neural
network. In the paper, the performance of this approach is compared to that of the self-organizing map (SOM), which is a
popular unsupervised training algorithm used in intrusion detection. While obtaining a similarly accurate detection rate as
the SOM does, the proposed approach uses only one forth of the computation times of the SOM. Furthermore, the
clustering result of this method is independent of the number of the initial neurons. This approach also exhibits the ability
to detect the known and unknown network attacks. The experimental results obtained by applying this approach to the
KDD-99 data set demonstrate that the proposed approach performs exceptionally in terms of both accuracy and
computation time.
A new clutering approach for anomaly intrusion detectionIJDKP
Recent advances in technology have made our work easier compare to earlier times. Computer network is
growing day by day but while discussing about the security of computers and networks it has always been a
major concerns for organizations varying from smaller to larger enterprises. It is true that organizations
are aware of the possible threats and attacks so they always prepare for the safer side but due to some
loopholes attackers are able to make attacks.
Intrusion detection is one of the major fields of research and researchers are trying to find new algorithms
for detecting intrusions. Clustering techniques of data mining is an interested area of research for detecting
possible intrusions and attacks. This paper presents a new clustering approach for anomaly intrusion
detection by using the approach of K-medoids method of clustering and its certain modifications. The
proposed algorithm is able to achieve high detection rate and overcomes the disadvantages of K-means
algorithm.
Improving the performance of Intrusion detection systemsyasmen essam
Intrusion detection systems (IDS) are widely studied by
researchers nowadays due to the dramatic growth in
network-based technologies. Policy violations and
unauthorized access is in turn increasing which makes
intrusion detection systems of great importance. Existing
approaches to improve intrusion detection systems focus on feature selection or reduction since some features are
irrelevant or redundant which when removed improve the
accuracy as well as the learning time.
ANALYSIS OF MACHINE LEARNING ALGORITHMS WITH FEATURE SELECTION FOR INTRUSION ...IJNSA Journal
In recent times, various machine learning classifiers are used to improve network intrusion detection. The researchers have proposed many solutions for intrusion detection in the literature. The machine learning classifiers are trained on older datasets for intrusion detection, which limits their detection accuracy. So, there is a need to train the machine learning classifiers on the latest dataset. In this paper, UNSW-NB15, the latest dataset is used to train machine learning classifiers. The selected classifiers such as K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD), Random Forest (RF), Logistic Regression (LR), and Naïve Bayes (NB) classifiers are used for training from the taxonomy of classifiers based on lazy and eager learners. In this paper, Chi-Square, a filter-based feature selection technique, is applied to the UNSW-NB15 dataset to reduce the irrelevant and redundant features. The performance of classifiers is measured in terms of Accuracy, Mean Squared Error (MSE), Precision, Recall, F1-Score, True Positive Rate (TPR) and False Positive Rate (FPR) with or without feature selection technique and comparative analysis of these machine learning classifiers is carried out.
High performance intrusion detection using modified k mean & naïve bayeseSAT Journals
Abstract
Internet Technology is growing at exponential rate day by day, making data security of computer systems more complex and critical. There has been multiple methodology implemented for the same in recent time as detailed in [1], [3]. Availability of larger bandwidth has made the multiple large computer server network connected worldwide and thus increasing the load on the necessity to secure data and Intrusion detection system (IDS) is one of the most efficient technique to maintain security of computer system. The proposed system is designed in such a way that are helpful in identifying malicious behavior and improper use of computer system. In this report we proposed a hybrid technique for intrusion detection using data mining algorithms. Our main objective is to do complete analysis of intrusion detection Dataset to test the implemented system.In This report we will propose a new methodology in which Modified k-mean is used for clustering whereas Naïve Bayes for the classification. These two data mining techniques will be used for Intrusion detection in large horizontally distributed database.
