Model for Intrusion Detection Based on Hybrid Feature Selection Techniques

Chahira, Joseph Mbugua

doi:10.7753/ijcatr0903.1005

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…The main aim of feature selection is to eliminates irrelevant and repetitive features from the dataset to make robust, efficient, accurate and lightweight intrusion detection system To achieve this objective, a model for network intrusion detection system based on Multi-Filter Feature Selection (EMFFS) Method is implemented developed by [14] to find the best set of features that are used in this work. The feature selection techniques integrated are Correlation Feature Selection (CFS) based evaluator with Best-first searching method, Information Gain (IG)…”

Section: Ensemble-based Multi-filter Feature Selection (Emffs) Methodsmentioning

confidence: 99%

Model for Enhancing Performance of Network Intrusion Detection based on Hybrid Feature Selection and Unsupervised Learning Techniques

Chahira¹,

Kiruki²

2022

IJCATR

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As security threats change and advance in a drastic way, relevant of the organizations implement multiple Network Intrusion Detection Systems (NIDSs) to optimize detection and to provide comprehensive view of intrusion activities. But NIDSs trigger a massive amount of alerts even for a day and overwhelmed security experts as they require high levels of human involvement in creating the system and/or maintaining it. The main goal in this work is to enhances the structural based alert correlation model to improve the quality of alerts and detection capability of NIDS by grouping alerts with common attributes based on unsupervised learning techniques. This work compares four unsupervised learning algorithms namely Self-organizing maps (SOM), K-means, Expectation and Maximization (EM) and Fuzzy C-means (FCM) to select the best cluster algorithm based on Clustering Accuracy Rate (CAR), Clustering Error (CE) and processing time. The result inferred that the proposed model based on hybrid feature selection, PCA and EM is effective in terms of Clustering Accuracy Rate (CAR) and processing time for The NSL-KDD Dataset

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Section: Ensemble-based Multi-filter Feature Selection (Emffs) Methodsmentioning

confidence: 99%

Model for Enhancing Performance of Network Intrusion Detection based on Hybrid Feature Selection and Unsupervised Learning Techniques

Chahira¹,

Kiruki²

2022

IJCATR

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“…Traffic attributes are calculated based on the previous connections. Traffic attributes are grouped into (1) Time-based traffic features and (2) Hostbased (machine) traffic features (Chahira, 2020). The summary of the features is given in Table 1.…”

Section: A Kdd99/kdd Cup 99 Datasetmentioning

confidence: 99%

Cyber Security Threats and Countermeasures using Machine and Deep Learning Approaches: A Survey

Manjula¹,

Venkatesh²,

Venugopal³

2023

Journal of Computer Science

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Recent advancements in e-business, e-healthcare, e-governance, and online digital transactions have brought valuable benefits. Unfortunately, it raises severe cyber-attacks. Cyberattacks disrupt normal operations, try to retrieve confidential information and defense secrets, and subvert the nation's defense systems and Internet-connected devices. Cyber security solutions are required to detect, analyze, defend against threats and protect sensitive data from unauthorized access. This study gives a detailed survey of different cybersecurity attacks, like Denial-of-service attacks, Botnet Evasion Attacks, Malware invasions, Spam and phishing invasion, Spoofing, Domain Generation algorithms, Probing attacks, R2L, and U2R attacks. This research review emphasizes Machine Learning and Deep Learning-based approaches to Cybersecurity problems. This study's key highlights are the research challenges, cybersecurity issues, cyber security domains, and tools for the Intrusion detection system. Data sets play a vital role in cybersecurity research; hence, Private and Publically available datasets are reviewed in this study. Various performance matrices are discussed in this survey which can be used to evaluate the effectiveness of cybersecurity solutions.

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“…A new feature selection model (Chahira, 2020)proposed is based on hybrid feature selection techniques (information gain, correlation, chi squere and gain ratio) and Principal Component Analysis (PCA) for feature reduction. This study employed data mining and machine learning techniques on NSL KDD dataset in order to explore significant features in detecting network intrusions.…”

Section: Introductionmentioning

confidence: 99%

“…Protocal_type, service, attck. Detailed experiment process and results are disscussed in(Chahira, 2020) Step2 Enhanced Structural-Based Alert Correlation Method The detection component of NIDSs generates a massive amount of alerts and can overwhelm the security experts. An automated and intelligent clustering system is important to reveal their structural correlation by grouping alerts with common attributes.…”

mentioning

confidence: 99%

Alert Correlation Model Based on Hybrid Machine Learning Techniques to Enhance the Performance of NIDS

2023

IJCATR

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There obstacles in developing an effective intrusion detection systemin this modern digital world. This work proposes a three level model in developingNIDS that offers multiple types of correlations. In the first level, several feature selection techniques are integrated existing feature selection techniques Correlation Feature Selection, Information Gain and Chi square to find the best set of features used in this work. The second level enhances the structural based alert correlation model based on Expectation and Maximization (EM) to improve the quality of alerts and detection capability by grouping alerts with common attributes. Then an anomaly classification module is designed in the third level based on fusion of five heterogeneous classifiers Support Vector Machine (SVM), Instance based Learners (IBL), Random Forest, J48, and Bayes Net using Voting as a Multi-Classifier.The NSL KDD dataset is used in this experiment. The overall detection rate is 99.9%, false error rate 0.1% and execution rate of 1340.7 seconds. This shows that HAC is effective and practical in providing complete correlation even on high dimensionality, large scaled and low quality dataset used in intrusion detection system.

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Model for Intrusion Detection Based on Hybrid Feature Selection Techniques

Cited by 4 publications

References 16 publications

Model for Enhancing Performance of Network Intrusion Detection based on Hybrid Feature Selection and Unsupervised Learning Techniques

Model for Enhancing Performance of Network Intrusion Detection based on Hybrid Feature Selection and Unsupervised Learning Techniques

Cyber Security Threats and Countermeasures using Machine and Deep Learning Approaches: A Survey

Alert Correlation Model Based on Hybrid Machine Learning Techniques to Enhance the Performance of NIDS

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