A Supervised Intrusion Detection System for Smart Home IoT Devices

By Eirini Anthi, Lowri Williams, M. Slowinska, G. Theodorakopoulos, P. Burnap. IEEE Internet of Things Journal, 2019

452 citations recorded by the source corpus.

The proliferation in Internet of Things (IoT) devices, which routinely collect sensitive information, is demonstrated by their prominence in our daily lives. Although such devices simplify and automate every day tasks, they also introduce tremendous security flaws. Current insufficient security measures employed to defend smart devices make IoT the “weakest” link to breaking into a secure infrastructure, and therefore an attractive target to attackers. This paper proposes a three layer intrusion detection system (IDS) that uses a supervised approach to detect a range of popular network based cyber-attacks on IoT networks. The system consists of three main functions: 1) classify the type and profile the normal behavior of each IoT device connected to the network; 2) identifies malicious packets on the network when an attack is occurring; and 3) classifies the type of the attack that has been deployed. The system is evaluated within a smart home testbed consisting of eight popular commercially available devices. The effectiveness of the proposed IDS architecture is evaluated by deploying 12 attacks from 4 main network based attack categories, such as denial of service (DoS), man-in-the-middle (MITM)/spoofing, reconnaissance, and replay. Additionally, the system is also evaluated against four scenarios of multistage attacks with complex chains of events. The performance of the system’s three core functions result in an ${F}$ -measure of: 1) 96.2%; 2) 90.0%; and 3) 98.0%. This demonstrates that the proposed architecture can automatically distinguish between IoT devices on the network, whether network activity is malicious or benign, and detect which attack was deployed on which device connected to the network successfully.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/0d10c6c188793b1541086b0553cb881737e32730

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Citations in the corpus

CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment (2023, background): “IoT-SH (2019) [36] is a dataset composed of captures of twelve attacks (categorized into four classes) against eight different smart home devices.”
Anomaly-based intrusion detection system for IoT networks through deep learning model (2022, unknown): “In addition, a thorough analysis of the network protection is required [2].”
A Survey on Smart Agriculture: Development Modes, Technologies, and Security and Privacy Challenges (2021, background): “[120] 2019 Three layers, including, layer 1 for scanning the network, layer 2 for classifying the packets, and layer 3 for classifying malicious packets Simulation-based + Detecting cyber attack + Identifying malicious packets - Authentication and privacy are not achieved High F-measure rates”
Variational LSTM Enhanced Anomaly Detection for Industrial Big Data (2021, background): “[12] concluded the rule/event/signature-based intrusion detection systems in IoT networks.”