The notorious B.I.T: The effects of a ransomware and a screen failure on distraction in automated driving

Payre, William; Perelló-March, Jaume; Sriranga, Ashwini Kanakapura; Birrell, Stewart A.

doi:10.1016/j.trf.2023.02.002

Cited by 9 publications

(4 citation statements)

References 32 publications

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“…Nonetheless, challenges remain in balancing scalability and authenticity -easily deployed honeypots tend to be less deceptive to humandirected ransomware [2,57]. Developing customizable, dynamic decoys indistinguishable from genuine data at scale is an open problem [28,58]. Moreover, data exfiltration prior to encryption leaves minimal time between decoy-based detection and actual data loss [59,16].…”

Section: Current Decoy File Techniquesmentioning

confidence: 99%

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Mitigating Data Exfiltration Ransomware through Advanced Decoy File Strategies

Liu,

Chen

2023

Preprint

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This study introduces an advanced decoy file strategy utilizing Generative Adversarial Networks (GANs) to combat data exfiltration ransomware threats. Focused on creating highly realistic decoy documents, the system embeds these across enterprise networks, engaging ransomware threats effectively. It includes real-time monitoring for abnormal interactions and proactive response mechanisms for threat containment. Rigorous testing against various ransomware samples demonstrated high engagement rates and rapid response times, confirming the system's effectiveness. Integration across diverse network types was achieved with minimal operational overhead. This study highlights the system's adaptability in the face of evolving ransomware tactics and underscores the balance between decoy realism and deployability.

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Section: Current Decoy File Techniquesmentioning

confidence: 99%

“…Thus, traditional decoy file strategies have managed to capture threats but largely failed to prevent actual data breaches [27]. This underscores the need for more intelligent and high-interaction decoys that lure malware into engaging more deeply before exposing itself [28].…”

Section: Introductionmentioning

confidence: 99%

Mitigating Data Exfiltration Ransomware through Advanced Decoy File Strategies

Liu,

Chen

2023

Preprint

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“…Many problems are possible due to the failure of display devices. In [9], the authors studied the effects of screen failure on driving performance in automated cars. They found that after the failure, objective trust declined, drivers took over more frequently, and lateral vehicle control was compromised.…”

Section: A Components Of Adasmentioning

confidence: 99%

Securing the Future: A Comprehensive Review of Security Challenges and Solutions in Advanced Driver Assistance Systems

Mehta,

Padaria,

Bavisi

et al. 2024

IEEE Access

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Advanced Driver Assistance Systems (ADAS) are advanced technologies that assist drivers with vehicle operation and navigation. Recent improvements and brisk expansion in the ADAS market, as well as an increase in the frequency of incidents such as sensor spoofing, communication interruption etc., in autonomous vehicles (AVs), have raised the need to research ADAS security technology. The security issues raised by incorporating these technologies into automobiles must be addressed to protect the privacy and safety of passengers and other road users. As a result, the purpose of this research is to investigate the security issues that arise from the integration of ADAS technologies. Addressing these challenges holds the potential to establish a foundation for enhanced safety and dependability within transportation networks amidst the ongoing advancements in vehicle technology. This paper starts by describing the vulnerabilities, threats, assaults, and defense mechanisms of the ADAS. It then delves into the attacks and countermeasures in three categories, namely VANET, Hardware, and Adversarial attacks.VANET attacks encompass threats targeting Vehicular Ad Hoc Networks, aiming to disrupt communication among vehicles or between vehicles and infrastructure. Hardware attacks focus on vulnerabilities within the physical components of ADAS, including sensors, processors, or communication modules. Adversarial attacks involve deliberate manipulations or perturbations introduced into machine learning models or algorithms utilized within ADAS. These attacks aim to deceive or undermine the functionality of AI-based systems, causing misclassification, compromising system integrity, and posing risks to user safety by exploiting vulnerabilities in the AI decision-making process. Finally, this study highlights potential areas for future research, such as the utilization of artificial intelligence (AI), the necessity of industry-wide standardization, and recommends specific future work tailored to each attack described in the corresponding sections. INDEX TERMS ADAS, Advanced Driver Assistance Systems, Attacks, Countermeasures, Defences, Security, Threats I. INTRODUCTION ADAS stands for "Advanced Driver Assistance System". It describes a group of electronic devices and technologies that help drivers operate their cars more safely and effectively. ADAS has numerous electrical and digital tools that aid drivers in minimizing errors and performing parking functions, among other things. The primary goal of this study is to lessen human mistakes and prevent accidents through technologies like automatic emergency braking, collision avoidance, lane-keeping assistance, and autonomous cruise

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“…The data is then slowly and stealthily funneled out of the organization over an extended period using encrypted communication channels to cloud storage sites controlled by the attackers [9]. With terabytes of sensitive data successfully extracted from the compromised network, the ransomware gangs demand sizable cryptocurrency payments in exchange for assurances the stolen data will not be made public [10][11][12]. The exfiltrated information often contains trade secrets, financial records, personal information, or other sensitive data that could prove catastrophically damaging if leaked [13].…”

Section: Introductionmentioning

confidence: 99%

Applying Moving Target Defense Against Data Theft Ransomware on Windows OS

Liu,

Chen

2023

Preprint

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This study addresses the escalating threat of data theft ransomware, a form of cyberattack that exfiltrates sensitive information from victim networks and demands ransom for its non-disclosure. Unlike traditional crypto-ransomware, data theft ransomware leverages various infiltration techniques to gain unauthorized access and control over critical data assets. The study introduces a novel Moving Target Defense (MTD) framework, specifically designed for the Windows operating system environment, to counter these sophisticated attacks. MTD increases the unpredictability of the system by dynamically altering its configurations, thereby disrupting the adversary's ability to execute successful attacks. Our research includes the development and empirical evaluation of the MTD framework, demonstrating its effectiveness in reducing ransomware operational capabilities through quantitative analysis, qualitative observations, and statistical significance testing. We also explore the challenges and complexities involved in implementing MTD in real-world scenarios and propose strategies to overcome these barriers. The study concludes with recommendations for future research directions and the potential of MTD in enhancing cybersecurity defense mechanisms.

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The notorious B.I.T: The effects of a ransomware and a screen failure on distraction in automated driving

Cited by 9 publications

References 32 publications

Mitigating Data Exfiltration Ransomware through Advanced Decoy File Strategies

Mitigating Data Exfiltration Ransomware through Advanced Decoy File Strategies

Securing the Future: A Comprehensive Review of Security Challenges and Solutions in Advanced Driver Assistance Systems

Applying Moving Target Defense Against Data Theft Ransomware on Windows OS

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