Browsing by Author "David, Gabriel Henrique Ribeiro"
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- Anomaly behavior detection in webPublication . David, Gabriel Henrique Ribeiro; Marreiros, Maria Goreti CarvalhoIn the domain of web application development, JavaScript plays an important role in enhancing the productivity and interactivity of web applications. However, its flexibility and dynamic nature also introduce potential security risks. Attackers can exploit vulnerabilities in JavaScript to perform various malicious activities, such as data theft, injection attacks, and unauthorized web modifications, including data tampering. This work introduces a novel approach to enhancing the security of web applications by focusing on malicious behavior executed through client-side JavaScript. The core objective of this research is to develop a model capable of identifying anomalous behaviors caused by third-party scripts on web pages. To this end, the research conducts a comparative analysis of four distinct models: One-class SVM, Isolation Forest, Local Outlier Factor, and Autoencoders. To identify the most effective solution, these models are evaluated based on specific performance metrics, including Area Under the Curve (AUC) and F-score. The selected model is used to pinpoint irregularities indicative of potential security breaches or malicious activities. This research significantly advances the field of web application security by providing actionable insights to enhance real-time response capabilities. By addressing the growing threat posed by malicious JavaScript, this work contributes to the development of more robust security measures. The dissertation employs a multi-faceted methodology to ensure a comprehensive approach. Initially, a systematic review methodology is used for a structured and unbiased literature analysis, providing a thorough understanding of the current state of the art. The CRISP-DM framework is adopted for the development phase, facilitating continuous adaptation in response to evolving insights. A Comparative Analysis methodology rigorously evaluates different anomaly detection algorithms, ensuring their possible practical applicability in real-world scenarios. The findings demonstrate that the chosen model can effectively identify anomalies with high accuracy and minimal false positives. This research highlights the importance of integrating anomaly detection with existing Data Loss Prevention (DLP) solutions to monitor and protect sensitive data against cyber-attacks.