A lightweight intelligent network intrusion detection system using OCSVM and Pigeon inspired optimizer

Alazzam, Hadeel; Sharieh, Ahmad; Sabri, Khair Eddin

doi:10.1007/s10489-021-02621-x

Cited by 29 publications

(13 citation statements)

References 49 publications

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“…From [54] GA-RF 0.921 ± 0.000 NA 0.016 ± 0.0000 NA From [55] IGRF-RFE 0.842 ± 0.000 0.0842 NA 0.829 ± 0.000 From [10] PIO 0.913 ± 0.0003 0.897 ± 0.0002 0.052 ± 0.0004 0.904 ± 0.0002 From [56] Rule-Based 0.652 ± 0.000 0.903 ± 0.000 0.02 ± 0.000 0.681 ± 0.000 From [57] Wrapper-Based-DT 0.864 ± 0.000 0.97 ± 0.000 0.028 ± 0.000 NA The proposed model with HC PHFS-IWDHC 4 [4,10,11,6] From [54] GA-RF 9 [27,3,41,35,36,10,31,2,18] From [55] IGRF-RFE 23 [0, 1,2,3,4,5,6,7,8,9,10,12,14,15,16,18,23,24,25,26,27,31,35] From [10] PIO 14 [3,8,9,11,12,…”

Section: Unsw-nb15 Resultsmentioning

confidence: 99%

“…Overall, the proposed methods provide competitive results depending on accuracy, FPR, F-score, and TPR, compared to some of the most recent methods from the literature using the least number of features, as shown in Table 5. From [10] PIO 18 [1,3,4,5,6,8,10,11,12,13,14,15,17,18,27,32,36,39,41] From [60] PSO 37 [2,3,4,5,6,7,8,9,10,11,12,13,14,15,17,18,20,21,22,23,24,25,26,27,28,29,31,32,…”

Section: Unsw-nb15 Resultsmentioning

confidence: 99%

“…Since society is becoming more technologically reliant than ever before, cybersecurity is one of the key research topics nowadays [1]. Cybersecurity represents the process of defending networks, computers, servers, mobiles, devices, electronic systems, and data from malicious attacks, and this defending can be achieved using many security solutions such as cryptography [2,3], firewalls, authentication techniques [4], prevention systems, or intrusion detection systems (henceforth IDS) [5]. IDS is a software application designed for detecting malicious content through monitoring network traffic activities to report suspicious network activity [6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hybrid Feature Selection Method for Intrusion Detection Systems Based on an Improved Intelligent Water Drop Algorithm

Alhenawi

Alazzam

Al-Sayyed

et al. 2022

Cybernetics and Information Technologies

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A critical task and a competitive research area is to secure networks against attacks. One of the most popular security solutions is Intrusion Detection Systems (IDS). Machine learning has been recently used by researchers to develop high performance IDS. One of the main challenges in developing intelligent IDS is Feature Selection (FS). In this manuscript, a hybrid FS for the IDS network is proposed based on an ensemble filter, and an improved Intelligent Water Drop (IWD) wrapper. The Improved version from IWD algorithm uses local search algorithm as an extra operator to increase the exploiting capability of the basic IWD algorithm. Experimental results on three benchmark datasets “UNSW-NB15”, “NLS-KDD”, and “KDDCUPP99” demonstrate the effectiveness of the proposed model for IDS versus some of the most recent IDS algorithms existing in the literature depending on “F-score”, “accuracy”, “FPR”, “TPR” and “the number of selected features” metrics.

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Section: Unsw-nb15 Resultsmentioning

confidence: 99%

Section: Unsw-nb15 Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid Feature Selection Method for Intrusion Detection Systems Based on an Improved Intelligent Water Drop Algorithm

Alhenawi

Alazzam

Al-Sayyed

et al. 2022

Cybernetics and Information Technologies

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“…The authors in [41] introduced an IDS, characterized by a low FAR and simultaneously high Detection Rate (DR). Their system employs a binary Pigeon Inspired Optimizer (PIO) to select highly important features.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparative Analysis of Intrusion Detection Systems: Leveraging Classification Algorithms and Feature Selection Techniques

Shakir,

Mohsin

2024

JASTT

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With the increasing use of the Internet and its coverage of all areas of life and the increasing amount of sensitive and confidential information on the Internet, the number of malicious attacks on that information has increased with the aim of destroying, changing, or misusing it. Consequently, the need to discover and prevent these kinds of attacks has increased in order to maintain privacy, reliability, and even availability. For this purpose, intelligent systems have been developed to detect these attacks, which are called Intrusion Detection System (IDS). These systems were tested and applied to special benchmark datasets that contain a large number of features and a massive number of observations. However, not all the features are important, and some are not relevant. Therefore, applying feature selection techniques becomes crucial, which select the features with the most importance and relevance in order to enhance the performance of the classification model. The aim of this review paper is to conduct a comparative analysis of various state-of-the-art IDS that use algorithm classifications to detect network attacks with the cooperation of feature selection techniques that have been applied to various well-known IDS datasets, such as KDD cup99, NSL-KDD, etc. This comparison is based on several factors, including the utilized classification technique, feature selection used, employed evaluation metrics, datasets used, and finally the highest accuracy rate obtained by each study.

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“…In [14], the authors presented lightweight NIDS using two simultaneous OCSVMbased subsystems. One of the systems has been taught to handle regular packets, and the other to handle attack packets.…”

Section: Literature Reviewmentioning

confidence: 99%

Effective One-Class Classifier Model for Memory Dump Malware Detection

Al–Qudah¹,

Ashi²,

Alnabhan³

et al. 2022

Preprint

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Malware complexity is rapidly increasing, causing catastrophic impacts on computer systems. Memory dump malware is gaining increased attention due to its ability to expose plaintext passwords or key encryption files. This paper presents an enhanced classification model based on One class SVM (OCSVM) classifier that can identify any deviation from the normal memory dump file patterns and detect it as malware. The proposed model integrates OCSVM and Principal Component Analysis (PCA) for increased model sensitivity and efficiency. An up-to-date dataset known as “MALMEMANALYSIS-2022” was utilized during the evaluation phase of this study. The accuracy achieved by the traditional one-class classification (TOCC) model was 55%, compared to 99.4% in the one-class classification with PCA (OCC-PCA) model. Such results have confirmed the increased performance achieved by the proposed model.

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A lightweight intelligent network intrusion detection system using OCSVM and Pigeon inspired optimizer

Cited by 29 publications

References 49 publications

Hybrid Feature Selection Method for Intrusion Detection Systems Based on an Improved Intelligent Water Drop Algorithm

Hybrid Feature Selection Method for Intrusion Detection Systems Based on an Improved Intelligent Water Drop Algorithm

A Comparative Analysis of Intrusion Detection Systems: Leveraging Classification Algorithms and Feature Selection Techniques

Effective One-Class Classifier Model for Memory Dump Malware Detection

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