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A critical review of intrusion detection systems in the internet of things : techniques, deployment strategy, validation strategy, attacks, public datasets and challenges

- Khraisat, Ansam, Alazab, Ammar

  • Authors: Khraisat, Ansam , Alazab, Ammar
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Cybersecurity Vol. 4, no. 1 (2021), p.
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  • Description: The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack on the end nodes. To this end, Numerous IoT intrusion detection Systems (IDS) have been proposed in the literature to tackle attacks on the IoT ecosystem, which can be broadly classified based on detection technique, validation strategy, and deployment strategy. This survey paper presents a comprehensive review of contemporary IoT IDS and an overview of techniques, deployment Strategy, validation strategy and datasets that are commonly applied for building IDS. We also review how existing IoT IDS detect intrusive attacks and secure communications on the IoT. It also presents the classification of IoT attacks and discusses future research challenges to counter such IoT attacks to make IoT more secure. These purposes help IoT security researchers by uniting, contrasting, and compiling scattered research efforts. Consequently, we provide a unique IoT IDS taxonomy, which sheds light on IoT IDS techniques, their advantages and disadvantages, IoT attacks that exploit IoT communication systems, corresponding advanced IDS and detection capabilities to detect IoT attacks. © 2021, The Author(s).

Deep learning and big data technologies for IoT security

- Amanullah, Mohamed, Habeeb, Riyaz, Nasaruddin, Fariza, Gani, Abdullah, Ahmed, Ejaz, Nainar, Abdul, Akim, Nazihah, Imran, Muhammad

  • Authors: Amanullah, Mohamed , Habeeb, Riyaz , Nasaruddin, Fariza , Gani, Abdullah , Ahmed, Ejaz , Nainar, Abdul , Akim, Nazihah , Imran, Muhammad
  • Date: 2020
  • Type: Text , Journal article , Review
  • Relation: Computer Communications Vol. 151, no. (2020), p. 495-517
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  • Description: Technology has become inevitable in human life, especially the growth of Internet of Things (IoT), which enables communication and interaction with various devices. However, IoT has been proven to be vulnerable to security breaches. Therefore, it is necessary to develop fool proof solutions by creating new technologies or combining existing technologies to address the security issues. Deep learning, a branch of machine learning has shown promising results in previous studies for detection of security breaches. Additionally, IoT devices generate large volumes, variety, and veracity of data. Thus, when big data technologies are incorporated, higher performance and better data handling can be achieved. Hence, we have conducted a comprehensive survey on state-of-the-art deep learning, IoT security, and big data technologies. Further, a comparative analysis and the relationship among deep learning, IoT security, and big data technologies have also been discussed. Further, we have derived a thematic taxonomy from the comparative analysis of technical studies of the three aforementioned domains. Finally, we have identified and discussed the challenges in incorporating deep learning for IoT security using big data technologies and have provided directions to future researchers on the IoT security aspects. © 2020 Elsevier B.V.

Towards secure fog computing: A survey on trust management, privacy, authentication, threats and access control

- Patwary, Abdullah Al-Noman, Naha, Ranesh Kumar, Garg, Saurabh, Battula, Sudheer, Patwary, Md Anwarul, Aghasian, Erfan, Amin, Muhammad, Mahanti, Aniket, Gong, Mingwei

  • Authors: Patwary, Abdullah Al-Noman , Naha, Ranesh Kumar , Garg, Saurabh , Battula, Sudheer , Patwary, Md Anwarul , Aghasian, Erfan , Amin, Muhammad , Mahanti, Aniket , Gong, Mingwei
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Electronics Vol. 10, no. 10 (2021), p. 1171
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  • Description: Fog computing is an emerging computing paradigm that has come into consideration for the deployment of Internet of Things (IoT) applications amongst researchers and technology industries over the last few years. Fog is highly distributed and consists of a wide number of autonomous end devices, which contribute to the processing. However, the variety of devices offered across different users are not audited. Hence, the security of Fog devices is a major concern that should come into consideration. Therefore, to provide the necessary security for Fog devices, there is a need to understand what the security concerns are with regards to Fog. All aspects of Fog security, which have not been covered by other literature works, need to be identified and aggregated. On the other hand, privacy preservation for user’s data in Fog devices and application data processed in Fog devices is another concern. To provide the appropriate level of trust and privacy, there is a need to focus on authentication, threats and access control mechanisms as well as privacy protection techniques in Fog computing. In this paper, a survey along with a taxonomy is proposed, which presents an overview of existing security concerns in the context of the Fog computing paradigm. Moreover, the Blockchain-based solutions towards a secure Fog computing environment is presented and various research challenges and directions for future research are discussed.
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