Decision tree-based detection of denial of service and command injection attacks on robotic vehicles

Vuong, Tuan; Loukas, George; Gan, Diane; Bezemskij, Anatolij

doi:10.1109/wifs.2015.7368559

Cited by 56 publications

(24 citation statements)

References 19 publications

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“…Disrupt communication typically by overwhelming the network with large volumes of meaningless or false data, such as fake alert messages about road accidents and congestion [30], [31], [32], [26], [33], [27], [34], [35], [36], [37] Bus-off attack Exploit the error-handling scheme of in-vehicle networks, by deceiving an uncompromised ECU into thinking it is defective, and eventually forcing itself or even the whole network to shut down [38] Message Distortion Generate distorted reliability message in a vehicular network and activate distribution of this message to a neighbouring vehicle [39] Timing attack An integrity attack that alters message timeslots [40] Replay attack A valid data transmission, such as a command or a sensor reading, is recorded and maliciously repeated at a later point.…”

Section: Attackmentioning

confidence: 99%

A taxonomy and survey of cyber-physical intrusion detection approaches for vehicles

Loukas

Karapistoli²,

Panaousis

et al. 2019

Ad Hoc Networks

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With the growing threat of cyber and cyber-physical attacks against automobiles, drones, ships, driverless pods and other vehicles, there is also a growing need for intrusion detection approaches that can facilitate defence against such threats. Vehicles tend to have limited processing resources and are energy-constrained. So, any security provision needs to abide by these limitations. At the same time, attacks against vehicles are very rare, often making knowledge-based intrusion detection systems less practical than behaviour-based ones, which is the reverse of what is seen in conventional computing systems. Furthermore, vehicle design and implementation can differ wildly between different types or different manufacturers, which can lead to intrusion detection designs that are vehicle-specific. Equally importantly, vehicles are practically defined by their ability to move, autonomously or not. Movement, as well as other physical manifestations of their operation may allow cyber security breaches to lead to physical damage, but can also be an opportunity for detection. For example, physical sensing can contribute to more accurate or more rapid intrusion detection through observation and analysis of physical manifestations of a security breach. This paper presents a classification and survey of intrusion detection systems designed and evaluated specifically on vehicles and networks of vehicles. Its aim is to help identify existing techniques that can be adopted in the industry, along with their advantages and disadvantages, as well as to identify gaps in the literature, which are attractive and highly meaningful areas of future research.

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Section: Attackmentioning

confidence: 99%

A taxonomy and survey of cyber-physical intrusion detection approaches for vehicles

Loukas

Karapistoli²,

Panaousis

et al. 2019

Ad Hoc Networks

Self Cite

View full text Add to dashboard Cite

show abstract

“…By utilizing leveraging physical dynamics of robots, Guo et al [7] have developed a new robotic intrusion detection system (IDS), which can detect actuator attacks as well as sensor attacks for lightweight robots subject to random noises. Through the method of decision tree to generate simple detection rules, Vuong et al [8] evaluated the small-scale remote control of robots against denial of service and command injection attacks. In addition, a novel framework for integrity analysis of robotic systems has been presented, whose output can be used to avoid the inherent design flaws in the system and reduce the damage that may be caused by undiscovered attackers [9].…”

Section: Related Workmentioning

confidence: 99%

Improved Deep Belief Networks (IDBN) Dynamic Model-Based Detection and Mitigation for Targeted Attacks on Heavy-Duty Robots

Xie

et al. 2018

Applied Sciences

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Featured Application: The research is mainly used for heavy-duty robots to detect and mitigate target attacks, which come from both the cyber-domain and the physical-domain. Abstract:In recent years, the robots, especially heavy-duty robots, have become the hardest-hit areas for targeted attacks. These attacks come from both the cyber-domain and the physical-domain. In order to improve the security of heavy-duty robots, this paper proposes a detection and mitigation mechanism which based on improved deep belief networks (IDBN) and dynamic model. The detection mechanism consists of two parts: (1) IDBN security checks, which can detect targeted attacks from the cyber-domain; (2) Dynamic model and security detection, used to detect the targeted attacks which can possibly lead to a physical-domain damage. The mitigation mechanism was established on the base of the detection mechanism and could mitigate transient and discontinuous attacks. Moreover, a test platform was established to carry out the performance evaluation test for the proposed mechanism. The results show that, the detection accuracy for the attack of the cyber-domain of IDBN reaches 96.2%, and the detection accuracy for the attack of physical-domain control commands reaches 94%. The performance evaluation test has verified the reliability and high efficiency of the proposed detection and mitigation mechanism for heavy-duty robots.

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“…This system is aimed at ensuring the availability of the transmitted data. In [8] an intrusion detection system based on the signature analysis is considered. The authors conducted a series of experiments to create a standard template describing the normal behavior of the robot in the absence of any external influence, as well as random behavioral anomalies.…”

Section: Maintaining the Integrity Of The Specificationsmentioning

confidence: 99%

Probabilistic Method for Anomalies Detection Based on the Analysis of Cyber Parameters in a Group of Mobile Robots

Basan¹,

Basan²,

Makarevich³

2018

Adv. sci. technol. eng. syst. j.

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This article is devoted to the issues of ensuring the security of a group of mobile robots in the implementation of attacks aimed at the property of accessibility of information and the availability of network nodes. The article presents a method for detecting an abnormal behavior of a network node based on the analysis by the group members of the parameters: residual energy and network load. Analysis of the behavior of individual robots relative to general behavior using probabilistic methods avoids the problem of creating a reference distribution for describing the behavior of a node, as well as creating a signature database for detecting anomalies. The developed method demonstrates high detection rate of denial of service attack and distributed denial of service attack with the number of malicious nodes not exceeding or slightly exceeding the number of trusted nodes. It also provides detection of the Sybil attack.

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Decision tree-based detection of denial of service and command injection attacks on robotic vehicles

Cited by 56 publications

References 19 publications

A taxonomy and survey of cyber-physical intrusion detection approaches for vehicles

A taxonomy and survey of cyber-physical intrusion detection approaches for vehicles

Improved Deep Belief Networks (IDBN) Dynamic Model-Based Detection and Mitigation for Targeted Attacks on Heavy-Duty Robots

Probabilistic Method for Anomalies Detection Based on the Analysis of Cyber Parameters in a Group of Mobile Robots

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