Hybrid intelligent intrusion detection system for internet of things

Jain, Shreeya; Pawar, Pranav M.; Muthalagu, Raja

doi:10.1016/j.teler.2022.100030

Cited by 10 publications

(2 citation statements)

References 15 publications

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“…In [87] A highly extendable DNN model was created for IoT networks; it successfully detected IoT DDoS botnet attacks with a headstrong detection rate of 0.94. In [88] Security attacks on IoT networks can now be more accurately detected and intelligently stopped with the use of convolutional neural networks (CNNs), bidirectional long short-term memory (Bi-LSTM), and other machine learning and deep learning techniques. Training and testing the model on two separate datasets, UNSW-NB151 and NSL-Botnet2, ensure its flexibility to handle various data types.…”

Section: B Features Selection Optimizedmentioning

confidence: 99%

Exploring Lightweight Deep Learning Techniques for Intrusion Detection Systems in IoT Networks: A Survey

Hind Ali abdul Hassan

2024

jes

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The proliferation of Internet users has coincided with a commensurate increase in the amount of very important, sensitive, and private information being transferred across the Internet. Malicious actors are increasingly targeting networks to breach them and obtain illegal access to critical information since this trend has revealed holes in security systems. In addition to endangering the privacy of the data concerned, these breaches disrupt the smooth functioning of systems. Therefore, in light of these dangers, intrusion detection systems (IDSs) are now an essential part of any cybersecurity program. The goal of these systems is to detect and report any suspicious activity by constantly monitoring and analyzing network traffic. Numerous review articles have investigated various methods for network intrusion detection. To improve detection accuracy while keeping computing efficiency high, this survey study investigates lightweight deep learning techniques for intrusion detection systems. These techniques include pruning, quantization, clustering, and collaborative optimization. This study analyzes five different types of new real-world traffic datasets (i.e., CSE-CIC- IDS2018, NSL-KDD, Bot-IoT, ToN IoT Network, and UNSW-NB15) and evaluates the performance of several machine learning and deep learning techniques. This survey provides metrics for measuring the accuracy of intrusion detection across various systems, which may be used to assess performance.

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Section: B Features Selection Optimizedmentioning

confidence: 99%

Exploring Lightweight Deep Learning Techniques for Intrusion Detection Systems in IoT Networks: A Survey

Hind Ali abdul Hassan

2024

jes

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“…Hybrid IDS utilizes the power of two or more detection approaches. After characterizing, comprehending, and analyzing the behavior of the bots, honeypots are used to detect the botnet [9]. Additionally, honeypots need signature extraction, data analysis, etc., to detect the presence of bots.…”

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confidence: 99%

IMTIBOT: An Intelligent Mitigation Technique for IoT Botnets

Garg,

Kumar,

Mahanti

2024

Future Internet

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The tremendous growth of the Internet of Things (IoT) has gained a lot of attention in the global market. The massive deployment of IoT is also inherent in various security vulnerabilities, which become easy targets for hackers. IoT botnets are one type of critical malware that degrades the performance of the IoT network and is difficult to detect by end-users. Although there are several traditional IoT botnet mitigation techniques such as access control, data encryption, and secured device configuration, these traditional mitigation techniques are difficult to apply due to normal traffic behavior, similar packet transmission, and the repetitive nature of IoT network traffic. Motivated by botnet obfuscation, this article proposes an intelligent mitigation technique for IoT botnets, named IMTIBoT. Using this technique, we harnessed the stacking of ensemble classifiers to build an intelligent system. This stacking classifier technique was tested using an experimental testbed of IoT nodes and sensors. This system achieved an accuracy of 0.984, with low latency.

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Surveying Strategies: Detecting Sinkhole Attacks in IoT Networks

Shanthini,

Sheeba Joice

2024

Lecture Notes in Networks and Systems

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Hybrid intelligent intrusion detection system for internet of things

Cited by 10 publications

References 15 publications

Exploring Lightweight Deep Learning Techniques for Intrusion Detection Systems in IoT Networks: A Survey

Exploring Lightweight Deep Learning Techniques for Intrusion Detection Systems in IoT Networks: A Survey

IMTIBOT: An Intelligent Mitigation Technique for IoT Botnets

Surveying Strategies: Detecting Sinkhole Attacks in IoT Networks

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