Implementation of Intrusion Detection System in the Internet of Things: A Survey

Zaman, Shakir; Tauqeer, Haseeb; Ahmad, Waqar I. U.; Shah, Syed Muhammad Adnan; Ilyas, Muhammad

doi:10.1109/inmic50486.2020.9318047

Cited by 7 publications

(2 citation statements)

References 32 publications

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“…The proposed model using J48 realizes a lower performance evaluation for multi-class detection than other models. The proposed model using J48 shows its weakness in detecting different types of attacks [ 36 , 37 , 38 , 39 , 40 , 41 ].…”

Section: Evaluation and Experimental Resultsmentioning

confidence: 99%

Dynamic Adaptation Attack Detection Model for a Distributed Multi-Access Edge Computing Smart City

Alotaibi

Ahmed

Kamel

2023

Sensors

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The internet of things (IoT) technology presents an intelligent way to improve our lives and contributes to many fields such as industry, communications, agriculture, etc. Unfortunately, IoT networks are exposed to many attacks that may destroy the entire network and consume network resources. This paper aims to propose intelligent process automation and an auto-configured intelligent automation detection model (IADM) to detect and prevent malicious network traffic and behaviors/events at distributed multi-access edge computing in an IoT-based smart city. The proposed model consists of two phases. The first phase relies on the intelligent process automation (IPA) technique and contains five modules named, specifically, dataset collection and pre-processing module, intelligent automation detection module, analysis module, detection rules and action module, and database module. In the first phase, each module composes an intelligent connecting module to give feedback reports about each module and send information to the next modules. Therefore, any change in each process can be easily detected and labeled as an intrusion. The intelligent connection module (ICM) may reduce the search time, increase the speed, and increase the security level. The second phase is the dynamic adaptation of the attack detection model based on reinforcement one-shot learning. The first phase is based on a multi-classification technique using Random Forest Trees (RFT), k-Nearest Neighbor (K-NN), J48, AdaBoost, and Bagging. The second phase can learn the new changed behaviors based on reinforced learning to detect zero-day attacks and malicious events in IoT-based smart cities. The experiments are implemented using a UNSW-NB 15 dataset. The proposed model achieves high accuracy rates using RFT, K-NN, and AdaBoost of approximately 98.8%. It is noted that the accuracy rate of the J48 classifier achieves 85.51%, which is lower than the others. Subsequently, the accuracy rates of AdaBoost and Bagging based on J48 are 98.9% and 91.41%, respectively. Additionally, the error rates of RFT, K-NN, and AdaBoost are very low. Similarly, the proposed model achieves high precision, recall, and F1-measure high rates using RFT, K-NN, AdaBoost, and Bagging. The second phase depends on creating an auto-adaptive model through the dynamic adaptation of the attack detection model based on reinforcement one-shot learning using a small number of instances to conserve the memory of any smart device in an IoT network. The proposed auto-adaptive model may reduce false rates of reporting by the intrusion detection system (IDS). It can detect any change in the behaviors of smart devices quickly and easily. The IADM can improve the performance rates for IDS by maintaining the memory consumption, time consumption, and speed of the detection process.

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Section: Evaluation and Experimental Resultsmentioning

confidence: 99%

Dynamic Adaptation Attack Detection Model for a Distributed Multi-Access Edge Computing Smart City

Alotaibi

Ahmed

Kamel

2023

Sensors

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“…Various researchers have proposed blockchain‐based IDS that make use of a variety of techniques, including node clustering for storing and sharing distributed information, support vector machine (SVM) classifiers for quick and accurate detection of network intrusions, anomaly detection based on big data technology, and more. The alert messages based on these systems are subject to bias and human errors, which reduces their accuracy and responsiveness [7]. Despite these improvements, these systems still need help with false positives and scalability.…”

Section: Background and Related Workmentioning

confidence: 99%

An efficient blockchain‐based approach to improve the accuracy of intrusion detection systems

Abubakar,

Liu,

Gilliard

2023

Electronics Letters

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Intrusion Detection System (IDS) is a critical cybersecurity task that involves monitoring network traffic for malicious activity and taking appropriate action to stop it. However, insufficient training data or improperly chosen thresholds often limit the accuracy of such systems, resulting in high false‐positive rates. To improve the accuracy of an IDS, blockchain technology can be used as it provides a secure, decentralized, immutable ledger that can track suspicious activity over time and also identify intrusions globally. In this paper, the authors propose a novel methodology to improve the accuracy of blockchain‐based IDS. The approach combines different intrusion detection algorithms using a blockchain‐integrated architecture. It is based on the fusion principle and weighted votes, which the authors used to determine their results. The authors tested the system on DARPA 99 and MIT‐Lincoln Labs datasets using accuracy and false‐positive rate as their two metrics. The system achieved 92.6% accuracy and 7.4% false‐positive rates, indicating that the proposed system significantly increases the accuracy while reducing the false‐positive rate, opening up new opportunities for the development of highly accurate networks.

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An intelligent intrusion prediction and prevention system for software defined internet of things cloud networks

Kranthi

Kanchana²,

Suneetha³

2022

Peer-to-Peer Netw. Appl.

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Implementation of Intrusion Detection System in the Internet of Things: A Survey

Cited by 7 publications

References 32 publications

Dynamic Adaptation Attack Detection Model for a Distributed Multi-Access Edge Computing Smart City

Dynamic Adaptation Attack Detection Model for a Distributed Multi-Access Edge Computing Smart City

An efficient blockchain‐based approach to improve the accuracy of intrusion detection systems

An intelligent intrusion prediction and prevention system for software defined internet of things cloud networks

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