With the increasing adoption of autonomous vehicles, ensuring the reliability of autonomous driving systems (ADSs) deployed on autonomous vehicles has become a significant concern. Driving simulators have emerged as crucial platforms for testing ADSs, offering realistic, dynamic, and configurable environments. However, existing simulation-based ADS testers have largely overlooked the reliability of the simulators, potentially leading to overlooked violation scenarios and subsequent safety security risks during real-world deployment. In our investigations, we identified that collision detectors in simulators could fail to detect and report collisions in certain collision scenarios, referred to asignored collision scenarios. This paper aims to systematically discover ignored collision scenarios to improve the reliability of autonomous driving (AD) simulators. To this end, we present ICSFuzz, a black-box fuzzing approach to discover ignored collision scenarios efficiently. Drawing upon the fact that the ignored collision scenarios are a sub-type of collision scenarios, our approach starts with the determined collision scenarios. Following the guidance provided by empirically studied factors contributing to collisions, we selectively mutate arbitrary collision scenarios in a step-wise manner toward the ignored collision scenarios and effectively discover them. We compare ICSFuzz with multiple state-of-the-art simulation-based ADS testing methods, by replacing their oracle with our ignored-collision-aware oracle. The evaluation demonstrates that ICSFuzz outperforms ADS testers by finding 7~40x more ignored collision scenarios with a 10~105x speedup. Within the discovered ignored collision scenarios, there are two more types of ignored collision scenarios that ADS testers did not find. All the discovered ignored collisions have been confirmed by developers with one CVE ID assigned.

Traditionally, industrial control systems operate in isolated networks with proprietary solutions. As smart factories and digital twins have become inevitable with AI advancement, the rapid adoption of Industrial Internet of Things (IIoT) devices has significantly increased cybersecurity risks. More precisely, the complexity of industrial environments, which includes production processes and device roles, creates substantial challenges for secure deployment. The authors introduce a bottom-up, industrial role-oriented verification framework (IROVF) for manufacturing line deployment. IROVF incorporates SCADA's MTU and RTU components, which are mapped to distinct device roles. This provides authentication and least-privilege principles that are tailored to factory environments. The proposed framework designs an alarm strategy, which can be helpful to detect and report potential operational disruptions during runtime, thus minimizing impact on system availability. Experimental results demonstrate the superior security coverage of the proposed framework compared to existing research, while a comprehensive application scenario validates its practical applicability. The scalable security parameters of IROVF allow organizations to select appropriate security levels based on their specific requirements. IROVF provides an effective security solution for modern industrial control systems during deployment phases.

High-definition (HD) maps provide precise environmental information essential for prediction and planning in autonomous driving (AD) systems. Due to the high cost of labeling and maintenance, recent research has turned to online HD map construction using onboard sensor data, offering wider coverage and more timely updates for autonomous vehicles (AVs). However, the robustness of online map construction under adversarial conditions remains underexplored. In this paper, we present a systematic vulnerability analysis of online map construction models, which reveals that these models exhibit an inherent bias toward predicting symmetric road structures. In asymmetric scenes like forks or merges, this bias often causes the model to mistakenly predict a straight boundary that mirrors the opposite side. We demonstrate that this vulnerability persists in the real-world and can be reliably triggered by obstruction or targeted interference. Leveraging this vulnerability, we propose a novel two-stage attack framework capable of manipulating online constructed maps. First, our method identifies vulnerable asymmetric scenes along the victim AV's potential route. Then, we optimize the location and pattern of camera-blinding attacks and adversarial patch attacks. Evaluations on a public AD dataset demonstrate that our attacks can degrade mapping accuracy by up to 9.9% in average precision, render up to 44% of targeted routes unreachable, and increase unsafe planned trajectory rates—colliding with real-world road boundaries—by up to 27%. These attacks are also validated on a real-world testbed vehicle. We further analyze root causes of the symmetry bias, attributing them to training data imbalance, model architecture, and map element representation. Based on these findings, we propose asymmetric data fine-tuning as a targeted defense, which significantly improves model robustness. To the best of our knowledge, this study presents the first vulnerability assessment of online map construction models and introduces the first digital and physical attack against them.

Safety validation, which assesses the safety of an autonomous system's motion planning decisions, is critical for the safe deployment of autonomous vehicles. Existing input validation techniques from other machine learning domains, such as image classification, face unique challenges in motion planning due to its contextual properties, including complex inputs and one-to-many mapping. Furthermore, current output validation methods in autonomous driving primarily focus on open-loop trajectory prediction, which is ill-suited for the closed-loop nature of motion planning. We introduce REDOUBT, the first systematic safety validation framework for autonomous vehicle motion planning that employs a duo mechanism, simultaneously inspecting input distributions and output uncertainty. REDOUBT identifies previously overlooked unsafe modes arising from the interplay of In-Distribution/Out-of-Distribution (OOD) scenarios and certain/uncertain planning decisions. We develop specialized solutions for both OOD detection via latent flow matching and decision uncertainty estimation via an energy-based approach. Our extensive experiments demonstrate that both modules outperform existing approaches, under both open-loop and closed-loop evaluation settings. Our codes are available at: https://github.com/sgNicola/Redoubt.

