The ISC International Journal of Information Security

Abstract Autonomous vehicles must balance road-safety objectives with growing cybersecurity threats. In this paper, we present a reinforcement-learning framework that jointly optimizes driving performance and resilience to Denial-of-Service (DoS) attacks.The problem is formulated as a multi-objective Markov Decision Process that integrates a safety reward with a security reward, while the partial observability of attacks is captured via a Bayesian belief. A Proximal Policy Optimization (PPO) agent controls steering, throttle, and dedicated mitigation actions. The system is implemented in the CARLA simulator with camera and LiDAR inputs and evaluated on urban driving scenarios. Experimental results demonstrate that the agent sustains stable lane-keeping and target-speed performance, while substantially reducing collision-prone incidents and retaining more than 90 % of the nominal travel distance under attack scenarios. The framework outperforms the safety-only PPO baseline and a rule-based security countermeasure.

Abstract Federated learning enables collaborative model training without centralized data collection, but existing frameworks rely on a central server, introducing risks of single points of failure, adversarial manipulation, and privacy leakage. To address these challenges, we propose HashLearner, a secure decentralized learning framework that utilizes the HashGraph consensus protocol for model aggregation without trusted authorities. HashLearner introduces two key innovations: (i) a consensus-driven decentralized aggregation mechanism resilient to Byzantine adversaries, and (ii) a privacy-preserving shuffling strategy that mitigates gradient reconstruction and poisoning attacks. To handle heterogeneous data distributions, the framework further employs transfer learning–based personalization. The simulation results of HashLearner, tested on benchmark Kaggle datasets, demonstrate that the platform maintains high accuracy while significantly enhancing scalability, security, and privacy. These findings indicate that HashLearner provides a practical path toward scalable, privacy-preserving, and trustworthy decentralized federated learning. 

Abstract In this paper we present a new finite field-based public key cryptosystem(NETRU) which is a non-commutative variant of CTRU. The original CTRU is defined by the ring of polynomials in one variable over a finite field F2. This system works in the ring R = F2[x]=hxN 􀀀 1i and is already broken by some attacks such as linear algebra attack. We extend this system over finite fields Zp, where p is a prime (or prime power) and it operates over the non-commutative ring M = Mk(Zp)[T; x]=hXn 􀀀 Ikki, where M is a matrix ring of k by k matrices of polynomials in R = Zp[T; x]=hxn 􀀀1i. In the proposed NETRU, the encryption and decryption computations are non-commutative and hence the system is secure against linear algebra attack as lattice-based attacks. NETRU is designed based on the CTRU core and exhibits high levels of security with two-sided matrix multiplication.

Abstract GGH class of public-key cryptosystems relies on computational problems based on the closest vector problem (CVP) in lattices for their security. The subject of lattice based cryptography is very active and there have recently been new ideas that revolutionized the field. We present EEH, a GGH-Like public key cryptosystem based on the Eisenstein integers Z [ζ₃] where ζ3 is a primitive cube root of unity. EEH applies representations of polynomials to the GGH encryption scheme and we discuss its key size and parameters selection. We also provide theoretical and experimental data to compare the security and efficiency of EEH to GGH with comparable parameter sets and show that EEH is an improvement over GGH in terms of security and efficiency.

Abstract The Tor network is probably one of the most popular online anonymity systems in the world. It has been built based on the volunteer relays from all around the world. It has a strong scientific basis which is structured very well to work in low latency mode that makes it suitable for tasks such as web browsing. Despite the advantages, the low latency also makes Tor insecure against timing and traffic analysis attacks, which are the most dominant attacks on Tor network in recent past years. In this paper, first all kinds of attacks on Tor network will be classified and then timing and traffic analysis attacks will be described in more details. Then we present a new circuit scheduling for Tor network in order to preserve two properties, fairness and randomness. Both properties are trying to make pattern and timing analysis attacks more difficult and even in some cases impractical. Our scheduler distorts timing patterns and size of packets in a random way (randomness) without imposing artificial delays or paddings (fairness). Finally, by using our new scheduler, one of the most powerful attacks in this area is debilitated, and by it is shown that analyzing traffic patterns and size of packets will be more difficult to manage.