Hyeran Mun

Robot Operating System (ROS) is a middleware widely used in robotics development. ROS simplifies communication between nodes within a network so that robot developers are able to simply develop robot applications. However, the communication in ROS is not secure because no authentication and encryption are provided. An unauthorized node can send malicious messages causing malfunction of robot applications. ROS2, the next version of ROS, is built on top of an existing middleware called Data Distribution Service (DDS), which incorporates security features. Unlike ROS, the communication in ROS2 can be protected if the security features are enabled. However, even with secure channels, unauthorized nodes can still pose a threat. For example, nodes with revoked certificates may still send messages because the Certificate Revocation List (CRL) is not enabled by default in ROS2, meaning that certificates might not be properly checked. Additionally, cryptographic operations in ROS2, such as encryption and authentication, can cause high latency, which could affect the performance of time-sensitive applications. For this reason, some developers are likely to disable the security features to avoid this performance issue. In this paper, we present the attack model where an unauthorized node is created and it publishes malicious messages. Also, we design a novel method (named ROSID) for detecting unauthorized nodes based on their clock offsets. Since clock offsets can serve as unique identifiers for each device, any unauthorized node sending messages would have a distinct clock offset. ROSID can be integrated into ROS applications by adding an additional device (ROSIDNode) which passively receives all messages. Accordingly, ROSID does not require any modification of existing nodes. We evaluate ROSID with several devices to verify that clock offsets can be their unique identifiers. Our evaluation results show that ROSID achieves an F1-score of 0.9703 for the node identification based on clock offsets as a fingerprint. Also, we show that ROSID is able to detect the on-target attack and the on-others attack with low false positive rates and false negative rates.

This paper presents a novel enhancement in the Hybrid TESLA protocol, integrating the Generic Bootstrapping Architecture (GBA) to replace public-key cryptography in the synchronization phase. The proposed protocol securely establishes communication between IoT devices and application servers without transmitting sensitive credentials over the network by leveraging GBA’s symmetric encryption and session key generation. Utilizing the existing mobile network infrastructure, GBA ensures secure authentication, making the protocol highly suitable for resource-constrained IoT environments. A simulation analysis conducted using Java demonstrated successful synchronization between clients and servers, with previous GBA experimental results confirming the protocol’s efficiency in terms of authentication time and power consumption. The Hybrid TESLA protocol’s use of SHA-2 and SHA-3 hashing functions ensures minimal computational demands while maintaining robust cybersecurity. This integration expands Hybrid TESLA’s utility in metaverse environments, enabling secure, seamless interactions across mobile networks.

With the widespread adoption of smartphones over the past decade, mobile applications have become a primary target for malicious attacks, usually in the form of malware. Recent studies have leveraged artificial intelligence (AI) techniques for malware detection and classification. However, applying such approaches, particularly deep learning (DL) techniques, to mobile malware detection poses significant challenges. These challenges arise from the difficulty of collecting large quantities of mobile malware samples and the inherent class imbalance in the collected datasets. To tackle these issues and enhance the performance of machine learning (ML) and DL detection models, we propose novel detection models based on a generative adversarial network (GAN). Furthermore, our approach not only employs a conditional tabular GAN (CTGAN) for data augmentation to explore the impact of augmentation but also identifies the optimal multiplication factor for achieving the best results. The evaluation results demonstrate that the proposed data augmentation approach significantly improves the performance of mobile malware detection models, especially those based on DL. We have notably figured out that doubling the original dataset is sufficient to enhance the performance of ML models, whereas DL models require additional data to achieve optimal results. Hence, our proposed mechanism is an effective solution for improving mobile malware detection.

