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Organizations such as government departments and financial institutionsprovide online service facilities accessible via an increasing number ofinternet connected devices which make their operational environment vulnerableto cyber attacks. Consequently, there is a need to have mechanisms in place todetect cyber security attacks in a timely manner. A variety of NetworkIntrusion Detection Systems (NIDS) have been proposed and can be categorizedinto signature-based NIDS and anomaly-based NIDS. The signature-based NIDS,which identify the misuse through scanning the activity signature against thelist of known attack activities, are criticized for their inability to identifynew attacks (never-before-seen attacks). Among anomaly-based NIDS, whichdeclare a connection anomalous if it expresses deviation from a trained model,the unsupervised learning algorithms circumvent this issue since they have theability to identify new attacks. In this study, we use an unsupervised learningalgorithm based on principal component analysis to detect cyber attacks. In thetraining phase, our approach has the advantage of also identifying outliers inthe training dataset. In the monitoring phase, our approach first identifiesthe affected dimensions and then calculates an anomaly score by aggregatingacross only those components that are affected by the anomalies. We explore theperformance of the algorithm via simulations and through two applications,namely to the UNSW-NB15 dataset recently released by the Australian Centre forCyber Security and to the well-known KDD'99 dataset. The algorithm is scalableto large datasets in both training and monitoring phases, and the results fromboth the simulated and real datasets show that the method has promise indetecting suspicious network activities.

Detection of cybersecurity attacks through analysis of web browsing activities using principal component analysis