Keywords: Intrusion Detection, Modified K-Mean, Naïve Bays
Artificial Neural Content Techniques for Enhanced Intrusion Detection and Pre...AM Publications
This paper presents a novel approach for detecting network intrusions based on a competitive training neural
network. In the paper, the performance of this approach is compared to that of the self-organizing map (SOM), which is a
popular unsupervised training algorithm used in intrusion detection. While obtaining a similarly accurate detection rate as
the SOM does, the proposed approach uses only one forth of the computation times of the SOM. Furthermore, the
clustering result of this method is independent of the number of the initial neurons. This approach also exhibits the ability
to detect the known and unknown network attacks. The experimental results obtained by applying this approach to the
KDD-99 data set demonstrate that the proposed approach performs exceptionally in terms of both accuracy and
computation time.
A new clutering approach for anomaly intrusion detectionIJDKP
Recent advances in technology have made our work easier compare to earlier times. Computer network is
growing day by day but while discussing about the security of computers and networks it has always been a
major concerns for organizations varying from smaller to larger enterprises. It is true that organizations
are aware of the possible threats and attacks so they always prepare for the safer side but due to some
loopholes attackers are able to make attacks.
Intrusion detection is one of the major fields of research and researchers are trying to find new algorithms
for detecting intrusions. Clustering techniques of data mining is an interested area of research for detecting
possible intrusions and attacks. This paper presents a new clustering approach for anomaly intrusion
detection by using the approach of K-medoids method of clustering and its certain modifications. The
proposed algorithm is able to achieve high detection rate and overcomes the disadvantages of K-means
algorithm.
Improving the performance of Intrusion detection systemsyasmen essam
Intrusion detection systems (IDS) are widely studied by
researchers nowadays due to the dramatic growth in
network-based technologies. Policy violations and
unauthorized access is in turn increasing which makes
intrusion detection systems of great importance. Existing
approaches to improve intrusion detection systems focus on feature selection or reduction since some features are
irrelevant or redundant which when removed improve the
accuracy as well as the learning time.
Progress of Machine Learning in the Field of Intrusion Detection Systemsijcisjournal
With the growth in the use of the Internet and local area networks, malicious attacks and intrusions into
computer systems are increasing. Implementing intrusion detection systems have become extremely
important to help maintain good network security. Support vector machines (SVMs), a classic pattern
recognition tool, have been widely used in intrusion detection. They can handle very large data with high
efficiency, are easy to use, and exhibit good prediction behavior. This paper presents a new SVM model
enriched with a Gaussian kernel function based on the features of the training data for intrusion detection.
The new model is tested with the CICIDS2017 dataset. The test proves better results in terms of detection
efficiency and false alarm rate, which can give better coverage and make detection more efficient.
Minkowski Distance based Feature Selection Algorithm for Effective Intrusion ...IJMER
Intrusion Detection System (IDS) plays a major role in the provision of effective security to various types of networks. Moreover, Intrusion Detection System for networks need appropriate rule set for classifying network bench mark data into normal or attack patterns. Generally, each dataset is characterized by a large set of features. However, all these features will not be relevant or fully contribute in identifying an attack. Since different attacks need various subsets to provide better detection accuracy. In this paper an improved feature selection algorithm is proposed to identify the most appropriate subset of features for detecting a certain attacks. This proposed method is based on Minkowski distance feature ranking and an improved exhaustive search that selects a better combination of features. This system has been evaluated using the KDD CUP 1999 dataset and also with EMSVM [1] classifier. The experimental results show that the proposed system provides high classification accuracy and low false alarm rate when applied on the reduced feature subsets
Comparative Performance Analysis of Machine Learning Techniques for Software ...csandit
Machine learning techniques can be used to analyse data from different perspectives and enable
developers to retrieve useful information. Machine learning techniques are proven to be useful
in terms of software bug prediction. In this paper, a comparative performance analysis of
different machine learning techniques is explored for software bug prediction on public
available data sets. Results showed most of the machine learning methods performed well on
software bug datasets.
