Immune System Based Intrusion Detection System (IS-IDS): A Proposed Model

Dutt, Inadyuti; Borah, Samarjeet; Maitra, Indra Kanta

doi:10.1109/access.2020.2973608

Cited by 49 publications

(24 citation statements)

References 23 publications

(25 reference statements)

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“…Dutt et al [35], proposed a two-layered immune system to monitor the network traffic and identify the intrusion within the network. The first layer of the proposed system was based on Statistical Modeling-based Anomaly Detection (SMAD) which worked as an Innate Immune System capable of detecting the first-hand vulnerabilities inside the network.…”

Section: Discussing the Use Of Conditionally Classified "Distributed" Ai Methods In Cybersecuritymentioning

confidence: 99%

The impacts of artificial intelligence techniques in augmentation of cybersecurity: a comprehensive review

Naik

Mehta

Yagnik³

et al. 2021

Complex Intell. Syst.

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Given the prevailing state of cybersecurity, it is reasonable to understand why cybersecurity experts are seriously considering artificial intelligence as a potential field that can aid improvements in conventional cybersecurity techniques. Various progressions in the field of technology have helped to mitigate some of the issues relating to cybersecurity. These advancements can be manifested by Big Data, Blockchain technology, Behavioral Analytics, to name but a few. The paper overviews the effects of applications of these technologies in cybersecurity. The central purpose of the paper is to review the application of AI techniques in analyzing, detecting, and fighting various cyberattacks. The effects of the implementation of conditionally classified “distributed” AI methods and conveniently classified “compact” AI methods on different cyber threats have been reviewed. Furthermore, the future scope and challenges of using such techniques in cybersecurity, are discussed. Finally, conclusions have been drawn in terms of evaluating the employment of different AI advancements in improving cybersecurity.

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Section: Discussing the Use Of Conditionally Classified "Distributed" Ai Methods In Cybersecuritymentioning

confidence: 99%

The impacts of artificial intelligence techniques in augmentation of cybersecurity: a comprehensive review

Naik

Mehta

Yagnik³

et al. 2021

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…a) IDS: The IDS for smart grids is considered an enduring problem, which is not developed yet. The IDS design for smart grids is a complicated assignment because of the vast size of the grids as well as the heterogeneity of their components [174]. Furthermore, IDS employed for conventional IT systems will not essentially work for the smart grids.…”

Section: Smart Grid Cp Controlsmentioning

confidence: 99%

CPS Attacks Mitigation Approaches on Power Electronic Systems With Security Challenges for Smart Grid Applications: A Review

et al. 2021

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This paper presents an inclusive review of the cyber-physical (CP) attacks, vulnerabilities, mitigation approaches on the power electronics and the security challenges for the smart grid applications. With the rapid evolution of the physical systems in the power electronics applications for interfacing renewable energy sources that incorporate with cyber frameworks, the cyber threats have a critical impact on the smart grid performance. Due to the existence of electronic devices in the smart grid applications, which are interconnected through communication networks, these networks may be subjected to severe cyber-attacks by hackers. If this occurs, the digital controllers can be physically isolated from the control loop. Therefore, the cyber-physical systems (CPSs) in the power electronic systems employed in the smart grid need special treatment and security. In this paper, an overview of the power electronics systems security on the networked smart grid from the CP perception, as well as then emphases on prominent CP attack patterns with substantial influence on the power electronics components operation along with analogous defense solutions. Furthermore, appraisal of the CPS threats attacks mitigation approaches, and encounters along the smart grid applications are discussed. Finally, the paper concludes with upcoming trends and challenges in CP security in the smart grid applications.

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“…The negative Selection algorithm [41,42] is a kind of Artificial Immune System (AIS).AIS, a type of rule-based machine learning system [43,44] and it is inspired by the human immune system. It works similarly to a pattern-based selection.…”

Section: Negative Selection Algorithm (Nsa)mentioning

confidence: 99%

Deep Learning Based Attack Detection in IIoT using Two-Level Intrusion Detection System

Raja

Karthikeyan

Abilash

et al. 2021

Preprint

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The Industrial Internet of Things (IIoT), also known as Industry 4.0, has brought a revolution in the production and manufacturing sectors as it assists in the automation of production management and reduces the manual effort needed in auditing and managing the pieces of machinery. IoT-enabled industries, in general, use sensors, smart meters, and actuators. Most of the time, the data held by these devices is surpassingly sensitive and private. This information might be modified, 1 stolen, or even the devices may be subjected to a Denial of Service (DoS) attack. As a consequence, the product quality may deteriorate or sensitive information may be leaked. An Intrusion Detection System (IDS), implemented in the network layer of IIoT, can detect attacks, thereby protecting the data and devices. Despite substantial advancements in attack detection in IIoT, existing works fail to detect certain attacks obfuscated from detectors resulting in a low detection performance. To address the aforementioned issue, we propose a Deep Learning-based Two Level Network Intrusion Detection System (DLTL-NIDS) for IIoT environment, emphasizing challenging attacks. The attacks that attain low accuracy or low precision in level-1 detection are marked as challenging attacks. Experimental results show that the proposed model, when tested against TON IoT, figures out the challenging attacks well and achieves an accuracy of 99.97%, precision of 95.62%, recall of 99.5%, and F1-score of 99.65%. The proposed DL-TLNIDS, when compared with state-of-art models, achieves a decrease in false alarm rate to 2.34% (flagging normal traffic as an attack) in IIoT.

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Immune System Based Intrusion Detection System (IS-IDS): A Proposed Model

Cited by 49 publications

References 23 publications

The impacts of artificial intelligence techniques in augmentation of cybersecurity: a comprehensive review

The impacts of artificial intelligence techniques in augmentation of cybersecurity: a comprehensive review

CPS Attacks Mitigation Approaches on Power Electronic Systems With Security Challenges for Smart Grid Applications: A Review

Deep Learning Based Attack Detection in IIoT using Two-Level Intrusion Detection System

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