Digital Twin (DT) aims to seamlessly replicate physical objects or processes in virtual environments, garnering attention for supporting diverse intelligent management services such as monitoring, analysis, and control. Integration with technologies like artificial intelligence (AI), big data, edge computing, and the Internet of Things (IoT) has propelled the development of DTs. However, the DT may not only introduce new potential security flaws but also inherit existing vulnerabilities from the technologies they incorporate and interact with due to their integration with diverse technologies. Despite its growing impact, research addressing the security concerns of DTs remains insufficient and still in its early stages. In this article, we provide a comprehensive and in-depth review of the current state of DTs, focusing on their security aspects. We first depict an overview of DTs, including the definition of the DT and various DT applications, and then present the architecture for DTs. Subsequently, we conduct a detailed examination of security attacks and threats across the functional layers of the DT architecture. In contrast to the previous surveys on DTs, we derive security properties and security functional requirements (SFRs) based on the Common Criteria (CC) standard for mitigating these attacks and threats. We also introduce technologies that enable the achievement of SFRs with a future research perspective. By addressing these critical security aspects, our article enhances the security and trustworthiness of DTs, contributing to their safe and reliable deployment in various domains.

Artificial intelligence (AI) technologies have been applied to the Internet of Vehicles (IoV) to provide convenience services such as traffic flow prediction. However, concerns regarding privacy and security are on the rise as huge amounts of data are aggregated to form large models (LMs). Although federated learning (FL), which trains and updates a model without sharing the actual datasets, has been intensively researched to prevent privacy breaches, there are still potential security threats like a single point of failure and intentional tampering with malicious data. This is because of the vulnerability of a central curator and a lack of authentication. As participants, they (i.e., vehicles) may unintentionally update low-quality data caused by poor wireless connectivity, unstable availability, and insufficient training datasets. They may also intentionally update unreliable data to carry out poisoning attacks. The divergence among local models, trained on non-independent and identically distributed ( non-IID) data, can slow convergence and diminish model accuracy when these models are aggregated. Therefore, it is important to carefully select trustworthy participants. In this paper, we propose a new reliable and secure federated learning for IoV based on decentralized blockchain and reputation management. To cope with a single point of failure, injection of malicious data, and lack of authentication while ensuring privacy and traceability, our scheme combines blockchain and a lightweight digital signature. Moreover, we employ the concept of the reputation of vehicles to select suitable participants with reliability, ultimately improving accuracy. Security analysis results, including comparisons with previous works, prove that the proposed scheme can address security concerns. The results of performance evaluations demonstrate the effectiveness of our proposed scheme.

As autonomous driving technology continues to develop, communication for vehicles is becoming more important to provide an efficient and secure driving environment. Currently, vehicle-to-everything (V2X) based on fifth-generation (5G) is the only solution to meet the demanding requirements of self-driving cars in terms of ultra-low latency and ultra-high reliable connectivity under high mobility conditions, earning this technology more attention. Secure communication for vehicle-to-vehicle based on 5G (5G-V2V) is essential because vehicles decide how to maneuver and perceive situations based on the data exchanged. In this paper, we not only propose a new architecture for V2X based on 5G (5G-V2X) by utilizing network slicing (NS) to guarantee different features of V2X services but also analyze security requirements according to V2X service type. We also propose a secure V2V communication scheme based on 5G that satisfies these requirements. The security evaluation demonstrates that our scheme is secure against attacks on a V2V environment via both security proof and verification tool ProVerif. Through the performance evaluation, we prove that our scheme is more effective than the existing schemes while meeting end-to-end delay requirements for autonomous driving.

As automotive embedded systems comprised of electronic control units (ECUs) connected via a controller area network (CAN) have continued to develop, the volume of information these systems are required to handle has also rapidly increased. Cyber attacks targeting vulnerable points of automotive embedded systems in particular are on the rise to hinder normal operation of a vehicle. However, adding security mechanisms to defend against attacks cannot neglect timing requirements in terms of vehicle safety. This is because it may lead to a violation of automobile safety. In short, both sides of this issue must be addressed from the outset of the system design stage to provide optimal security and safety. As a response to this pressing issue, we propose a novel and efficient scheme. The design optimization during the system design phase not only ensures all the real-time applications are executed within their deadline but also reduces the number of transmitted messages over the CAN bus. After optimization, we apply a hash message authentication code (HMAC) to specific messages, providing secure communication between ECUs and protecting against cyber attacks. Security analysis and experimental results prove that the proposed scheme can counter attacks on the CAN bus while meeting timing requirements. Therefore, our proposed scheme is effective in satisfying improvement of both safety and security.