Behavior Rule Specification-Based Intrusion Detection for Safety Critical Medical Cyber Physical Systems

Mitchell, Robert; Chen, Ing-Ray

doi:10.1109/tdsc.2014.2312327

Cited by 185 publications

(96 citation statements)

References 29 publications

(30 reference statements)

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“…A relatively common approach is to use a human expert to first specify the safe and unsafe states of the vehicle and determine a large number of rules that cover all potential states, in what is known as behaviour-specification intrusion detection [4], [5]. Rules can also be determined through a more automated learning phase without the involvement of a human expert: The vehicle is subjected to a series of different attacks, observing their impact and training a machine learning system to recognise these.…”

Section: Related Workmentioning

confidence: 99%

Behaviour-Based Anomaly Detection of Cyber-Physical Attacks on a Robotic Vehicle

Bezemskij

Loukas

Anthony

et al. 2016

2016 15th International Conference on Ubiquitous Computing and Communications and 2016 International Symposium on Cyberspace An

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Abstract-Security is one of the key challenges in cyberphysical systems, because by their nature, any cyber attack against them can have physical repercussions. This is a critical issue for autonomous vehicles; if compromised in terms of their communications or computation they can cause considerable physical damage due to their mobility. Our aim here is to facilitate the automatic detection of cyber attacks on a robotic vehicle. For this purpose, we have developed a detection mechanism, which monitors real-time data from a large number of sources onboard the vehicle, including its sensors, networks and processing. Following a learning phase, where the vehicle is trained in a non-attack state on what values are considered normal, it is then subjected to a series of different cyberphysical and physical-cyber attacks. We approach the problem as a binary classification problem of whether the robot is able to self-detect when and whether it is under attack. Our experimental results show that the approach is promising for most attacks that the vehicle is subjected to. We further improve its performance by using weights that accentuate the anomalies that are less common thus improving overall performance of the detection mechanism for unknown attacks.

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Section: Related Workmentioning

confidence: 99%

Behaviour-Based Anomaly Detection of Cyber-Physical Attacks on a Robotic Vehicle

Bezemskij

Loukas

Anthony

et al. 2016

2016 15th International Conference on Ubiquitous Computing and Communications and 2016 International Symposium on Cyberspace An

View full text Add to dashboard Cite

show abstract

“…Medical Device Security For safety-critical Medical Cyber-Physical Systems (MCPSs), the ability to detect attackers, whilst limiting false alarms, is of critical importance [4]. Attackers who penetrate medical cyber-physical systems have the potential to cause harm to patients through reprogramming devices [8].…”

Section: Amentioning

confidence: 99%

“…Real world attacks on MCPS components are increasing with the attackers aiming to cause node compromise, particularly against Insulin Pumps and Cardiac Devices [4]. These attacks can be initiated through over-the-air software updates, stack overflow exploits or logic bombs through third party developers.…”

Section: Amentioning

confidence: 99%

“…These factors have the potential to make healthcare accessible to everyone and to reduce costs. However, they also provide potential for attackers to gain access to sensitive data for opportunistic purposes, such as for profit [4]. Implanted Medical Devices (IMDs) have become increasingly popular due to the precision control of dosage and the rapid access to data they provide to healthcare professionals [2].…”

Section: Introductionmentioning

confidence: 99%

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A Study into Detecting Anomalous Behaviours within HealthCare Infrastructures

Boddy

Hurst

Mackay

et al. 2016

2016 9th International Conference on Developments in eSystems Engineering (DeSE)

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Abstract-The theft of medical data, which is intrinsically valuable, can lead to loss of patient privacy and trust. With increasing requirements for valuable and accurate information, patients need to be confident that their data is being stored safely and securely. However, medical devices are vulnerable to attacks from the digital domain, with many devices transmitting data unencrypted wirelessly to electronic patient record systems. As such, it is now becoming more necessary to visualise data patterns and trends in order identify erratic and anomalous data behaviours. In this paper, a system design for modelling data flow within healthcare infrastructures is presented. The system assists information security officers within healthcare organisations to improve the situational awareness of cyber security risks. In addition, a visualisation of TCP Socket Connections using real-world network data is put forward, in order to demonstrate the framework and present an analysis of potential risks.

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“…Built upon open transmission mediums, communication networks are vulnerable to channel impairments, failure, interference and fading, and so on [3,4]. Misbehaving nodes always exist along the available paths between sources and destinations.…”

Section: Introductionmentioning

confidence: 99%

Fault-Aware Resource Allocation for Heterogeneous Data Sources with Multipath Routing

Zhang

Chen

Shi

et al. 2017

Scientific Programming

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With the rapid development of cloud computing and big data, diverse types of traffic generated from heterogeneous data sources are delivered throughout communication networks, which consist of various node kinds such as digital sensors and smart actuators, and different applications. Due to the shared medium, communication networks are vulnerable to misbehaving nodes, and it is a crucial aspect to maintain an acceptable level of service degradation. This paper studies the fault-aware resource allocation problem by exploiting multipath routing and dynamic rate assignment for heterogeneous sources. We estimate the impacts of faults and formulate the resource allocation as a lossy network flow optimization problem based on these estimates. The traditional flow optimization solutions focus on homogeneous traffic. In our work, we model the performance of heterogeneous applications as a relaxed utility function and develop an effective utility framework of rate control for heterogeneous sources with multipath routing in presence of misbehaving nodes. We design a distributed algorithm to decide the routing strategy and obtain the rate assignments on the available paths in a lossy utility fair manner. Extensive performance evaluations corroborate the significant performance of our algorithm in effective utility and utility fairness in the presence of misbehaving nodes.

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Behavior Rule Specification-Based Intrusion Detection for Safety Critical Medical Cyber Physical Systems

Cited by 185 publications

References 29 publications

Behaviour-Based Anomaly Detection of Cyber-Physical Attacks on a Robotic Vehicle

Behaviour-Based Anomaly Detection of Cyber-Physical Attacks on a Robotic Vehicle

A Study into Detecting Anomalous Behaviours within HealthCare Infrastructures

Fault-Aware Resource Allocation for Heterogeneous Data Sources with Multipath Routing

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