DATA COLLECTION FRAMEWORK AND MACHINE LEARNING ALGORITHMS FOR THE ANALYSIS OF CYBER SECURITY ATTACKS

• Other Authors: Calvert, Chad (author)
• Format: Others
• Language: English
• Published: Florida Atlantic University
• Subjects: Machine learning; Algorithms; Anomaly detection (Computer security); Intrusion detection systems (Computer security); Big data
• Online Access: http://purl.flvc.org/fau/fd/FA00013289

The integrity of network communications is constantly being challenged by more sophisticated intrusion techniques. Attackers are shifting to stealthier and more complex forms of attacks in an attempt to bypass known mitigation strategies. Also, many detection methods for popular network attacks have been developed using outdated or non-representative attack data. To effectively develop modern detection methodologies, there exists a need to acquire data that can fully encompass the behaviors of persistent and emerging threats. When collecting modern day network traffic for intrusion detection, substantial amounts of traffic can be collected, much of which consists of relatively few attack instances as compared to normal traffic. This skewed distribution between normal and attack data can lead to high levels of class imbalance. Machine learning techniques can be used to aid in attack detection, but large levels of imbalance between normal (majority) and attack (minority) instances can lead to inaccurate detection results. === Includes bibliography. === Dissertation (Ph.D.)--Florida Atlantic University, 2019. === FAU Electronic Theses and Dissertations Collection