scholarly journals Artificial Intelligence for Creating Low Latency and Predictive Intrusion Detection with Security Enhancement in Power Systems

2021 ◽  
Vol 11 (24) ◽  
pp. 11988
Author(s):  
Robin Singh Bhadoria ◽  
Naman Bhoj ◽  
Hatim G. Zaini ◽  
Vivek Bisht ◽  
Md. Manzar Nezami ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Random Forest ◽  
Intrusion Detection ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Low Latency ◽  
Machine Model ◽  
Time Accuracy

Advancement in network technology has vastly increased the usage of the Internet. Consequently, there has been a rise in traffic volume and data sharing. This has made securing a network from sophisticated intrusion attacks very important to preserve users’ information and privacy. Our research focuses on combating and detecting intrusion attacks and preserving the integrity of online systems. In our research we first create a benchmark model for detecting intrusions and then employ various combinations of feature selection techniques based upon ensemble machine learning algorithms to improve the performance of the intrusion detection system. The performance of our model was investigated using three evaluation metrics namely: elimination time, accuracy and F1-score. The results of the experiment indicated that the random forest feature selection technique had the minimum elimination time, whereas the support vector machine model had the best accuracy and F1-score. Therefore, conclusive evidence could be drawn that the combination of random forest and support vector machine is suitable for low latency and highly accurate intrusion detection systems.

Intrusion Detection System Based on Hybrid Feature Selection and Support Vector Machine (HFS-SVM)

2015 ◽  
Vol 781 ◽  
pp. 125-128 ◽  
Author(s):  
Yonchanok Khaokaew ◽  
Tanapat Anusas-Amornkul ◽  
Koonlachat Meesublak
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Motif Discovery ◽  
Detection System ◽  
Random Projection ◽  
Support Vector ◽  
Training Time ◽  
Detection Techniques ◽  
Projection Techniques

In recent years, anomaly based intrusion detection techniques are continuously developed and a support vector machine (SVM) is one of the technique. However, it requires training time and storage if there are lots of numbers of features. In this paper, a hybrid feature selection, using Correlation based on Feature Selection and Motif Discovery using Random Projection techniques, is proposed to reduce the number of features from 41 to 3 features with KDD'99 dataset. It is compared with a regular SVM technique with 41 features. The results show that the accuracy rate is also high at 98% and the training time is less than the regular SVM almost by half.

scholarly journals Intelligent Intrusion Detection System Through Combined and Optimized Machine Learning

2018 ◽  
Vol 17 (02) ◽  
pp. 1850007 ◽  
Author(s):  
Syed Ali Raza Shah ◽  
Biju Issac ◽  
Seibu Mary Jacob
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Fuzzy Logic ◽  
Intrusion Detection ◽  
Open Source Software ◽  
Intrusion Detection System ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Rule Based

In this paper, an existing rule-based intrusion detection system (IDS) is made more intelligent through the application of machine learning. Snort was chosen as it is an open source software and though it was performing well, it showed false positives (FPs). To find the best performing machine learning algorithms (MLAs) to use with Snort so as to improve its detection, we tested some algorithms on three available datasets. Support vector machine (SVM) was chosen along with fuzzy logic and decision tree based on their accuracy. Combined versions of algorithms through ensemble SVM along with other variants were tried on the generated traffic of normal and malicious packets at 10[Formula: see text]Gbps. Optimized versions of the SVM along with firefly and ant colony optimization (ACO) were also tried, and the accuracy improved remarkably. Thus, the application of combined and optimized MLAs to Snort at 10[Formula: see text]Gbps worked quite well.

A new ensemble based approach for intrusion detection system using voting

2021 ◽  
pp. 1-11
Author(s):  
Nitesh Singh Bhati ◽  
Manju Khari
Keyword(s):  
Support Vector Machine ◽  
Intrusion Detection ◽  
Detection System ◽  
Severe Form ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Detection Systems ◽  
Confidential Data ◽  
Effective System ◽  
Better Than

With the increase in the amount of data available today, the responsibility of keeping that data safe has also taken a more severe form. To prevent confidential data from getting in the hands of an attacker, some measures need to be taken. Here comes the need for an effective system, which can classify the traffic as an attack or normal. Intrusion Detection Systems can do this work with perfection. Many machine learning algorithms are used to develop efficient IDS. These IDS provide remarkable results. However, ensemble-based IDS using voting have been seen to outperform individual approaches (Support Vector Machine and ExtraTree). Since the Voting methodology is able to work around both, theoretically similar and different classifiers and produce a single classifier based on the majority characteristics, it proved to be better than the other ensemble based techniques. In this paper, an ensemble IDS implementation is presented based on the voting ensemble method, using the two algorithms, Support Vector Machine (SVC) and ExtraTree. The experiment is performed on the KDDCup99 Dataset. The evaluation of the performance of the proposed method is based on the comparison with an unoptimized implementation of the same. The results based on performing the experiment in Python fetched an accuracy of 99.90%.

scholarly journals Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Mohammad Aljanabi ◽  
Mohd Arfian Ismail ◽  
Vitaly Mezhuyev
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Parameter Tuning ◽  
Feature Subset Selection ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Feature Subset

Many optimisation-based intrusion detection algorithms have been developed and are widely used for intrusion identification. This condition is attributed to the increasing number of audit data features and the decreasing performance of human-based smart intrusion detection systems regarding classification accuracy, false alarm rate, and classification time. Feature selection and classifier parameter tuning are important factors that affect the performance of any intrusion detection system. In this paper, an improved intrusion detection algorithm for multiclass classification was presented and discussed in detail. The proposed method combined the improved teaching-learning-based optimisation (ITLBO) algorithm, improved parallel JAYA (IPJAYA) algorithm, and support vector machine. ITLBO with supervised machine learning (ML) technique was used for feature subset selection (FSS). The selection of the least number of features without causing an effect on the result accuracy in FSS is a multiobjective optimisation problem. This work proposes ITLBO as an FSS mechanism, and its algorithm-specific, parameterless concept (no parameter tuning is required during optimisation) was explored. IPJAYA in this study was used to update the C and gamma parameters of the support vector machine (SVM). Several experiments were performed on the prominent intrusion ML dataset, where significant enhancements were observed with the suggested ITLBO-IPJAYA-SVM algorithm compared with the classical TLBO and JAYA algorithms.

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