The escalation of security attacks has led to a growing complexity and posed serious challenges for traditional IDS paradigms to securing and sustaining a resilient network infrastructure. Current solutions are generally inefficient at optimizing detection parameters, cannot detect attacks that have previously not been encountered and do not offer clear decision making processes. In this work, a framework for optimization of secure data protection using quantum computing is proposed which combines machine learning, anomaly detection, explainable AI and quantum computing inspired optimization techniques in a single cybersecurity framework to overcome these limitations. Selected network traffic from CICIDS2017 data set is used in the system to detect various types of attacks such as Denial-of-Service (DoS), Distributed Denial-of-Service (DDoS), PortScan and WebAttack. The multi-class intrusion detection uses an XGBoost (Extreme Gradient Boosted) Classifier to learn discriminative patterns from network flow features. The Quantum Approximate Optimization Algorithm (QAOA) is integrated to optimize certain model parameters (learning rate, tree depth and decision thresholds) that can enhance detection performance. Moreover, an Isolation Forest model is run concurrently to detect zero day and unknown anomalies not found in the training set. To enhance the interpretability, SHAP-based explainability is incorporated to measure the network's contribution to each prediction by its features. All the framework is deployed via Flask and is displayed on an interactive dashboard that shows attack classifications, threat risk scores, optimized configurations and explanatory insights. The hybrid architecture proposed shows a novel combination of classical artificial intelligence and quantum optimization techniques to create an adaptive, explainable, and intelligent cybersecurity solution.
M. Anusha, M. Neha, PG Student et al.· 2026 International Conferenc...· 0 citations
The evolving complexity of urban environments and the effectiveness of traditional CCTV surveillance is making it increasingly difficult to ensure public safety, effective crowd management, crime prevention, and workplace security solutions. However, the traditional approach to surveillance is largely manual, leading to late reactions, missed events, and scalability issues. The intelligent surveillance system based on the YOLOv8 object detection algorithm is designed to enhance workplace safety, prevent crimes, manage crowds, and achieve face recognition in real time within a single AI platform. This paper introduces the concept of an intelligent surveillance framework that combines real-time face recognition, workplace safety monitoring, crowd management, and crime prevention through the use of YOLOv8 object detection algorithms within a single AI-driven solution. The framework employs the YOLOv8 algorithm for object detection, identifying people, weapons, suspicious activities, abandoned objects, and workplace safety violations, and issuing automatic alerts to facilitate swift decision-making. The proposed framework provides an integrated platform of multiple surveillance functionalities as opposed to the existing surveillance systems, where each surveillance task is monitored separately, which provides overall situational awareness using the existing CCTV. The model was trained with surveillance images annotated and tested with Precision, Recall, F1-score, Accuracy, and mAP@0.5. An overall detection accuracy of 92.4%, a precision of 92.4%, a recall of 89.7%, an F1-score of 91.0%, and an mAP@0.5 of 93.2% have been achieved during experimental evaluation. Moreover, the framework's average inference latency is 18ms per frame, which guarantees that it can be used in real-time surveillance applications without compromising the accuracy of its detection results when deployed in various surveillance environments. The proposed system is versatile and feasible for implementation in smart city systems, transportation hubs, industrial production sites, and various organizational environments, and can enable smart surveillance by merging multiple security functions into a single framework based on the YOLOv8 object detection model.
M. Anusha, N. Prashanth, T. Swetha et al.· 2026 International Conferenc...· 0 citations