A critical review on Adversarial Attacks on Intrusion Detection Systems: Must...PhD Assistance
The present article helps the USA, the UK, Europe and the Australian students pursuing their computer Science postgraduate degree to identify right topic in the area of computer science specifically on deep learning, adversarial attacks and intrusion detection system. These topics are researched in-depth at the University of Spain, Cornell University, University of Modena and Reggio Emilia, Modena, Italy, and many more
http://www.phdassistance.com/industries/computer-science-information/
PhD Assistance offers UK Dissertation Research Topics Services in Computer Science Engineering Domain. When you Order Computer Science Dissertation Services at PhD Assistance, we promise you the following – Plagiarism free, Always on Time, outstanding customer support, written to Standard, Unlimited Revisions support and High-quality Subject Matter Experts http://www.phdassistance.com/services/phd-literature-review/gap-identification/
For Any Queries : Website: www.phdassistance.com
Phd Research Lab : www.research.phdassistance.com
Email: info@phdassistance.com
Phone : +91-4448137070
Contact Name Ganesh / Vinoth Kumar
Part I Machine learning technique
Introduction to Machine Learning
Genetic Algorithm
Monte Calo
Reinforcement Learning
Generative Adversarial Networks
Part II Anomaly Detection technique
Type of Anomaly
RNN
Historical
DB-SCAN
Time Shift Detection
Text Pattern Anomaly Detection
A review of machine learning based anomaly detectionMohamed Elfadly
Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Anomaly detection refers to the problem of finding patterns in data that do not conform to expected behavior. These nonconforming patterns are often referred to as anomalies, outliers, discordant observations, exceptions, aberrations, surprises, peculiarities, or contaminants in different application domains.
It is a basic ppt of pattern recognition using wavelates and contourlets.... I will describe the algo into next slide... Thank you... It is a good ppt you can learn the basic of this project
* Logistic regression, logistic loss (log loss)
* stochastic optimization
* adding new features, generalized linear model
* Kernel trick, intro to SVM
* Overfitting
* Decision trees for classification and regression
* Building trees greedily: Gini index, entropy
* Trees fighting with overfitting: pre-stopping and post-pruning
* Feature importances
Progress of Machine Learning in the Field of Intrusion Detection Systemsijcisjournal
With the growth in the use of the Internet and local area networks, malicious attacks and intrusions into
computer systems are increasing. Implementing intrusion detection systems have become extremely
important to help maintain good network security. Support vector machines (SVMs), a classic pattern
recognition tool, have been widely used in intrusion detection. They can handle very large data with high
efficiency, are easy to use, and exhibit good prediction behavior. This paper presents a new SVM model
enriched with a Gaussian kernel function based on the features of the training data for intrusion detection.
The new model is tested with the CICIDS2017 dataset. The test proves better results in terms of detection
efficiency and false alarm rate, which can give better coverage and make detection more efficient.
Minkowski Distance based Feature Selection Algorithm for Effective Intrusion ...IJMER
Intrusion Detection System (IDS) plays a major role in the provision of effective security to various types of networks. Moreover, Intrusion Detection System for networks need appropriate rule set for classifying network bench mark data into normal or attack patterns. Generally, each dataset is characterized by a large set of features. However, all these features will not be relevant or fully contribute in identifying an attack. Since different attacks need various subsets to provide better detection accuracy. In this paper an improved feature selection algorithm is proposed to identify the most appropriate subset of features for detecting a certain attacks. This proposed method is based on Minkowski distance feature ranking and an improved exhaustive search that selects a better combination of features. This system has been evaluated using the KDD CUP 1999 dataset and also with EMSVM [1] classifier. The experimental results show that the proposed system provides high classification accuracy and low false alarm rate when applied on the reduced feature subsets
Comparative Performance Analysis of Machine Learning Techniques for Software ...csandit
Machine learning techniques can be used to analyse data from different perspectives and enable
developers to retrieve useful information. Machine learning techniques are proven to be useful
in terms of software bug prediction. In this paper, a comparative performance analysis of
different machine learning techniques is explored for software bug prediction on public
available data sets. Results showed most of the machine learning methods performed well on
software bug datasets.
