ABSTRACT Optimizing routes in road networks is crucial for smooth transportation and economic progress. Different methods exist for finding the best routes, including genetic algorithms, particle swarm optimization, and simulated annealing. Ant Colony Optimization (ACO) stands out for its efficiency. In this study, we introduce a modified version called MACO, which considers accidents when determining optimal routes. Evaluating different ACO versions reveals differences in solution quality, runtime, and number of iterations. Performance metrics including maximum obtained solution, runtime, and iteration number were evaluated for each method. In Case 1, TACO, and AACO both achieved a maximum of 21 solutions from the available possible solution of 24, exhibiting run‐times of 0.4359 and 0.4575 s, respectively. Meanwhile, MACO attained a maximum of 22 solutions from available possible solution 24, in a runtime of 0.5345 s and 10 iterations. In the second scenario, TACO, AACO, and MACO achieved maximum solutions of 20 with obtained solutions of 15, 16, and 17, respectively. TACO demonstrated a runtime of 0.1853 s with 26 iterations, AACO ran in 0.1749 s with 22 iterations, and MACO completed in 0.5799 s with 15 iterations. These findings highlight the varying performance of the optimization methods and suggest MACO as a promising approach for balancing solution quality and computational efficiency in road network path optimization.

With the increasing prevalence of cyber threats, the demand for efficient and effective malware detection systems has reached unprecedented levels. This research paper presents a novel approach to detecting malware packets based on URL analysis, utilizing natural language processing (NLP) techniques. Traditional malware detection methods rely heavily on statistical approaches and anomaly detection techniques, which have inherent limitations in detecting complex and rapidly evolving malware. In contrast, our proposed approach harnesses the power of NLP to examine the payload of network traffic and identify malicious packets by analyzing specific text patterns found in the URLs in the payload. In this research, we achieved sparsity problems with TF-IDF vectorization and also demonstrated that our proposed approach, deploying the ROBERTa model in a real-world network, achieves exceptional detection rates while maintaining low false-positive rates, i.e., 2%, where as random forest 7.1 % and SVM 13.8%. It surpasses statistical methods and other NLP-based models in terms of malware packet detection. Compared to random forest (90.2% accuracy) and SVM (79.0% accuracy), which are powerful in classification, our ROBERTa-based approach achieves an impressive accuracy of 99.6 %. Moreover, our approach exhibits greater resilience against adversarial attacks as it does not rely on fixed signatures or patterns.

Computer vision is a rapidly advancing technology that relies heavily on camera sensors to provide input for Machine Learning (ML) models to make decisions. It is confirmed to play a critical role in various futuristic applications, such as advancements in self-driving vehicles, autonomous & target-tracking drones, parking assistance, and collision avoidance systems. However, with the increasing prevalence of hardware-level sensor hacking, even camera sensors are susceptible to being compromised. This experimental paper proposes the idea of sensor hacking against Machine Learning capabilities of vehic-ular Computer Vision (CV) using Electromagnetic Interference (EMI). A mid-range EMI intrusion device is developed to disrupt computer vision systems' accuracy and supervisory capabilities. The evaluation examines the impact of sensor hacking on camera sensors crucial to obstacle identification models reliant on live feeds, comparing decision-making capabilities with and without sensor tampering to assess the overall effect. Our results show that EMI significantly affects camera sensor performance, reducing accuracy and frame rates in machine learning-based object detection systems. These findings underscore the vulnerability of camera sensors to sensor hacking and highlight the need for improved security measures to safeguard against such attacks in computer vision systems.