Deep reinforcement learning approach for autonomous vehicle systems for maintaining security and safety using LSTM-GAN

Rasheed, Iftikhar; Hu, Fei; Zhang, Lin

doi:10.1016/j.vehcom.2020.100266

Cited by 39 publications

(19 citation statements)

References 18 publications

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“…This section describes the comparative analysis for various parameter metrics namely the FID, overall accuracy rate, and cost function for various respective methods namely the proposed CNN-OHGS approach, new deep reinforcement learning-based long short-term memory with generative adversarial network (NDRL-LSTM-GNN) (Rasheed et al, 2020) approach as well as new deep reinforcement learning approach (NDRL) (Ferdowsi et al, 2018). Figure 9 shows the comparative analysis for FID with respect to the total number of iterations.…”

Section: Comparative Analysismentioning

confidence: 99%

CNN‐OHGS: CNN‐oppositional‐based Henry gas solubility optimization model for autonomous vehicle control system

Ravikumar

Kavitha

2021

Journal of Field Robotics

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Numerous developments in technology toward autonomous vehicle systems (AVSs) have been performed for so many years all over the world. As our day-to-day life is becoming progressively dependent on automation vehicle system and control devices, the craze on automation advancements is expected to move closer through scientific technologies like artificial intelligence and robotics. From another point of view, the cyber threat to the AVS causes drastic accidents and traffic congestion by varying the speed differences among the vehicles. To overcome such shortcomings, this paper presented a convolutional neural network-oppositional-based Henry gas solubility optimization (CNN-OHGS) algorithm for an autonomous vehicle control system to enhance the robustness of the vehicle. At the same time, the attackers attempt to embed the faulty or defective data into the sensor readings of the autonomous vehicle to interrupt the optimal distances among the automated vehicles. Therefore to minimize such issues, our proposed framework employs the CNN-OHGS algorithm to reduce the distance variations among the vehicles thus ensuring the safety and optimal distance variation. Finally, the experimental analysis is conducted and the performance evaluation for various attacks, FID evaluation, and remorse function, and distance deviation for all sensor signal attacks are evaluated.The comparative analysis is made and we can clearly state that the proposed work has outperformed other existing approaches.

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Section: Comparative Analysismentioning

confidence: 99%

CNN‐OHGS: CNN‐oppositional‐based Henry gas solubility optimization model for autonomous vehicle control system

Ravikumar

Kavitha

2021

Journal of Field Robotics

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“…Hardware/Software-based Attacks Several attacks can take place over the hardware components and software sys- Platoon Attack [37] DDoS Attack [38] DDoS Attack [74] DDoS Attack [77] DDoS Attack [39] GreyHole & BlackHole [40] Black hole [41] Sybil Attack [42] Sybil Attack [43] Sybil Attack [44] Jamming Attack [69] Jamming Attack [45] jamming Attack [91] Data Manipulation [70] Crossfire Attack [56] Spoofing Attack [57] Spoofing Attack [58] Spoofing Attack [92] Cyber Physical Attack [93] Cyber Physical Attack [46] MDS [47] MDS [49] MDS [50] MDS [82] MDS [68] MDS [76] MDS [55] MDS [48] FDI [51] IDS [52] IDS [59] IDS [60] IDS [53] IDS [54] IDS [81] IDS [73] IDS [94] IDS [95] Trust Computation [96] Trust Computation [61] Trust Computation [62] Trust Computation [63] Trust Computation [71] Trust Computation [65] Trust Computation [66] Trust Com...…”

Section: Security Attacks and Requirementsmentioning

confidence: 99%

“…Confidentiality [53] Availability [37]- [40], [44], [45], [49]- [55], [59], [60], [69], [70], [74], [77], [81], [84], [94], [98] Integrity [36], [46], [48], [52], [68], [76], [82], [91]- [93] Privacy [42], [51], [54], [64], [72], [75], [78], [80] Authentication [41]- [43], [46], [49], [53], [55]- [60] Trust [61]- [63], [65]- [67], [71], [79], [95], [96] This survey centers around the use of ML in achieving the above security requirements. As shown in Fig.…”

