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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).

Zero-Touch Network Security (ZTNS) : a network intrusion detection system based on deep learning

- Qazi, Emad-Ul-Haq, Zia, Tanveer, Hamza Faheem, Muhammad, Shahzad, Khurram, Imran, Muhammad, Ahmed, Zeeshan

  • Authors: Qazi, Emad-Ul-Haq , Zia, Tanveer , Hamza Faheem, Muhammad , Shahzad, Khurram , Imran, Muhammad , Ahmed, Zeeshan
  • Date: 2024
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 12, no. (2024), p. 141625-141638
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  • Description: The rapid evaluation of smart cities has revolutionized the research and development field to a very extensive level which presents challenges in handling massive amounts of data. However, the integration of IoT into various aspects of life has introduced various challenges related to the security and privacy of IoT systems. IoT sensors capture large volumes of sensitive customer data, which can potentially make them a target and pose serious threats, including financial loss and identity theft. Strong intrusion detection systems are essential for protecting networked, data-driven ecosystems from potential cyber threats. In this paper, we propose a novel deep learning-based approach that focuses on emerging zero-Touch networks that autonomously manage network resources to ensure network security, the proposed approach identifies various network intrusions such as DDoS, Botnet, Brute force, and Infiltration. Our proposed approach presents a major improvement in IoT security. We have used the CICIDS-2018 benchmark dataset and propose a deep learning-based network intrusion detection System for Zero Touch Networks (DL-NIDS-ZTN). The proposed study utilizes convolutional neural networks that correctly identify benign and malicious traffic and achieve 99.80% accuracy with the CICIDS-2018 dataset. By implementing the DL-NIDS-ZTN methodology, we aim to strengthen the security framework of smart cities and ensure the secure and seamless integration of IoT. © 2013 IEEE.
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