A critical review on Adversarial Attacks on Intrusion Detection Systems: Must...PhD Assistance
The present article helps the USA, the UK, Europe and the Australian students pursuing their computer Science postgraduate degree to identify right topic in the area of computer science specifically on deep learning, adversarial attacks and intrusion detection system. These topics are researched in-depth at the University of Spain, Cornell University, University of Modena and Reggio Emilia, Modena, Italy, and many more
http://www.phdassistance.com/industries/computer-science-information/
PhD Assistance offers UK Dissertation Research Topics Services in Computer Science Engineering Domain. When you Order Computer Science Dissertation Services at PhD Assistance, we promise you the following – Plagiarism free, Always on Time, outstanding customer support, written to Standard, Unlimited Revisions support and High-quality Subject Matter Experts http://www.phdassistance.com/services/phd-literature-review/gap-identification/
For Any Queries : Website: www.phdassistance.com
Phd Research Lab : www.research.phdassistance.com
Email: info@phdassistance.com
Phone : +91-4448137070
Contact Name Ganesh / Vinoth Kumar
Part I Machine learning technique
Introduction to Machine Learning
Genetic Algorithm
Monte Calo
Reinforcement Learning
Generative Adversarial Networks
Part II Anomaly Detection technique
Type of Anomaly
RNN
Historical
DB-SCAN
Time Shift Detection
Text Pattern Anomaly Detection
A review of machine learning based anomaly detectionMohamed Elfadly
Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Anomaly detection refers to the problem of finding patterns in data that do not conform to expected behavior. These nonconforming patterns are often referred to as anomalies, outliers, discordant observations, exceptions, aberrations, surprises, peculiarities, or contaminants in different application domains.
It is a basic ppt of pattern recognition using wavelates and contourlets.... I will describe the algo into next slide... Thank you... It is a good ppt you can learn the basic of this project
* Logistic regression, logistic loss (log loss)
* stochastic optimization
* adding new features, generalized linear model
* Kernel trick, intro to SVM
* Overfitting
* Decision trees for classification and regression
* Building trees greedily: Gini index, entropy
* Trees fighting with overfitting: pre-stopping and post-pruning
* Feature importances
Osteoarthritis (OA) is the most common form of arthritis seen in aged or older populations. It is caused
because of a degeneration of articular cartilage, which functions as shock absorption cushion in knee joint. OA
also leads sliding of bones together, cause swelling, pain, eventually and loss of motion. Nowadays, magnetic
resonance imaging (MRI) technique is widely used in the progression of osteoarthritis diagnosis due to the ability
to display the contrast between bone and cartilage. Usually, analysis of MRI image is done manually by a
physician which is very unpredictable, subjective and time consuming. Hence, there is need to develop automated
system to reduce the processing time. In this paper, a new automatic knee OA detection system based on feature
extraction and artificial neural network is developed. The different features viz GLCM texture, statistical, shape
etc. is extracted by using different image processing algorithms. This detection system consists of 4 stages, which
are pre-processing with ROI cropping, segmentation, feature extraction, and classification by neural network. This
technique results 98.5% of classification accuracy at training stage and 92% at testing stage.
Keywords — Artificial Neural Network (ANN), Gray Level Co-occurrence Matrix (GLCM),Knee
Joint, Magnetic Resonance Imaging (MRI), Osteoarthritis(OA).
This presentation gives introductory information regarding whar is comparative studies, what and how to compare along with case study on Comparative studies.
Artificial Neural Network in a Tic Tac Toe Symfony Console Application - Symf...aferrandini
Among all the C libraries bindings PHP offers, there is one for FANN: Fast Artificial Neural Network (libfann). With it you can easily create a neural Network with different activation functions for each neuron/ layer. ANNs (Artificial Neural Networks) are used for machine learning and for recommendation systems. In this Talk we will show an implementation (and running demo) of a basic IA that will learn to play Tic Tac Toe leveraging the Symfony console componente as UI While we demo, we will show a detailed log of what is going on.
