scholarly journals Intrusion Detection Systems for Smart Home IoT Devices: Experimental Comparison Study

2020 ◽  
pp. 87-98
Author(s):  
Faisal Alsakran ◽  
Gueltoum Bendiab ◽  
Stavros Shiaeles ◽  
Nicholas Kolokotronis
Keyword(s):  
Intrusion Detection ◽  
Smart Home ◽  
Intrusion Detection Systems ◽  
Experimental Comparison ◽  
Comparison Study ◽  
Detection Systems ◽  
Iot Devices

scholarly journals Recent Advancements in Intrusion Detection Systems for the Internet of Things

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Zeeshan Ali Khan ◽  
Peter Herrmann
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Ad Hoc ◽  
Denial Of Service ◽  
Research Work ◽  
Intrusion Detection Systems ◽  
Future Research ◽  
Detection Systems ◽  
Iot Applications ◽  
Iot Devices

Many Internet of Things (IoT) systems run on tiny connected devices that have to deal with severe processor and energy restrictions. Often, the limited processing resources do not allow the use of standard security mechanisms on the nodes, making IoT applications quite vulnerable to different types of attacks. This holds particularly for intrusion detection systems (IDS) that are usually too resource-heavy to be handled by small IoT devices. Thus, many IoT systems are not sufficiently protected against typical network attacks like Denial-of-Service (DoS) and routing attacks. On the other side, IDSs have already been successfully used in adjacent network types like Mobile Ad hoc Networks (MANET), Wireless Sensor Networks (WSN), and Cyber-Physical Systems (CPS) which, in part, face limitations similar to those of IoT applications. Moreover, there is research work ongoing that promises IDSs that may better fit to the limitations of IoT devices. In this article, we will give an overview about IDSs suited for IoT networks. Besides looking on approaches developed particularly for IoT, we introduce also work for the three similar network types mentioned above and discuss if they are also suitable for IoT systems. In addition, we present some suggestions for future research work that could be useful to make IoT networks more secure.

Hybrid Intrusion Detection System for Smart Home Applications

2021 ◽  
pp. 300-322
Author(s):  
Fida Hussain ◽  
Abhaya Induruwa ◽  
Man Qi
Keyword(s):  
Intrusion Detection ◽  
Smart Home ◽  
Detection System ◽  
Intrusion Detection Systems ◽  
Privacy And Security ◽  
Detection Scheme ◽  
Detection Systems ◽  
Cyber Threats ◽  
Network Intrusions ◽  
Anomaly Intrusion Detection

Smart homes, which incorporate IoT technologies to provide home security, efficient environmental services, conveniences, and improved living standards, are becoming the centre of smart urban developments. With the increased inter-connectivity of smart objects and sensors, there is now, also, an increased level of cyber threats, which can compromise privacy and security. These threats either modify packets of information or inject modified packets into the networks. This chapter examines current intrusion detection systems (IDSs) and presents a unique solution to overcome intrusion detection challenges. It discusses the implementation of smart home IDS (SHIDS), using a machine learning based signature and anomaly intrusion detection scheme to detect network intrusions in the smart home. Suggested mechanism is based on naïve Bayes technique to improve the detection performance. The performance of SHIDS has been tested with network intrusions resulting from DoS, probe, remote-to-local (R2L), and user-to-root (U2R) attacks.

Intrusion Detection Systems for Internet of Things

2020 ◽  
pp. 148-171
Author(s):  
Gayathri K. S. ◽  
Tony Thomas
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Remote Monitoring ◽  
Human Life ◽  
Intrusion Detection Systems ◽  
Security Vulnerabilities ◽  
Detection Systems ◽  
Iot Devices ◽  
And Storage ◽  
Security Of Iot

Internet of things (IoT) is revolutionizing this world with its evolving applications in various aspects of life such as sensing, healthcare, remote monitoring, and so on. These systems improve the comfort and efficiency of human life, but the inherent vulnerabilities in these IoT devices create a backdoor for intruders to enter and attack the entire system. Hence, there is a need for intrusion detection systems (IDSs) designed for IoT environments to mitigate IoT-related security attacks that exploit some of these security vulnerabilities. Due to the limited computing and storage capabilities of IoT devices and the specific protocols used, conventional IDSs may not be an option for IoT environments. Since the security of IoT systems is critical, this chapter presents recent research in intrusion detection systems in IoT systems.

scholarly journals Endpoint detection and response using machine learning

2021 ◽  
Vol 2062 (1) ◽  
pp. 012013
Author(s):  
Harmionee Kaur ◽  
Richa Tiwari
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection Systems ◽  
Endpoint Detection ◽  
Detection Systems ◽  
The Past ◽  
Iot Devices

Abstract The need for cybersecurity has increased manifold over the past decade due to an unprecedented shift towards digital. With the increase in the number and sophistication of threats, cybersecurity experts have been forced to seek out new and efficient ways to secure endpoints on a network. Machine learning provides one such solution. This paper discusses how IoT devices are threatened and the need for endpoint security. It overviews different Machine learning-based intrusion detection systems that are currently in use e.g., STAT, Haystack, etc., and other Endpoint Detection and Response Techniques.

Disadvantages of Modern Intrusion Detection Systems

2006 ◽  
Vol 65 (10) ◽  
pp. 929-936
Author(s):  
A. V. Agranovskiy ◽  
S. A. Repalov ◽  
R. A. Khadi ◽  
M. B. Yakubets
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection Systems ◽  
Detection Systems
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