From gimmicky IoT devices to self-driving cars, cyber-physical systems have become increasingly accessible to the masses. As these systems interact intimately with the physical world, failures in the systems can lead to severe, potentially life-threatening damage. Classical cyber-physical systems, such as aircraft flight control, employ dedicated redundancies for fault tolerance. However, a more flexible and cost-effective approach to redundancy is needed as cyber-physical systems become more versatile and expand to the consumer market. In this work, we explore the synthesis of redundancies for program functionalities with neural network models. The models are trained with the data from normal program executions and will be deployed to supplant the original program functionalities when failures occur. We have tested the prototype on representative cyber-physical systems, including ArduPilot and OpenPilot. The evaluation results indicate that the approach can synthesize redundancy models for both numerical and logical programs. We also demonstrate that the redundancy models can effectively avoid bugs and security vulnerabilities in the original programs.

Traditional industries rapidly transcend the time and place restrictions of the country according to the technology growth by leaps and bounds over the year. Regrettably, international cybercrime incidents simultaneously explode by 2,400 million from 2020 to 2021. Undoubtedly, the real-time incident response has become the primary subject of incident handling. In this article, we aim to propose lawful remote forensics mechanism for ensuring the optimal protection of potential evidence in stochastic and unpredictable transnational crime. Meanwhile, the entire process can be performed remotely and compliant with legal requirements, such as ISO/IEC and NIST regulations. Specifically, all the procedures can be retroactive based on the design of the chain of custody, which leads to the proof of evidence admissibility. Aside from the security essential confirmation by the formal tools Proverif, AVISPA, and Scyther, simulation results have demonstrated that remote forensics can fulfill the legal requirements and perform excellently in various incident scales.

Digitizing medical records facilitates the healthcare process. However, it can also cause serious security and privacy problems, which are the major concern in the Health Insurance Portability and Accountability Act (HIPAA). While various conventional encryption mechanisms can solve some aspects of these problems, they cannot address the illegal distribution of decrypted medical images, which violates the regulations defined in the HIPAA. To protect decrypted medical images from being illegally distributed by an authorized staff member, the model proposed in this paper provides a way to integrate several cryptographic mechanisms. In this model, the malicious staff member can be tracked by a watermarked clue. By combining several well-designed cryptographic mechanisms and developing a key management scheme to facilitate the interoperation among these mechanisms, the risk of illegal distribution can be reduced.

Existing secure communication mechanisms between set-top box and smart card in digital television broadcasting are unfair and inconvenient since the subscribers cannot receive programs for themselves on any set-top boxes owned. In this paper, a novel scheme for fair use is presented to receive programs for subscribers themselves on any set-top boxes. Moreover, the proposed scheme can prevent McCormac Hack and smart card cloning problems which are serious threats in digital television broadcasting.

The Health Insurance Portability and Accountability Act (HIPAA) privacy and security regulations are two crucial provisions in the protection of healthcare privacy. Privacy regulations create a principle to assure that patients have more control over their health information and set limits on the use and disclosure of health information. The security regulations stipulate the provisions implemented to guard data integrity, confidentiality, and availability. Undoubtedly, the cryptographic mechanisms are well defined to provide suitable solutions. In this paper, to comply with the HIPAA regulations, a flexible cryptographic key management solution is proposed to facilitate interoperations among the applied cryptographic mechanisms. In addition, case of consent exceptions intended to facilitate emergency applications and other possible exceptions can also be handled easily.

Proxy signature is a variant of digital signature, in which the proxy signer could sign message on behalf of the original signer. In 2007, Hu et al. introduced a mechanism into the proxy signature model to allow rebuilding the short-term proxy key in a very efficient way. The mechanism reflects the necessary while the content stated in the warrant has been changed. Unfortunately, the new mechanism solves the demand on changing warrant but introduce a new security issue that the genuine long-term secret key of the proxy signer will be exposed if one of the proxy keys is compromised. Therefore, the impersonation attack is possible in Hu et al.'s scheme. In this paper, the security weakness will be examined and remedied. The enhanced version still retains the merits of the original design and this Elliptic curve based scheme is definitely adaptive for the mobile environment to provide a security solution for the next century.

In 2005, Zhang et al. proposed a novel fair digital signature exchange protocol that can impose a penalty on the misbehaving party. However, we have discovered a weakness concerning the fairness aspect of the protocol which will occur when an initiator tries to exchange the signature of the same document to others again. That is, the latter exchange partner can obtain the initiator's valid signature with the help of the previous exchange partner without completing the protocol, and therefore the protocol is not fair enough. In this paper, the reason for the occurrence of the unfair exchange is discussed, and an improvement to remove the weakness is proposed.

A copyright management system for online trade of digital images is proposed in which low-resolution images with a visible mark are freely downloadable. An authorized user receives a package containing the details of the image he/she buys together with a key for mark removing. Using a downloaded client-end routine, the user removes the mark, reconstructs the high-quality image and embeds a robust fingerprint in the image simultaneously.

Portable communication systems (PCSs) provide a convenient means of communication; however, many problems arise relating to data security, user privacy, computational load, and communicational efficiency. To provide solutions for these problems, we introduce the concept of delegation into the wireless communication system. This new model makes our scheme an especially valuable improvement to portable communication systems.