SymfonyCon Madrid 2014
AN ANN APPROACH FOR NETWORK INTRUSION DETECTION USING ENTROPY BASED FEATURE S...IJNSA Journal
With the increase in Internet users the number of malicious users are also growing day-by-day posing a serious problem in distinguishing between normal and abnormal behavior of users in the network. This has led to the research area of intrusion detection which essentially analyzes the network traffic and tries to determine normal and abnormal patterns of behavior.In this paper, we have analyzed the standard NSL-KDD intrusion dataset using some neural network based techniques for predicting possible intrusions. Four most effective classification methods, namely, Radial Basis Function Network, SelfOrganizing Map, Sequential Minimal Optimization, and Projective Adaptive Resonance Theory have been applied. In order to enhance the performance of the classifiers, three entropy based feature selection methods have been applied as preprocessing of data. Performances of different combinations of classifiers and attribute reduction methods have also been compared.
With the increase in Internet users the number of malicious users are also growing day-by-day posing a
serious problem in distinguishing between normal and abnormal behavior of users in the network. This
has led to the research area of intrusion detection which essentially analyzes the network traffic and tries
to determine normal and abnormal patterns of behavior.In this paper, we have analyzed the standard
NSL-KDD intrusion dataset using some neural network based techniques for predicting possible
intrusions. Four most effective classification methods, namely, Radial Basis Function Network, Self-
Organizing Map, Sequential Minimal Optimization, and Projective Adaptive Resonance Theory have been
applied. In order to enhance the performance of the classifiers, three entropy based feature selection
methods have been applied as preprocessing of data. Performances of different combinations of classifiers
and attribute reduction methods have also been compared.
Intelligent Handwritten Digit Recognition using Artificial Neural NetworkIJERA Editor
The aim of this paper is to implement a Multilayer Perceptron (MLP) Neural Network to recognize and predict handwritten digits from 0 to 9. A dataset of 5000 samples were obtained from MNIST. The dataset was trained using gradient descent back-propagation algorithm and further tested using the feed-forward algorithm. The system performance is observed by varying the number of hidden units and the number of iterations. The performance was thereafter compared to obtain the network with the optimal parameters. The proposed system predicts the handwritten digits with an overall accuracy of 99.32%.
ANALYSIS OF MACHINE LEARNING ALGORITHMS WITH FEATURE SELECTION FOR INTRUSION ...IJNSA Journal
In recent times, various machine learning classifiers are used to improve network intrusion detection. The researchers have proposed many solutions for intrusion detection in the literature. The machine learning classifiers are trained on older datasets for intrusion detection, which limits their detection accuracy. So, there is a need to train the machine learning classifiers on the latest dataset. In this paper, UNSW-NB15, the latest dataset is used to train machine learning classifiers. The selected classifiers such as K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD), Random Forest (RF), Logistic Regression (LR), and Naïve Bayes (NB) classifiers are used for training from the taxonomy of classifiers based on lazy and eager learners. In this paper, Chi-Square, a filter-based feature selection technique, is applied to the UNSW-NB15 dataset to reduce the irrelevant and redundant features. The performance of classifiers is measured in terms of Accuracy, Mean Squared Error (MSE), Precision, Recall, F1-Score, True Positive Rate (TPR) and False Positive Rate (FPR) with or without feature selection technique and comparative analysis of these machine learning classifiers is carried out.
Abstract—Classical machine learning techniques have been employed severally in intrusion detection. But due to the rising cases and sophistication of attacks, more advanced machine learning techniques including ensemble-based methods, neural networks and deep learning techniques have been applied. However, there is still need for improved machine learning approach to detect attacks more effectively and efficiently. Stacked generalization approach has been shown to be capable of learning from features and meta-features but has been limited by the deficiencies of base classifiers and lack of optimization in the choice of meta-feature combination. This paper therefore proposes a stacked generalization ensemble approach based on two-tier meta-learner, in which the outputs of classical stacked ensemble are passed to multi-feature-based stacked ensemble, which is optimized. A Grid-search approach is used for the optimization. Nine data features and four meta-features derived from Logistic Regression, Support Vector Machine, Naïve Bayes, and Multilayer Perceptron neural network are used for the machine learning classification task. By applying neural networks as the meta-learner for the classification of NSL-KDD data, improved performances in terms of accuracy, precision, recall and F-measure of 0.97, 0.98, 0.98 and 0.98, respectively are achieved.
