Network Based Detection of IoT Attack Using AIS-IDS Model

Sabitha, R.; Gopikrishnan, S.; Bejoy, B. J.; Anusuya, V.; Saravanan, V.

doi:10.1007/s11277-022-10009-4

Cited by 9 publications

(3 citation statements)

References 23 publications

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“…In a similar effort, most of the IDS strategies employed efficient feature selection and optimization techniques to bolster the accuracy of the IDS models. Such feature engineering approaches include HHO and SCA [72], ECSO [83], Deep GB-RBM [26], SMO [31], RSL [30], IG [27,96], FS and BE [27], HOA [31], RF and XGBoost [54], F-PSO [92] and SEA and AE [34]. Equally, to enhance the security and preserve-privacy of IoT data, different techniques have been utilized, such as cryptocurrency and blockchain [30,44,49,52,69,78,84,94,99], cryptography-MGO and DHPEA [78], PUFs(ECDSA) [94], homomorphic encryption [51], routing/two-fish symmetry key [85], authentication [68], access control [36], DP [51,52,82].…”

Section: Discussionmentioning

confidence: 99%

“…BoT-IoT dataset: This dataset was created by simulating a real IoT network in UNSW Canberra's Cyber Range Lab used for testing IoT-focused IDS. It has both normal and botnet traffic with 46 features covering different IoT devices, communication protocols, and attack types such as DDoS, DoS, OS and Service Scan, Keylogging and Data exfiltration [27,28,40,59,67,72,75,92].…”

Section: Benchmark Datasetsmentioning

confidence: 99%

“…Tested with the CICDDoS2019 dataset, this LSTM model surpassed traditional IDS methods, achieving 99.19% accuracy. In a similar effort, Sabitha et al [92] developed an IDS that utilizes Artificial Immune Systems (AIS-IDS) to improve the detection of DoS/DDoS attacks in IoT networks, aiming to increase accuracy and reduce FARs. The AIS-IDS employs Hopfield Neural Networks (HNN) and incorporates danger theory principles with fixed weights for precise classification and response to threats.…”

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confidence: 99%

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Insights into Modern Intrusion Detection Strategies for Internet of Things Ecosystems

Isong,

Kgote,

Abu-Mahfouz

2024

Electronics

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The swift explosion of Internet of Things (IoT) devices has brought about a new era of interconnectivity and ease of use while simultaneously presenting significant security concerns. Intrusion Detection Systems (IDS) play a critical role in the protection of IoT ecosystems against a wide range of cyber threats. Despite research advancements, challenges persist in improving IDS detection accuracy, reducing false positives (FPs), and identifying new types of attacks. This paper presents a comprehensive analysis of recent developments in IoT, shedding light on detection methodologies, threat types, performance metrics, datasets, challenges, and future directions. We systematically analyze the existing literature from 2016 to 2023, focusing on both machine learning (ML) and non-ML IDS strategies involving signature, anomaly, specification, and hybrid models to counteract IoT-specific threats. The findings include the deployment models from edge to cloud computing and evaluating IDS performance based on measures such as accuracy, FP rates, and computational costs, utilizing various IoT benchmark datasets. The study also explores methods to enhance IDS accuracy and efficiency, including feature engineering, optimization, and cutting-edge solutions such as cryptographic and blockchain technologies. Equally, it identifies key challenges such as the resource-constrained nature of IoT devices, scalability, and privacy issues and proposes future research directions to enhance IoT-based IDS and overall ecosystem security.

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