IMIDS: An Intelligent Intrusion Detection System against Cyber Threats in IoT

Le, Kim-Hung; Nguyen, Minh-Huy; Tran, Trong-Dat; Tran, Ngoc-Duan

doi:10.3390/electronics11040524

Cited by 59 publications

(12 citation statements)

References 51 publications

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“…Experimental result validate that it has 85% accuracy rate and are able to detect malicious modes within 200 seconds [29]. In 2022, Kim-Hung-Le present an intrusion detection software to protect vehicular communication from different kind of cyber attack such as wormhole, Backdoor etc [30].In 2022,vartika agarwal investigate about deep learning technique to improve RRM in VCN.They highlight various algorithm for resource allocation [31]. In 2022, Vartika Agarwal highlight multitype vehicle identification scheme from real time traffic database and offer subscription plan for its user [32].…”

Section: Literature Reviewmentioning

confidence: 67%

P2PCPM: Point to Point Critical Path Monitoring Based Denial of Service Attack Detection for Vehicular Communication Network Resource Management

Agarwal¹,

Sharma²

2022

IJCDS

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Various types of security attacks are normal in vehicular communication networks. The current study uses a support vector machine to implement a Point to Point Critical Path Monitoring (P2PCPM) based Denial of Service (DOS) Attack detection technique for Vehicular Communication Network (VCN) resource management. Greatest quality of P2PCPM is that it eliminates attacked nodes from the network for the smooth process of vehicular communication. This scheme works well in terms of accuracy as well as attack detection rate. The whole simulation is made and tried by utilizing MATLAB Software. Simulation result shows 99% accuracy in case of security attack detection as well as reduced training and testing error upto 2%. Experimental results indicate that this scheme has a great efficiency and works well up to 1000 nodes, which is the limitation of current implementation. In future, simulation test may be done for unlimited nodes using similar or other techniques of attack detection.

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Section: Literature Reviewmentioning

confidence: 67%

P2PCPM: Point to Point Critical Path Monitoring Based Denial of Service Attack Detection for Vehicular Communication Network Resource Management

Agarwal¹,

Sharma²

2022

IJCDS

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“…The features used in their identifier were a combination of application layer features, RPL features, transaction-based features, and others. Another detection method was proposed in [78] that utilized a convolutional neural network to detect different attacks, including replay attacks.…”

Section: Solutions For Replay Attacksmentioning

confidence: 99%

Replay Attacks in RPL-Based Internet of Things: Survey and Empirical Comparative Study

Albinali,

Azzedin

2023

Preprint

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RPL was developed as a routing protocol in low-power and lossy network contexts to connect many applications using IP-based communication. However, RPL has been subjected to several attacks, including replay attacks. Replay attacks pose significant challenges as any node can initiate the attack by replaying control messages. These messages play a vital role in establishing and sustaining network topology. However, studies that discourse replay attacks are severely limited. To address this issue, we conducted a comprehensive study on replay attack forms and their impact on RPL networks. Our study includes the latest security countermeasures. According to the literature, most RPL replay attacks rely on replaying DIO messages leading to neighbor, DIO suppression, or copycat attacks. DAO messages are also utilized to launch a replay attack. Nevertheless, the literature lacks any study that analyzes DAO replay attacks. To the best of our knowledge, this is the first study that evaluates DAO replay attacks and includes the route table falsification attack.We conduct a comparative study of these attacks and empirically investigate their impact on networks through extensive evaluation experiments. Our findings verify the harm of replay attacks in terms of packet delivery and latency. The average local delivery ratio dropped to less than 60% under DIO suppression and copycat attacks, and the communication latency increased by 50% under neighbor attacks. These results confirm the threat of replay attacks on RPL networks and the need to design countermeasures to mitigate them.

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“…The designed model is then evaluated with existing solutions and shows significant productive superiority with an attack detection accuracy of 98%. Another DL inspired intrusion detection scheme is presented in [30], which is purely inspired by Convolutional Neural Networks (CNN). Authors claim to investigate and further categorize the existence of crucial security threats in IoT.…”

Section: B Related Workmentioning

confidence: 99%

Cyber Threats Detection in Smart Environments Using SDN-Enabled DNN-LSTM Hybrid Framework

et al. 2022

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Internet of Things (IoT) is an instantly exacerbated communication technology that is manifesting miraculous effectuation to revolutionize conventional means of network communication. The applications of IoT are compendiously encompassing our prevalent lifestyle and the integration of IoT with other technologies makes this application spectrum even more latitudinous. However, this admissibility also introduces IoT with a pervasive array of imperative security hazards that demands noteworthy solutions to be swamped. In this scientific study, we proposed Deep Learning (DL) driven Software Defined Networking (SDN) enabled Intrusion Detection System (IDS) to combat emerging cyber threats in IoT. Our proposed model (DNNLSTM) is capable to encounter a tremendous class of common as well as less frequently occurring cyber threats in IoT communications. The proposed model is trained on CICIDS 2018 dataset, and its performance is evaluated on several decisive parameters i.e Accuracy, Precision, Recall, and F1-Score. Furthermore, the designed framework is analytically compared with relevant classifiers, i.e., DNNGRU, and BLSTM for appropriate validation. An exhaustive performance comparison is also conducted between the proposed system and a few preeminent solutions from the literature. The proposed design has circumvented the existing literature with unprecedented performance repercussions such as 99.55% accuracy, 99.36% precision, 99.44% recall, and 99.42% F1-score.

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IMIDS: An Intelligent Intrusion Detection System against Cyber Threats in IoT

Cited by 59 publications

References 51 publications

P2PCPM: Point to Point Critical Path Monitoring Based Denial of Service Attack Detection for Vehicular Communication Network Resource Management

P2PCPM: Point to Point Critical Path Monitoring Based Denial of Service Attack Detection for Vehicular Communication Network Resource Management

Replay Attacks in RPL-Based Internet of Things: Survey and Empirical Comparative Study

Cyber Threats Detection in Smart Environments Using SDN-Enabled DNN-LSTM Hybrid Framework

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