International Journal of Computer Science and Information Security,IJCSIS ISSN 1947-5500, Pittsburgh, PA, USA
Email: ijcsiseditor@gmail.com
http://sites.google.com/site/ijcsis/
https://google.academia.edu/JournalofComputerScience
https://www.linkedin.com/in/ijcsis-research-publications-8b916516/
http://www.researcherid.com/rid/E-1319-2016
A novel ensemble modeling for intrusion detection system IJECEIAES
Vast increase in data through internet services has made computer systems more vulnerable and difficult to protect from malicious attacks. Intrusion detection systems (IDSs) must be more potent in monitoring intrusions. Therefore an effectual Intrusion Detection system architecture is built which employs a facile classification model and generates low false alarm rates and high accuracy. Noticeably, IDS endure enormous amounts of data traffic that contain redundant and irrelevant features, which affect the performance of the IDS negatively. Despite good feature selection approaches leads to a reduction of unrelated and redundant features and attain better classification accuracy in IDS. This paper proposes a novel ensemble model for IDS based on two algorithms Fuzzy Ensemble Feature selection (FEFS) and Fusion of Multiple Classifier (FMC). FEFS is a unification of five feature scores. These scores are obtained by using feature-class distance functions. Aggregation is done using fuzzy union operation. On the other hand, the FMC is the fusion of three classifiers. It works based on Ensemble decisive function. Experiments were made on KDD cup 99 data set have shown that our proposed system works superior to well-known methods such as Support Vector Machines (SVMs), K-Nearest Neighbor (KNN) and Artificial Neural Networks (ANNs). Our examinations ensured clearly the prominence of using ensemble methodology for modeling IDSs, and hence our system is robust and efficient.
Exploring and comparing various machine and deep learning technique algorithm...CSITiaesprime
Domain generation algorithm (DGA) is used as the main source of script in different groups of malwares, which generates the domain names of points and will further be used for command-and-control servers. The security measures usually identify the malware but the domain name algorithms will be updating themselves in order to avoid the less efficient older security detection methods. The reason being the older detection methods does not use either the machine learning or deep learning algorithms to detect the DGAs. Thus, the impact of incorporating the machine learning and deep learning techniques to detect the DGA is well discussed. As a result, they can create a huge number of domains to avoid debar and henceforth, block the hackers and zombie systems with the older methods itself. The main purpose of this research work is to compare and analyse by implementing various machine learning algorithms that suits the respective dataset yielding better results. In this research paper, the obtained dataset is pre-processed and the respective data is processed by different machine learning algorithms such as random forest (RF), support vector machine (SVM), Naive Bayes classifier, H20 AutoML, convolutional neural network (CNN), long short-term memory neural network (LSTM) for the classification. It is observed and understood that the LSTM provides a better classification efficiency of 98% and the H20 AutoML method giving the least efficiency of 75%.
A Defect Prediction Model for Software Product based on ANFISIJSRD
Artificial intelligence techniques are day by day getting involvement in all the classification and prediction based process like environmental monitoring, stock exchange conditions, biomedical diagnosis, software engineering etc. However still there are yet to be simplify the challenges of selecting training criteria for design of artificial intelligence models used for prediction of results. This work focus on the defect prediction mechanism development using software metric data of KC1.We have taken subtractive clustering approach for generation of fuzzy inference system (FIS).The FIS rules are generated at different radius of influence of input attribute vectors and the developed rules are further modified by ANFIS technique to obtain the prediction of number of defects in software project using fuzzy logic system.
