Zero-Day Vulnerabilities & Advanced Persistent Threats

Zero-day vulnerabilities are critical cybersecurity risks arising from undiscovered software flaws that attackers can exploit before developers have a chance to address them.Traditional detection methods, such as signaturebased approaches, have struggled to handle these threats effectively.Intrusion Detection Systems (IDS) are commonly used in enterprise networks, cloud security, and industrial control systems to monitor suspicious activities, while heuristic analysis aids in malware detection and network traffic analysis by identifying new threats based on behavioral patterns.However, these traditional methods rely heavily on known attack patterns, tend to be reactive, often produce high false positive rates, and may struggle to adapt to new attack techniques.Existing research has explored anomaly detection in network traffic, but many solutions lack the level of detail needed to detect low frequency anomalies associated with zero-day exploits, underscoring the need for a more robust detection model.This study proposes a hybrid model that integrates heavy-hitter analysis and graph-based techniques to enhance zero-day vulnerability detection.Heavy-hitter analysis identifies significant traffic patterns by monitoring high-volume data flows, potentially indicative of anomalous behaviors that align with zero-day exploits.Meanwhile, graph-based techniques map interactions between network entities, offering insights into the relationships and dependencies within traffic patterns, which helps uncover complex attack patterns and deviations from normal behavior.By combining these two techniques, the hybrid model improves detection accuracy, specifically within Software-Defined Networks (SDNs), by proactively identifying unusual behaviors, including subtle and low-frequency attacks.The hybrid model also utilizes machine learning to continuously adapt and improve detection accuracy, reducing false positives and enhancing its responsiveness to emerging threats.The model's adaptability within SDNs further strengthens its ability to detect complex, low-frequency attacks, making it a promising solution for addressing zero-day vulnerabilities.