A Lightweight and Scalable Physical Layer Attack Detection Mechanism for the Internet of Things (IoT) Using Hybrid Security Schema

Fotohi, Reza; Pakdel, Hossein

doi:10.1007/s11277-021-08388-1

Cited by 20 publications

(8 citation statements)

References 23 publications

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“…Also the model is tested on real testbed with a smaller size of IoT network. Also, Fotohi et al 23 proposes a lightweight clustering mechanism for detecting flooding attacks in IoT environment using ant colony optimization algorithm. The model efficacy has been tested using the NS‐3 network simulator.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised ensemble based deep learning approach for attack detection in IoT network

Ahmad

Shah

2022

Concurrency and Computation

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Summary The Internet of Things (IoT) has altered living by controlling devices/things over the Internet. IoT has specified many smart solutions for daily problems, transforming cyber‐physical systems (CPS) and other classical fields into smart regions. Most of the edge devices that make up the Internet of Things have very minimal processing power. To bring down the IoT network, attackers can utilize these devices to conduct a variety of network attacks. In addition, as more and more IoT devices are added, the potential for new and unknown threats grows exponentially. For this reason, an intelligent security framework for IoT networks must be developed that can identify such threats. In this paper, we have developed an unsupervised ensemble learning model that is able to detect new or unknown attacks in an IoT network from an unlabeled dataset. The system‐generated labeled dataset is used to train a deep learning model to detect IoT network attacks. Additionally, the research presents a feature selection mechanism for identifying the most relevant aspects in the dataset for detecting attacks. The study shows that the suggested model is able to identify the unlabeled IoT network datasets and DBN (Deep Belief Network) outperform the other models with a detection accuracy of 97.5% and a false alarm rate of 2.3% when trained using labeled dataset supplied by the proposed approach.

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Section: Related Workmentioning

confidence: 99%

Unsupervised ensemble based deep learning approach for attack detection in IoT network

Ahmad

Shah

2022

Concurrency and Computation

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“…𝜂: In mobile ad-hoc networks, the end-to-end delay criterion refers to the time it takes for data packets along the entire network to reach the destination node from the source node. [31][32][33][34] Delay is calculated as Equation ( 7):…”

Section: Performance Metricsmentioning

confidence: 99%

A reliable and secure method for network‐layer attack discovery and elimination in mobile ad‐hoc networks based on a probabilistic threshold

Ebazadeh

Fotohi

2021

Security and Privacy

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The wireless nature of mobile networks makes it vulnerable to all kinds of attacks. Therefore, designing a secure method for detecting intrusive nodes in this type of network seems necessary. Among the types of attacks on mobile ad-hoc networks, the Grayhole attack is one of the most dangerous attacks. In this type of attack, a malicious node absorbs network traffic and then selectively deletes a number of received packets. In this paper, a method called RSA-GRAY HOLE is used to detect and isolate the Grayhole attack. The proposed method not only detects the malicious node, but also disconnects it from the network and does not allow it to participate in the next routing process. The proposed method detects a malicious node in two steps; in the first step, it examines the validity of the nodes of a route to select a valid route. In the second step, the destination node detects the presence of malicious nodes in the route and after detecting the malicious nodes, disconnects them from the network. To demonstrate the efficiency of the proposed RSA-GRAY HOLE method, the NS-2 simulator was used and this method was compared with the DSR protocol under Grayhole attack. The simulation results show the superior performance of the proposed RSA-GRAY HOLE method compared to the Grayhole DSR method in terms of throughput (23%), package delivery rate (26%), average delay (19%) and number of lost packets (20%).

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“…In Botnets, high‐capacity resources have command and control (C&C), which issues instructions per the attacker's guidelines. The dangerous flooding attacks fall into the category of DDoS attacks 6 . In order to ensure security, intrusion detection systems (IDSs), especially network IDSs (NIDSs) are currently one of the most prominent solutions 7 .…”

Section: Introductionmentioning

confidence: 99%

“…The dangerous flooding attacks fall into the category of DDoS attacks. 6 In order to ensure security, intrusion detection systems (IDSs), especially network IDSs (NIDSs) are currently one of the most prominent solutions. 7 DDoS attack architecture is shown in Figure 1.…”

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confidence: 99%

An intelligent intrusion detection system for distributed denial of service attacks: A support vector machine with hybrid optimization algorithm based approach

Sumathi

Rajesh

2022

Concurrency and Computation

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Summary Cloud computing offers comfortable service to business sectors as they can concentrate on their products. Over the internet, cloud computing is liable to various security threats and attacks which is a primary obstacle to the growth of cloud computing services. Distributed denial of service (DDoS) is one such attack that exploits cloud computing services using compromised machines; hence, its detection is a significant field of research. Several DDoS detection schemes have been proposed in the past, but they fail to detect real‐time active DDoS attacks because of their growth in severity and volume. Machine learning (ML) techniques are efficient in making predictions; hence, in this study, a hybrid ML intrusion detection system (IDS) model is proposed. The performance of the proposed IDS model is improved by employing a 10‐fold cross‐validation technique to perform feature selection, reducing data dimensions on the publicly available benchmark NSL‐KDD dataset. Performance validation of the proposed hybrid IDS model is done using the confusion matrix. Support vector machine (SVM) parameters are fine‐tuned using hybrid Harris Hawks optimization (HHO) and particle swarm optimization (PSO) algorithms. The performance of these hybrid algorithms is compared with other classical algorithms such as C4.5, K‐nearest neighbor, and SVM using performance metrics such as precision, sensitivity, specificity, F1 score, and accuracy. From these comparisons, it can be inferred that the proposed SVM with hybrid optimization HHO‐PSO machine learning IDS model performs better DDoS detection with good performance metric values.

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A Lightweight and Scalable Physical Layer Attack Detection Mechanism for the Internet of Things (IoT) Using Hybrid Security Schema

Cited by 20 publications

References 23 publications

Unsupervised ensemble based deep learning approach for attack detection in IoT network

Unsupervised ensemble based deep learning approach for attack detection in IoT network

A reliable and secure method for network‐layer attack discovery and elimination in mobile ad‐hoc networks based on a probabilistic threshold

An intelligent intrusion detection system for distributed denial of service attacks: A support vector machine with hybrid optimization algorithm based approach

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