Biblio
Autonomous vehicles (AVs) are capable of making driving decisions autonomously using multiple sensors and a complex autonomous driving (AD) software. However, AVs introduce numerous unique security challenges that have the potential to create safety consequences on the road. Security mechanisms require a benchmark suite and an evaluation framework to generate comparable results. Unfortunately, AVs lack a proper benchmarking framework to evaluate the attack and defense mechanisms and quantify the safety measures. This paper introduces BenchAV – a security benchmark suite and evaluation framework for AVs to address current limitations and pressing challenges of AD security. The benchmark suite contains 12 security and performance metrics, and an evaluation framework that automates the metric collection process using Carla simulator and Robot Operating System (ROS).
Cyber-Physical Power Systems (CPPSs) currently face an increasing number of security attacks and lack methods for optimal proactive security decisions to defend the attacks. This paper proposed an optimal defensive method based on game theory to minimize the system performance deterioration of CPPSs under cyberspace attacks. The reinforcement learning algorithmic solution is used to obtain the Nash equilibrium and a set of metrics of system vulnerabilities are adopted to quantify the cost of defense against cyber-attacks. The minimax-Q algorithm is utilized to obtain the optimal defense strategy without the availability of the attacker's information. The proposed solution is assessed through experiments based on a realistic power generation microsystem testbed and the numerical results confirmed its effectiveness.
Internet of Things (IoT) is flourishing in several application areas, such as smart cities, smart factories, smart homes, smart healthcare, etc. With the adoption of IoT in critical scenarios, it is crucial to investigate its security aspects. All the layers of IoT are vulnerable to severely disruptive attacks. However, the attacks in IoT Network layer have a high impact on communication between the connected objects. Routing in most of the IoT networks is carried out by IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL). RPL-based IoT offers limited protection against routing attacks. A trust-based approach for routing security is suitable to be integrated with IoT systems due to the resource-constrained nature of devices. This research proposes a trust-based secure routing protocol to provide security against packet dropping attacks in RPL-based IoT networks. IoT networks are dynamic and consist of both static and mobile nodes. Hence the chosen trust metrics in the proposed method also include the mobility-based metrics for trust evaluation. The proposed solution is integrated into RPL as a modified objective function, and the results are compared with the default RPL objective function, MRHOF. The analysis and evaluation of the proposed protocol indicate its efficacy and adaptability in a mobile IoT environment.
Software developers can use diverse techniques and tools to reduce the number of vulnerabilities, but the effectiveness of existing solutions in real projects is questionable. For example, Static Analysis Tools (SATs) report potential vulnerabilities by analyzing code patterns, and Software Metrics (SMs) can be used to predict vulnerabilities based on high-level characteristics of the code. In theory, both approaches can be applied from the early stages of the development process, but it is well known that they fail to detect critical vulnerabilities and raise a large number of false alarms. This paper studies the hypothesis of using Machine Learning (ML) to combine alerts from SATs with SMs to predict vulnerabilities in a large software project (under development for many years). In practice, we use four ML algorithms, alerts from two SATs, and a large number of SMs to predict whether a source code file is vulnerable or not (binary classification) and to predict the vulnerability category (multiclass classification). Results show that one can achieve either high precision or high recall, but not both at the same time. To understand the reason, we analyze and compare snippets of source code, demonstrating that vulnerable and non-vulnerable files share similar characteristics, making it hard to distinguish vulnerable from non-vulnerable code based on SAT alerts and SMs.