A Defect Prediction Model for Software Product based on ANFISIJSRD
Artificial intelligence techniques are day by day getting involvement in all the classification and prediction based process like environmental monitoring, stock exchange conditions, biomedical diagnosis, software engineering etc. However still there are yet to be simplify the challenges of selecting training criteria for design of artificial intelligence models used for prediction of results. This work focus on the defect prediction mechanism development using software metric data of KC1.We have taken subtractive clustering approach for generation of fuzzy inference system (FIS).The FIS rules are generated at different radius of influence of input attribute vectors and the developed rules are further modified by ANFIS technique to obtain the prediction of number of defects in software project using fuzzy logic system.
11421ijcPROGRESS OF MACHINE LEARNING IN THE FIELD OF INTRUSION DETECTION SYST...ijcisjournal
With the growth in the use of the Internet and local area networks, malicious attacks and intrusions into computer systems are increasing. Implementing intrusion detection systems have become extremely important to help maintain good network security. Support vector machines (SVMs), a classic pattern recognition tool, have been widely used in intrusion detection. They can handle very large data with high efficiency, are easy to use, and exhibit good prediction behavior. This paper presents a new SVM model enriched with a Gaussian kernel function based on the features of the training data for intrusion detection. The new model is tested with the CICIDS2017 dataset. The test proves better results in terms of detection efficiency and false alarm rate, which can give better coverage and make detection more efficient.
COMPUTER INTRUSION DETECTION BY TWOOBJECTIVE FUZZY GENETIC ALGORITHMcscpconf
The purpose of this paper is to describe two objective fuzzy genetics-based learning algorithms
and discusses its usage to detect intrusion in a computer network. Experiments were performed
with KDD-cup data set, which have information on computer networks, during normal behavior
and intrusive behavior. The performance of final fuzzy classification system has been
investigated using intrusion detection problem as a high dimensional classification problem.
This task is formulated as optimization problem with two objectives: To minimize the number of
fuzzy rules and to maximize the classification rate. We show a two-objective genetic algorithm
for finding non-dominated solutions of the fuzzy rule selection problem
ANALYSIS AND COMPARISON STUDY OF DATA MINING ALGORITHMS USING RAPIDMINERIJCSEA Journal
Comparison study of algorithms is very much required before implementing them for the needs of any
organization. The comparisons of algorithms are depending on the various parameters such as data
frequency, types of data and relationship among the attributes in a given data set. There are number of
learning and classifications algorithms are used to analyse, learn patterns and categorize data are
available. But the problem is the one to find the best algorithm according to the problem and desired
output. The desired result has always been higher accuracy in predicting future values or events from the
given dataset. Algorithms taken for the comparisons study are Neural net, SVM, Naïve Bayes, BFT and
Decision stump. These top algorithms are most influential data mining algorithms in the research
community. These algorithms have been considered and mostly used in the field of knowledge discovery
and data mining.
ANALYSIS AND COMPARISON STUDY OF DATA MINING ALGORITHMS USING RAPIDMINER
My
1. 1
Comparison of Genetic Algorithm Optimization on
Artificial Neural Network and Support Vector Machine
Case Study : Intrusion Detection System
Presented by : Amin Dastanpour
PhD Candidate of Network Security
Advanced Informatics School, University Technology Malaysia, Kuala lumpur
2. 2
Table of Content
Introduction Slide 3
Problem of IDS Slide 4
Solution Slide 5
Related Work Slide 6
Artificial Neural Network Slide 7
Support Vector Machine Slide 8
Genetic Algorithm Slide 9
Methodology Slide 10
Data Set Slide 11
Result Slide 12
Conclusion Slide 15
4. Problem of IDS
It is only capable of detecting the known attacks
and there should be a frequent update for the
attacks.
Network traffic that needs to be dealt with is very
large and the data distribution is highly
imbalanced.
4
5. Solution
Machine learning is to discover and learn and then
adapt to the situation that might change over
time .
In IDS, algorithms are deployed on the input
attacks that have been previously unseen in order
to perform the actual process of detection.
Recognizing the new attacks.
Numbers of key features and the process of
detection will be optimized.
