Performance Evaluation of Cyber-Physical Intrusion Detection on a Robotic Vehicle

Vuong, Tuan; Loukas, George; Gan, Diane

doi:10.1109/cit/iucc/dasc/picom.2015.313

Cited by 43 publications

(32 citation statements)

References 14 publications

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“…Loukas and G. Oke, 2007). Also, in cyber-physical systems, such as semiautonomous vehicles, the human operator is likely to be the first to observe the adverse physical impact of a command injection attack (T. Vuong et al, 2015). In the future, we intend to extend the scope of this research on human sensors of security threats in terms of types of attacks and platforms involved.…”

Section: Resultsmentioning

confidence: 99%

Predicting the performance of users as human sensors of security threats in social media

Heartfield¹,

Loukas²

2016

IJCSA

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While the human as a sensor concept has been utilised extensively for the detection of threats to safety and security in physical space, especially in emergency response and crime reporting, the concept is largely unexplored in the area of cyber security. Here, we evaluate the potential of utilising users as human sensors for the detection of cyber threats, specifically on social media. For this, we have conducted an online test and accompanying questionnairebased survey, which was taken by 4,457 users. The test included eight realistic social media scenarios (four attack and four non-attack) in the form of screenshots, which the participants were asked to categorise as "likely attack" or "likely not attack". We present the overall performance of human sensors in our experiment for each exhibit, and also apply logistic regression and Random Forest classifiers to evaluate the feasibility of predicting that performance based on different characteristics of the participants. Such prediction would be useful where accuracy of human sensors in detecting and reporting social media security threats is important. We identify features that are good predictors of a human sensor's performance and evaluate them in both a theoretical ideal case and two more realistic cases, the latter corresponding to limited access to a user's characteristics Keyword: -Social media, computer security, semantic attacks, phishing, social engineering, human as a sensor.

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Section: Resultsmentioning

confidence: 99%

Predicting the performance of users as human sensors of security threats in social media

Heartfield¹,

Loukas²

2016

IJCSA

Self Cite

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“…Ref [51][103] [110] showed the consistent characteristics in these applications. The utilization of ML for mechanical fault diagnosis and prevention of cyber attacks in transport system can be more explored, as only two [94] [107] reported the benefits of ML in this area. ML is also a popular solution to configure plant/production, optimize electrical load/dispatch, and reduce road latent cost, forecast short term in electricity usage and etc.…”

Section: Machine Learning Methods In Cpsmentioning

confidence: 99%

CPS data streams analytics based on machine learning for Cloud and Fog Computing: A survey

Fei

Shah

Verba

et al. 2019

Future Generation Computer Systems

100

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Cloud and Fog computing has emerged as a promising paradigm for the Internet of things (IoT) and cyber-physical systems (CPS). One characteristic of CPS is the reciprocal feedback loops between physical processes and cyber elements (computation, software and networking), which implies that data stream analytics is one of the core components of CPS. The reasons for this are: (i) it extracts the insights and the knowledge from the data streams generated by various sensors and other monitoring components embedded in the physical systems; (ii) it supports informed decision making; (iii) it enables feedback from the physical processes to the cyber counterparts; (iv) it eventually facilitates the integration of cyber and physical systems. There have been many successful applications of data streams analytics, powered by machine learning techniques, to CPS systems. Thus, it is necessary to have a survey on the particularities of the application of machine learning techniques to the CPS domain. In particular, we explore how machine learning methods should be deployed and integrated in cloud and fog architectures for better fulfilment of the requirements, e.g. mission criticality and time criticality, arising in CPS domains. To the best of our knowledge, this paper is the first to systematically study machine learning techniques for CPS data stream analytics from various perspectives, especially from a perspective that leads to the discussion and guidance of how the CPS machine learning methods should be deployed in a cloud and fog architecture.

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“…At this point, the source node will be used to prevent data from being sent via the malicious route [16]. Vuong et al developed traditional IDS for detecting cyber-attacks on robotic vehicles [17]. Their IDS is based on a decision tree method that relies on physical features and cyber in detecting malware codes.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks

2016

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Vehicular ad hoc networks (VANETs) play a vital role in the success of self-driving and semi self-driving vehicles, where they improve safety and comfort. Such vehicles depend heavily on external communication with the surrounding environment via data control and Cooperative Awareness Messages (CAMs) exchanges. VANETs are potentially exposed to a number of attacks, such as grey hole, black hole, wormhole and rushing attacks. This work presents an intelligent Intrusion Detection System (IDS) that relies on anomaly detection to protect the external communication system from grey hole and rushing attacks. These attacks aim to disrupt the transmission between vehicles and roadside units. The IDS uses features obtained from a trace file generated in a network simulator and consists of a feed-forward neural network and a support vector machine. Additionally, the paper studies the use of a novel systematic response, employed to protect the vehicle when it encounters malicious behaviour. Our simulations of the proposed detection system show that the proposed schemes possess outstanding detection rates with a reduction in false alarms. This safe mode response system has been evaluated using four performance metrics, namely, received packets, packet delivery ratio, dropped packets and the average end to end delay, under both normal and abnormal conditions.

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Performance Evaluation of Cyber-Physical Intrusion Detection on a Robotic Vehicle

Cited by 43 publications

References 14 publications

Predicting the performance of users as human sensors of security threats in social media

Predicting the performance of users as human sensors of security threats in social media

CPS data streams analytics based on machine learning for Cloud and Fog Computing: A survey

Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks

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