The tremendous increase in usage and complexity of modern communication and network systems connected to the Internet, places demands upon security management to protect organisations' sensitive data and resources from malicious intrusion. Malicious attacks by intruders and hackers exploit flaws and weakness points in deployed systems through several sophisticated techniques that cannot be prevented by traditional measures, such as user authentication, access controls and firewalls. Consequently, automated detection and timely response systems are urgently needed to detect abnormal activities by monitoring network traffic and system events. Network Intrusion Detection Systems (NIDS) and Network Intrusion Prevention Systems (NIPS) are technologies that inspect traffic and diagnose system behaviour to provide improved attack protection. The current implementation of intrusion detection systems (commercial and open-source) lacks the scalability to support the massive increase in network speed, the emergence of new protocols and services. Multi-giga networks have become a standard installation posing the NIDS to be susceptible to resource exhaustion attacks. The research focuses on two distinct problems for the NIDS: missing alerts due to packet loss as a result of NIDS performance limitations; and the huge volumes of generated alerts by the NIDS overwhelming the security analyst which makes event observation tedious. A methodology for analysing alerts using a proposed framework for alert correlation has been presented to provide the security operator with a global view of the security perspective. Missed alerts are recovered implicitly using a contextual technique to detect multi-stage attack scenarios. This is based on the assumption that the most serious intrusions consist of relevant steps that temporally ordered. The pre- and post- condition approach is used to identify the logical relations among low level alerts. The alerts are aggregated, verified using vulnerability modelling, and correlated to construct multi-stage attacks. A number of algorithms have been proposed in this research to support the functionality of our framework including: alert correlation, alert aggregation and graph reduction. These algorithms have been implemented in a tool called Multi-stage Attack Recognition System (MARS) consisting of a collection of integrated components. The system has been evaluated using a series of experiments and using different data sets i.e. publicly available datasets and data sets collected using real-life experiments. The results show that our approach can effectively detect multi-stage attacks. The false positive rates are reduced due to implementation of the vulnerability and target host information.

The tremendous increase in usage and complexity of modern communication and network systems connected to the Internet, places demands upon security management to protect organisations¿ sensitive data and resources from malicious intrusion. Malicious attacks by intruders and hackers exploit flaws and weakness points in deployed systems through several sophisticated techniques that cannot be prevented by traditional measures, such as user authentication, access controls and firewalls. Consequently, automated detection and timely response systems are urgently needed to detect abnormal activities by monitoring network traffic and system events. Network Intrusion Detection Systems (NIDS) and Network Intrusion Prevention Systems (NIPS) are technologies that inspect traffic and diagnose system behaviour to provide improved attack protection. The current implementation of intrusion detection systems (commercial and open-source) lacks the scalability to support the massive increase in network speed, the emergence of new protocols and services. Multi-giga networks have become a standard installation posing the NIDS to be susceptible to resource exhaustion attacks. The research focuses on two distinct problems for the NIDS: missing alerts due to packet loss as a result of NIDS performance limitations; and the huge volumes of generated alerts by the NIDS overwhelming the security analyst which makes event observation tedious. A methodology for analysing alerts using a proposed framework for alert correlation has been presented to provide the security operator with a global view of the security perspective. Missed alerts are recovered implicitly using a contextual technique to detect multi-stage attack scenarios. This is based on the assumption that the most serious intrusions consist of relevant steps that temporally ordered. The pre- and post- condition approach is used to identify the logical relations among low level alerts. The alerts are aggregated, verified using vulnerability modelling, and correlated to construct multi-stage attacks. A number of algorithms have been proposed in this research to support the functionality of our framework including: alert correlation, alert aggregation and graph reduction. These algorithms have been implemented in a tool called Multi-stage Attack Recognition System (MARS) consisting of a collection of integrated components. The system has been evaluated using a series of experiments and using different data sets i.e. publicly available datasets and data sets collected using real-life experiments. The results show that our approach can effectively detect multi-stage attacks. The false positive rates are reduced due to implementation of the vulnerability and target host information.