5
6. Related work
Author Method objective
Bin Luo et
al.
four-angle-star based visualized feature
generation approach, (FASVFG)
evaluate the distance between
samples in a 5-class
classification problem
Abraham et
al.
fuzzy rule based
classifiers
framework for Distributed
Intrusion Detection Systems
(DIDS)
Amiri et al. Forward feature selection algorithm(FFSA)
Liner correlation feature selection (LCFS)
Modified mutual information feature selection
(MMIFS)
Propose a feature selection
phase, which can be generally
implemented on any intrusion
detection
Li et al. Ant colony algorithm and support vector
machine (SVM)
This paper proposes a desirable
IDS model with high efficiency
and accuracy
Dastanpour
et al.
Propose a feature selection based on the
Genetic Algorithm (GA) and Support Vector
Machine (SVM)
Improve detection rate with
the less number of features
Dastanpour
et al.
Applying Genetic Algorithms (GA) with
Artificial Neural Networks (ANN) classifier to
detect the attacks in network
Increase of accuracy with the
optimal number of features
6
7. Artificial Neural Network (ANN)
Artificial Neural Network (ANN) and it has been
used to solve the regression and classification
problems and ability of recognition of the
patterns.
Recognize the new attacks or data from the
previous ones.
Problem Of ANN
The purpose of classification and reorganization, a
large data set is required by the ANN. For
optimizing this data type and making or
generating a feature or pattern.7
8. Support Vector Machine (SVM)
Support vector machine (SVM) used for solving
classification .
non-linear classification.
Problem of SVM
SVM needs a large set of data.
8
9. Genetic Algorithm (GA)
Genetic algorithm is an exploratory and adaptive
algorithm for work and search which has been
base on the natural genetics evolutionary ideas.
GA is capable of proposing a solution in a single
solution with an optimal value.
In this Research use GA to Support ANN and SVM.
9
11. DataSet
Knowledge Discovery and Data Mining (KDD CUP
1999) has been applied.
494,020 single connection vectors each of which
contains 41 features and is labeled with exact one
specific attack type : normal or an attack.
Probing
U2R
R2L
DOS
11
14. COMPARING WITH OTHER ALGORITHM
COMPARATIVE OF GA-ANN AND GA-SVM WITH OTHER
ALGORITHM MENTION ON THE RELATED WORK.
14
Name of algorithm Detection rate Number of Feature
LCFS 100 % 21
FFSA 100 % 31
MMIFS 100 % 24
fuzzy rule based 100 % 41
FASVFG 94 % 20
SVM With GA 100 % 24
ANN with GA 100 % 18
15. Conclusion
In this study GA has been proposed for producing
the detection features. Then the SVM and ANN are
used for the detection system classifier and
comparing with each other to show the
effectiveness of the GA on these methods.
Comparison with the other methods, the highest
detection rate is.
The GA with SVM requires 24 features and GA
with ANN needs 18 for achieving 100% of
detection.
15
16. References
1) F. Amiri, M. Rezaei Yousefi, C. Lucas, A. Shakery, and N. Yazdani, "Mutual information-
based feature selection for intrusion detection systems," Journal of Network and
Computer Applications, vol. 34, pp. 1184-1199, 2011.
2) A. Abraham, R. Jain, J. Thomas, and S. Y. Han, "D-SCIDS: Distributed soft computing
intrusion detection system," Journal of Network and Computer Applications, vol. 30,
pp. 81-98, 2007.
3) A. Dastanpour and R. A. R. Mahmood, "Feature Selection Based on Genetic Algorithm
and SupportVector Machine for Intrusion Detection System," in The Second
International Conference on Informatics Engineering & Information Science
(ICIEIS2013), 2013, pp. 169-181.
4) A. Dastanpour, S. Ibrahim, and R. Mashinchi, "Using Genetic Algorithm to Supporting
Artificial Neural Network for Intrusion Detection System," in The International
Conference on Computer Security and Digital Investigation (ComSec2014), 2014, pp.
1-13.
5) …
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Presented by : Amin Dastanpour
PhD Candidate of Network Security
Advanced Informatics School, University Technology Malaysia, Kuala lumpur