Cyber Security Violence Detection Using ML AND DL

Abstract

The exponential growth in digital interactions and interconnectivity has rendered cyberspace increasingly vulnerable to sophisticated and evolving threats. Traditional security mechanisms, although foundational, often fall short in detecting and mitigating novel, zero-day, and complex intrusion patterns. This research addresses the limitations of existing Intrusion Detection Systems (IDS) by proposing an intelligent framework that leverages Machine Learning (ML) and Deep Learning (DL) methodologies for proactive cybersecurity violence detection. The system integrates state-of-the-art models such as Support Vector Machines (SVM), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and hybrid architectures to classify and respond to network anomalies and malicious behaviors in real-time.

The study begins with a comprehensive literature review encompassing recent advances in violence detection technologies, highlighting their merits and constraints in processing structured and unstructured data from varied sources such as surveillance feeds, network logs, and dark web content. Datasets like NSL-KDD and CICIDS2017 were employed for empirical validation. Key challenges tackled include data imbalance, false-positive minimization, real-time response capabilities, and attack-type classification. The proposed solution further integrates domain knowledge-driven feature engineering to optimize detection accuracy and scalability.

Experimentation reveals that combining ML classifiers with deep architectures significantly enhances detection precision and recall, particularly in identifying stealthy or previously unseen attack vectors such as Denial-of-Service (DoS), Remote-to-User (R2L), and User-to-Root (U2R) intrusions. An intuitive user interface built on Django, powered by MySQL databases and visual analytics, ensures seamless usability and actionable insights for security analysts.

Ultimately, this work contributes a modular, adaptive, and extensible framework that addresses pressing cybersecurity challenges with intelligent automation. Future enhancements aim to incorporate continual learning and incremental model updates to accommodate the dynamic nature of cyber threats while addressing ethical considerations and privacy concerns inherent to automated surveillance systems.