Section: Security Attacks and Requirementsmentioning

confidence: 99%

Machine Learning for Security in Vehicular Networks: A Comprehensive Survey

Talpur,

Gurusamy

2021

Preprint

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Machine Learning (ML) has emerged as an attractive and viable technique to provide effective solutions for a wide range of application domains. An important application domain is vehicular networks wherein ML-based approaches are found to be very useful to address various problems. The use of wireless communication between vehicular nodes and/or infrastructure makes it vulnerable to different types of attacks. In this regard, ML and its variants are gaining popularity to detect attacks and deal with different kinds of security issues in vehicular communication. In this paper, we present a comprehensive survey of ML-based techniques for different security issues in vehicular networks. We first briefly introduce the basics of vehicular networks and different types of communications. Apart from the traditional vehicular networks, we also consider modern vehicular network architectures. We propose a taxonomy of security attacks in vehicular networks and discuss various security challenges and requirements. We classify the ML techniques developed in the literature according to their use in vehicular network applications. We explain the solution approaches and working principles of these ML techniques in addressing various security challenges and provide insightful discussion. The limitations and challenges in using ML-based methods in vehicular networks are discussed. Finally, we present observations and lessons learned before we conclude our work.

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“…As the autonomous technology develops, the AVs will have to wirelessly communicate with road facilities, satellites, and other vehicles (e.g., vehicular cloud). How to make sure the cyber-security will be one of the biggest concerns for AVs [88].…”

Section: Cyber-securitymentioning

confidence: 99%

Safety of Autonomous Vehicles

Wang

Zhang

Huang

et al. 2020

Journal of Advanced Transportation

102

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Autonomous vehicle (AV) is regarded as the ultimate solution to future automotive engineering; however, safety still remains the key challenge for the development and commercialization of the AVs. Therefore, a comprehensive understanding of the development status of AVs and reported accidents is becoming urgent. In this article, the levels of automation are reviewed according to the role of the automated system in the autonomous driving process, which will affect the frequency of the disengagements and accidents when driving in autonomous modes. Additionally, the public on-road AV accident reports are statistically analyzed. The results show that over 3.7 million miles have been tested for AVs by various manufacturers from 2014 to 2018. The AVs are frequently taken over by drivers if they deem necessary, and the disengagement frequency varies significantly from 2 × 10−4 to 3 disengagements per mile for different manufacturers. In addition, 128 accidents in 2014–2018 are studied, and about 63% of the total accidents are caused in autonomous mode. A small fraction of the total accidents (∼6%) is directly related to the AVs, while 94% of the accidents are passively initiated by the other parties, including pedestrians, cyclists, motorcycles, and conventional vehicles. These safety risks identified during on-road testing, represented by disengagements and actual accidents, indicate that the passive accidents which are caused by other road users are the majority. The capability of AVs to alert and avoid safety risks caused by the other parties and to make safe decisions to prevent possible fatal accidents would significantly improve the safety of AVs. Practical applications. This literature review summarizes the safety-related issues for AVs by theoretical analysis of the AV systems and statistical investigation of the disengagement and accident reports for on-road testing, and the findings will help inform future research efforts for AV developments.

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Deep reinforcement learning approach for autonomous vehicle systems for maintaining security and safety using LSTM-GAN

Cited by 39 publications

References 18 publications

CNN‐OHGS: CNN‐oppositional‐based Henry gas solubility optimization model for autonomous vehicle control system

CNN‐OHGS: CNN‐oppositional‐based Henry gas solubility optimization model for autonomous vehicle control system

Machine Learning for Security in Vehicular Networks: A Comprehensive Survey

Safety of Autonomous Vehicles

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