Enhanced Cyber-Physical Security in IoT

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: closed (28 July 2022) | Viewed by 6871

Special Issue Editors


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Guest Editor
School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA
Interests: cyber and information security

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Guest Editor
Intelligent Secure Systems Research Center/Department of Computer Engineering, King Fahad University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
Interests: cybersecurity; data privacy; cyber resiliency

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) is emerging as the key pillar of Industry 4.0 standard and critical cyber-physical infrastructures. IoT describes the network of physical devices and entities in the form of embedded technology for sensing, actuating, and communicating data with other devices and systems. Typically, IoT devices are connected to the Internet for data exchange, making IoT devices vulnerable to a wide range of cyberattacks such as data spoofing and sniffing, sybil attacks, and DoS attacks. In addition, IoT devices are mostly deployed in an open environment, making them vulnerable to physical attacks such as node injection, object tampering, and side-channel attacks. Many challenges are encountered when addressing the cyber and physical security of the IoT with regard to hardware, software, architecture, data, protocols, safety, design, implementation, verification, and validation.

The Special Issue seeks original scientific research/implementation papers that contribute to enhancing the cyber and physical security of the IoT. The main topics of interest include but are not limited to the following:

  • Cyber and physical security issues in IoT;
  • Safety, governance, risk, threat, and trust management in IoT;
  • Vulnerability and exploitation scanning for IoT;
  • Risk and threat modelling in IoT;
  • Verification and validation of IoT security;
  • Resilience of IoT devices and systems against attacks;
  • Secure design of IoT systems, hardware, software, and protocols;
  • Trusted execution environments (TEEs) for IoT;
  • Physically unclonable functions (PUFs) for IoT;
  • Security and reliability of IoT data;
  • Artificial intelligence and machine learning for IoT security;
  • Security of machine-to-machine communication in IoT;
  • Blockchain solutions for IoT systems;
  • Post-quantum and quantum-enabled security.

Prof. Dr. Arif Ghafoor
Dr. Muhamad Felemban
Guest Editors

Manuscript Submission Information

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Keywords

  • cybersecurity
  • physical security
  • IoT

Published Papers (2 papers)

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Research

21 pages, 901 KiB  
Article
Deep Learning Approach for SDN-Enabled Intrusion Detection System in IoT Networks
by Rajasekhar Chaganti, Wael Suliman, Vinayakumar Ravi and Amit Dua
Information 2023, 14(1), 41; https://doi.org/10.3390/info14010041 - 09 Jan 2023
Cited by 25 | Viewed by 4461
Abstract
Owing to the prevalence of the Internet of things (IoT) devices connected to the Internet, the number of IoT-based attacks has been growing yearly. The existing solutions may not effectively mitigate IoT attacks. In particular, the advanced network-based attack detection solutions using traditional [...] Read more.
Owing to the prevalence of the Internet of things (IoT) devices connected to the Internet, the number of IoT-based attacks has been growing yearly. The existing solutions may not effectively mitigate IoT attacks. In particular, the advanced network-based attack detection solutions using traditional Intrusion detection systems are challenging when the network environment supports traditional as well as IoT protocols and uses a centralized network architecture such as a software defined network (SDN). In this paper, we propose a long short-term memory (LSTM) based approach to detect network attacks using SDN supported intrusion detection system in IoT networks. We present an extensive performance evaluation of the machine learning (ML) and deep learning (DL) model in two SDNIoT-focused datasets. We also propose an LSTM-based architecture for the effective multiclass classification of network attacks in IoT networks. Our evaluation of the proposed model shows that our model effectively identifies the attacks and classifies the attack types with an accuracy of 0.971. In addition, various visualization methods are shown to understand the dataset’s characteristics and visualize the embedding features. Full article
(This article belongs to the Special Issue Enhanced Cyber-Physical Security in IoT)
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15 pages, 588 KiB  
Article
LPCOCN: A Layered Paddy Crop Optimization-Based Capsule Network Approach for Anomaly Detection at IoT Edge
by Bhuvaneswari Amma Narayanavadivoo Gopinathan, Velliangiri Sarveshwaran, Vinayakumar Ravi and Rajasekhar Chaganti
Information 2022, 13(12), 587; https://doi.org/10.3390/info13120587 - 16 Dec 2022
Viewed by 1376
Abstract
Cyberattacks have increased as a consequence of the expansion of the Internet of Things (IoT). It is necessary to detect anomalies so that smart devices need to be protected from these attacks, which must be mitigated at the edge of the IoT network. [...] Read more.
Cyberattacks have increased as a consequence of the expansion of the Internet of Things (IoT). It is necessary to detect anomalies so that smart devices need to be protected from these attacks, which must be mitigated at the edge of the IoT network. Therefore, efficient detection depends on the selection of an optimal IoT traffic feature set and the learning algorithm that classifies the IoT traffic. There is a flaw in the existing anomaly detection systems because the feature selection algorithms do not identify the most appropriate set of features. In this article, a layered paddy crop optimization (LPCO) algorithm is suggested to choose the optimal set of features. Furthermore, the use of smart devices generates tremendous traffic, which can be labelled as either normal or attack using a capsule network (CN) approach. Five network traffic benchmark datasets are utilized to evaluate the proposed approach, including NSL KDD, UNSW NB, CICIDS, CSE-CIC-IDS, and UNSW Bot-IoT. Based on the experiments, the presented approach yields assuring results in comparison with the existing base classifiers and feature selection approaches. Comparatively, the proposed strategy performs better than the current state-of-the-art approaches. Full article
(This article belongs to the Special Issue Enhanced Cyber-Physical Security in IoT)
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