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A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions

Javed AsharfMilitary College of Signals, National University of Sciences and Technology (NUST), H-12, Islamabad 44000, PakistanNour MoustafaSchool of Engineering and Information Technology, University of New South Wales at the Australian Defence Force Academy, Canberra BC 2610, AustraliaHasnat KhurshidMilitary College of Signals, National University of Sciences and Technology (NUST), H-12, Islamabad 44000, PakistanEssam DebieSchool of Engineering and Information Technology, University of New South Wales at the Australian Defence Force Academy, Canberra BC 2610, AustraliaWaqas HaiderSchool of Engineering and Information Technology, University of New South Wales at the Australian Defence Force Academy, Canberra BC 2610, AustraliaAbdul WahabDepartment of computer Science, Riphah University, Islamabad 44000, Pakistan

2020en

ABI

Аннотация

The Internet of Things (IoT) is poised to impact several aspects of our lives with its fast proliferation in many areas such as wearable devices, smart sensors and home appliances. IoT devices are characterized by their connectivity, pervasiveness and limited processing capability. The number of IoT devices in the world is increasing rapidly and it is expected that there will be 50 billion devices connected to the Internet by the end of the year 2020. This explosion of IoT devices, which can be easily increased compared to desktop computers, has led to a spike in IoT-based cyber-attack incidents. To alleviate this challenge, there is a requirement to develop new techniques for detecting attacks initiated from compromised IoT devices. Machine and deep learning techniques are in this context the most appropriate detective control approach against attacks generated from IoT devices. This study aims to present a comprehensive review of IoT systems-related technologies, protocols, architecture and threats emerging from compromised IoT devices along with providing an overview of intrusion detection models. This work also covers the analysis of various machine learning and deep learning-based techniques suitable to detect IoT systems related to cyber-attacks.

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Идентификаторы

DOI: 10.3390/electronics9071177

Цитирования и источники

Цитирований: 2Использованных источников: 0

Показатели — AkademScholar