Intrusion Detection in the Internet of Things

Bamou, Abdelouahed

doi:10.30534/ijatcse/2020/0191.52020

Cited by 2 publications

(3 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The goal is to warn users of various attacks threatening their IoT networks, whether internal or external. Internal when the attacker is part of this network, while external when he is outside of it [22].…”

Section: B Intrusion Detection Systemmentioning

confidence: 99%

“…To build an IDS, it is necessary to develop three modules: the monitoring module, which monitors system resources and network traffic; the analysis and detection module, which corresponds to the core of the IDS and its role is to find attacks based on a; finally, the alarm module informs users of the presence of attacks [23]. The detection module is based on the following detection algorithms [22]: Anomaly, signature, specification, and hybrid approaches.…”

Section: B Intrusion Detection Systemmentioning

confidence: 99%

See 1 more Smart Citation

IDS Based on Machine Learning in Reaction to IoT Attacks: Review and Empirical Evaluation

Bamou¹,

Ouadghiri²,

Aghoutane³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

View full text Add to dashboard Cite

Recently, connected objects have been the subject of cyber-attacks at an alarming rate. These devices connected to a vast volume data stream have insufficient resources and are not manually configured. Typically, attacks target the usability and exploitation of these vulnerabilities. These attacks make the mission of traditional intrusion detection (IDS) systems more challenging to limit intrusion threats. Machine learning (ML) can solve this problem, mainly since the Internet of Things (IoT) can collect and transfer massive amounts of data. This data is the essence of ML, enabling it to build security and privacy models which can predict or classify malicious nodes and network traffic in the IoT. This article looks at the more common forms of cyberattacks, which could lead to an IoT system failure, as well as a countermeasure capable of limiting their damage. First, we present a general review of IDS and these evaluation measures as a solution to limit these attacks. After reviewing the ML domain and these often-used algorithms, on which the IDS can be based to accomplish its mission, we examine the different datasets researchers use to form their IDS. Finally, we look at a practical example of using Python to evaluate ML methods on a current dataset (TON IoT). The research is based on previous research on the topic. The results enable us to choose the appropriate algorithms for the IDS to achieve the best binary and multi-classification based on the evaluation parameters.

show abstract

Section: B Intrusion Detection Systemmentioning

confidence: 99%

Section: B Intrusion Detection Systemmentioning

confidence: 99%

IDS Based on Machine Learning in Reaction to IoT Attacks: Review and Empirical Evaluation

Bamou¹,

Ouadghiri²,

Aghoutane³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

View full text Add to dashboard Cite

show abstract

“…In the case of action strategy S ad = ðS A , S C Þ, the attacker obtains the highest benefit in the mobile network. In action strategy S ad = ðS P , S D Þ, the intrusion detection system is beneficial to the attacker when the attacker attacks the moving body [10,11]. If an attacker wants to attack a moving body on the network, the intrusion detection system will take preventive measures.…”

Section: Iot Performance Monitoringmentioning

confidence: 99%

Intrusion Detection Analysis of Internet of Things considering Practical Byzantine Fault Tolerance (PBFT) Algorithm

Chen

Lin

2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

In order to improve the security performance and accuracy of the Internet of things in the use process, it is necessary to use the Internet of things intrusion detection method. At present, the problem of inconsistency between the accuracy of detection results and nodes is more prominent when the Internet of things intrusion detection methods are running. This paper proposes a practical Byzantine fault-tolerant intrusion detection method for the use process of the Internet of things. This method introduces the intrusion detection method and the operation function of foreign attackers on the basis of practical Byzantine fault tolerance; using the expected utility function to the corresponding benefit function of practical Byzantine fault tolerance, the results of Internet of things intrusion detection model can be effectively calculated. Finally, the experimental results show that compared with the existing intrusion detection methods, the proposed method can effectively reduce the energy consumption of the Internet of things in the operation process, can effectively reduce 14.3% and 7.8%, and can effectively reduce the energy consumption of the Internet of things in the operation process.

show abstract

Intrusion Detection in the Internet of Things

Cited by 2 publications

References 25 publications

IDS Based on Machine Learning in Reaction to IoT Attacks: Review and Empirical Evaluation

IDS Based on Machine Learning in Reaction to IoT Attacks: Review and Empirical Evaluation

Intrusion Detection Analysis of Internet of Things considering Practical Byzantine Fault Tolerance (PBFT) Algorithm

Contact Info

Product

Resources

About