Cloud-Based Cyber-Physical Intrusion Detection for Vehicles Using Deep Learning

Loukas, George; Vuong, Tuan; Heartfield, Ryan; Sakellari, Georgia; Yoon, Yongpil; Gan, Diane

doi:10.1109/access.2017.2782159

Cited by 220 publications

(104 citation statements)

References 44 publications

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“…This accounts for only a small subset of the attack space in vehicles. Using an LSTM, Loukas et al [155] achieved a 86.9% accuracy across all attack types including DDoS, command injection, and network malware. This accuracy rate was better than that achieved with the other standard machine learning methods.…”

Section: Network Intrusion Detectionmentioning

confidence: 99%

A Survey of Deep Learning Methods for Cyber Security

et al. 2019

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This survey paper describes a literature review of deep learning (DL) methods for cyber security applications. A short tutorial-style description of each DL method is provided, including deep autoencoders, restricted Boltzmann machines, recurrent neural networks, generative adversarial networks, and several others. Then we discuss how each of the DL methods is used for security applications. We cover a broad array of attack types including malware, spam, insider threats, network intrusions, false data injection, and malicious domain names used by botnets.

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Section: Network Intrusion Detectionmentioning

confidence: 99%

A Survey of Deep Learning Methods for Cyber Security

et al. 2019

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“…However, in terms of the origin of a sensor's failure, there is no provision to distinguish between malicious threats of cyber origin and natural sensor failures, making this work rather impractical in this context. Contrary to previous approaches that prioritise lightweight approaches, Loukas et al [13], [67] have shown that very accurate, but also computationally heavy machine learning algorithms, such as deep learning, can be used if the detection task is offloaded to a more powerful infrastructure, such as a remote server or cloud. The authors argue that computation offloading can be extremely useful for demanding, realtime and continuous tasks required by resource-constrained and time-critical cyber-physical systems.…”

Section: B Land Vehiclesmentioning

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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“…Therefore, Spark has a very strong potential for the operation of iterative algorithms in machine learning and data mining. In addition, Spark can support streaming computing by dividing micro-batches [13]. Streaming data is a kind of data set that can grow infinitely with the passing of time.…”

Section: Spark-based Elevator Flow Data Processing Frameworkmentioning

confidence: 99%

Influence of the combination of big data technology on the Spark platform with deep learning on elevator safety monitoring efficiency

2020

PLoS ONE

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To effectively minimize elevator safety accidents, big data technology is combined with deep learning technology based on the Spark platform. This study first introduces the relevant theories of elevator safety monitoring technology, namely big data technology and deep learning technology. Then, the fault types that occur in the running state of the elevator are identified, and a finite state machine model is established. An elevator fault monitoring method based on the Spark platform is proposed, namely finite state machine (FSM), and the results of elevator safety fault monitoring are evaluated. Based on deep learning, an elevator fault warning model is constructed and its early warning performance is evaluated. The results show that the study can realize real-time and effective monitoring in the operation state of the elevator, and can determine the fault type of the elevator by binding the abnormal operation state with the corresponding fault. The feasibility of the elevator safety monitoring efficiency is evaluated based on three indexes: mutual information, accuracy, and false positives. Compared with other algorithms, the proposed FSM algorithm has the largest mutual information (0.1337), the highest accuracy (0.9899), the lowest false positive rate (0.0624), and the lowest false negative rate (0.1126); compared with other models, the elevator fault warning model proposed in this study has the lowest root mean-square error (RMSE) value (0.0201), the highest accuracy (0.9834), the lowest Loss value (0.0012), and the shortest convergence time (88.2608s), indicating that the elevator safety monitoring system and elevator fault warning model are feasible. This study establishes a good direction for elevator safety monitoring efficiency in China.

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Cloud-Based Cyber-Physical Intrusion Detection for Vehicles Using Deep Learning

Cited by 220 publications

References 44 publications

A Survey of Deep Learning Methods for Cyber Security

A Survey of Deep Learning Methods for Cyber Security

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

Influence of the combination of big data technology on the Spark platform with deep learning on elevator safety monitoring efficiency

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