Securing Software Defined Networking Using Intrusion Detection System - A Review

Al-Mi’ani, Noor; Anbar, Mohammed; Sanjalawe, Yousef K.; Karuppayah, Shankar

doi:10.1007/978-981-16-8059-5_26

Communications in Computer and Information Science

2021

DOI: 10.1007/978-981-16-8059-5_26

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Securing Software Defined Networking Using Intrusion Detection System - A Review

Noor Al-Mi’ani

Mohammed Anbar

Yousef K. Sanjalawe

et al.

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2024

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Cited by 2 publications

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“…host intrusion detection systems (HIDSs) that monitor hosts for malicious actions [19]. These systems are anomaly-based, detecting unusual behavior, or misuse-based, looking for known attack patterns.…”

mentioning

confidence: 99%

An efficient intrusion detection systems in fog computing using forward selection and BiLSTM

Abu Zwayed,

Anbar,

Manickam

et al. 2024

Bulletin EEI

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Intrusion detection systems (IDS) play a pivotal role in network security and anomaly detection and are significantly impacted by the feature selection (FS) process. As a significant task in machine learning and data analysis, FS is directed toward pinpointing a subset of pertinent features that primarily influence the target variable. This paper proposes an innovative approach to FS, leveraging the forward selection search algorithm with hybrid objective/fitness functions such as correlation, entropy, and variance. The approach is evaluated using the BoT-IoT and TON_IoT datasets. By employing the proposed methodology, our bidirectional long-short term memory (BiLSTM) model achieved an accuracy of 98.42% on the TON_IoT dataset and 98.7% on the BoT-IoT dataset. This superior classification accuracy underscores the efficacy of the synergized BiLSTM deep learning model and the innovative FS approach. The study accentuates the potency of the proposed hybrid approach in FS for IDS and highlights its substantial contribution to achieving high classification performance in internet of things (IoT) network traffic analysis.

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mentioning

confidence: 99%

An efficient intrusion detection systems in fog computing using forward selection and BiLSTM

Abu Zwayed,

Anbar,

Manickam

et al. 2024

Bulletin EEI

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A comprehensive analysis of threat vectors in software-defined networking

Dahiya,

Sehrawat,

Kharb

et al. 2024

Multimed Tools Appl

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Securing Software Defined Networking Using Intrusion Detection System - A Review

Cited by 2 publications

References 86 publications

An efficient intrusion detection systems in fog computing using forward selection and BiLSTM

An efficient intrusion detection systems in fog computing using forward selection and BiLSTM

A comprehensive analysis of threat vectors in software-defined networking

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