Active Learning for Wireless IoT Intrusion Detection

Yang, Kai; Ren, Jie; Zhu, Yanqiao; Zhang, Weiyi

doi:10.1109/mwc.2017.1800079

Cited by 100 publications

(46 citation statements)

References 14 publications

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“…Indeed, when an anomaly occurs, there is no need to send non-trusted or faulty data. Instead, the ADS logs locally all information about the anomaly and only alarms are reported remotely [ 27 , 58 , 59 ].…”

Section: Ads For Smart Hospital Iot Systemsmentioning

confidence: 99%

Efficient Anomaly Detection for Smart Hospital IoT Systems

Said

Yahyaoui

Abdellatif

2021

Sensors

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In critical Internet of Things (IoT) application domains, such as the Defense Industry and Healthcare, false alerts have many negative effects, such as fear, disruption of emergency services, and waste of resources. Therefore, an alert must only be sent if triggered by a correct event. Nevertheless, IoT networks are exposed to intrusions, which affects event detection accuracy. In this paper, an Anomaly Detection System (ADS) is proposed in a smart hospital IoT system for detecting events of interest about patients’ health and environment and, at the same time, for network intrusions. Providing a single system for network infrastructure supervision and e-health monitoring has been shown to optimize resources and enforce the system reliability. Consequently, decisions regarding patients’ care and their environments’ adaptation are more accurate. The low latency is ensured, thanks to a deployment on the edge to allow for a processing close to data sources. The proposed ADS is implemented and evaluated while using Contiki Cooja simulator and the e-health event detection is based on a realistic data-set analysis. The results show a high detection accuracy for both e-health related events and IoT network intrusions.

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Section: Ads For Smart Hospital Iot Systemsmentioning

confidence: 99%

Efficient Anomaly Detection for Smart Hospital IoT Systems

Said

Yahyaoui

Abdellatif

2021

Sensors

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“…With the enormous success of machine learning, many studies have been able to successfully apply machine learning at the network level to detect attacks in IoT-EDs. Yang et al in [48], investigate the active learning method for intrusion detection of wireless IoT device networks. It is seen that the active learning method can efficiently improve the performance over the traditional supervised learning methods for intrusion detection.…”

Section: Related Workmentioning

confidence: 99%

Fusion-On-Field Security and Privacy Preservation for IoT Edge Devices: Concurrent Defense Against Multiple Types of Hardware Trojan Attacks

2020

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Internet of Things (IoT) devices have connected millions of houses around the globe via the internet. In the recent past, threats due to hardware Trojan (HT) in the integrated circuits (IC) have become a serious concern, which affects IoT edge devices (IoT-ED). In this paper, the possibility of the IoT-ED with embedded HT that can cause serious security, privacy, and availability problems to the IoT based Home Area Network (HAN) has been discussed. Conventional network attack detection techniques work at the network protocol layers, whereas IoT-ED with HT can lead to the peculiar manifestation of attack at the physical and/or firmware level. On the other hand, in the IC design, most of the HT-based attack detection techniques require design time intervention, which is expensive for many of the IoT-ED and cannot guarantee 100% immunity. The argument in this paper is that the health of modern IoT-ED requires a final line of defense against possible HT-based attacks that goes undetected during IC design and test. The approach is to utilize power profiling (PP) and network traffic (NT) data without intervening into the IC design to detect malicious activity in HAN. The proposed technique is to effectively identify multiple attacks concurrently and to differentiate between different types of attacks. The IoT-ED behaviors for five different types of random attacks have been studied, including covert channel, DoS, ARQ, power depletion, and impersonation attacks. Data fusion has been leveraged by combining the PP and NT data and is able to detect, without design time intervention, each of the five attacks individually with up to 99% accuracy. Moreover, the proposed technique can also detect all the attacks concurrently with 92% accuracy. To the best of authors' knowledge, this is the first work where multiple HT based attacks are concurrently detected in IoT-ED without requiring any design time intervention. INDEX TERMS Internet of Things, hardware security, home area network, hardware Trojan, machine learning, power profile, ARQ attack, DoS attack.

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“…Also, such solutions rely on good network traces for the training of ML models. The collection and labeling of real network traces are considered as a big problem in the case of resource‐constrained networks . Also, the real‐time efficiency of RPL specific ML‐based IDS solutions is yet to be realized on test beds or real networks.…”

Section: Related Workmentioning

confidence: 99%

Mitigation of DIS flooding attacks in RPL‐based 6LoWPAN networks

Verma

Ranga

2019

Trans Emerging Tel Tech

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The IPv6 Routing Protocol for Low‐Power and Lossy Networks (RPL) is the de facto routing protocol for IPv6‐based Low‐Power Wireless Personal Area Networks (6LoWPAN). In RPL protocol, DODAG Information Solicitation (DIS) messages are sent by the node to join the network. A malicious node can exploit this mechanism to send illegitimate DIS messages to the neighbor nodes to perform a DIS flooding attack. In this research paper, it is observed that the DIS flooding attack increases the control packet overhead of the network, which significantly degrades the network's performance. This further increases the power consumption of the nodes in the network. To address this problem, a mitigation scheme named Secure‐RPL is proposed. The proposed scheme mitigates significantly the effects of DIS flooding attack on the network's performance. The effectiveness of the proposed Secure‐RPL scheme is compared with standard RPL protocol. The experimental results show that Secure‐RPL detects and mitigates DIS flooding attack quickly and efficiently in both static and dynamic network scenarios, without adding any significant overhead to the nodes.

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Active Learning for Wireless IoT Intrusion Detection

Cited by 100 publications

References 14 publications

Efficient Anomaly Detection for Smart Hospital IoT Systems

Efficient Anomaly Detection for Smart Hospital IoT Systems

Fusion-On-Field Security and Privacy Preservation for IoT Edge Devices: Concurrent Defense Against Multiple Types of Hardware Trojan Attacks

Mitigation of DIS flooding attacks in RPL‐based 6LoWPAN networks

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