Algorithmic Segmentation and Behavioral Profiling for Ransomware Detection Using Temporal-Correlation Graphs
The rapid evolution of cyber threats has outpaced traditional detection methodologies, necessitating innovative approaches capable of addressing the adaptive and complex behaviors of modern adversaries. A novel framework was introduced, leveraging Temporal-Correlation Graphs to model the intricate relationships and temporal patterns inherent in malicious operations. The approach dynamically captured behavioral anomalies, offering a robust mechanism for distinguishing between benign and malicious activities in real-time scenarios. Extensive experiments demonstrated the framework's effectiveness across diverse ransomware families, with consistently high precision, recall, and overall detection accuracy. Comparative evaluations highlighted its better performance over traditional signature-based and heuristic methods, particularly in handling polymorphic and previously unseen ransomware variants. The architecture was designed with scalability and modularity in mind, ensuring compatibility with enterprise-scale environments while maintaining resource efficiency. Analysis of encryption speeds, anomaly patterns, and temporal correlations provided deeper insights into the operational strategies of ransomware, validating the framework's adaptability to evolving threats. The research contributes to advancing cybersecurity technologies by integrating dynamic graph analytics and machine learning for future innovations in threat detection. Results from this study underline the potential for transforming the way organizations detect and mitigate complex cyberattacks.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Minerva: A File-Based Ransomware Detector
Ransomware attacks have caused billions of dollars in damages in recent years, and are expected to cause billions more in the future. Consequently, significant effort has been devoted to ransomware detection and mitigati…
feature selectionRansomTrack: A Hybrid Behavioral Analysis Framework for Ransomware Detection
Ransomware poses a serious and fast-acting threat to critical systems, often encrypting files within seconds of execution. Research indicates that ransomware is the most reported cybercrime in terms of financial damage, …
The Naked Sun: Malicious Cooperation Between Benign-Looking Processes
Recent progress in machine learning has generated promising results in behavioral malware detection. Behavioral modeling identifies malicious processes via features derived by their runtime behavior. Behavioral features …
Behavioral Malware DetectionMalware DetectionWannaLaugh: A Configurable Ransomware Emulator -- Learning to Mimic Malicious Storage Traces
Ransomware, a fearsome and rapidly evolving cybersecurity threat, continues to inflict severe consequences on individuals and organizations worldwide. Traditional detection methods, reliant on static signatures and appli…
Multimodal Multi-Agent Ransomware Analysis Using AutoGen
Ransomware has become one of the most serious cybersecurity threats causing major financial losses and operational disruptions worldwide.Traditional detection methods such as static analysis, heuristic scanning and behav…