Stories of cyber attacks are becoming a routine in which cyber attackers show new levels of intention by sophisticated attacks on networks. Unfortunately, cybercriminals have figured out profitable business models and they take advantage of the online anonymity. A serious situation that needs to improve for networks' defenders. Therefore, a paradigm shift is essential to the effectiveness of current techniques and practices. Since the majority of cyber incidents are human enabled, this shift requires expanding research to underexplored areas such as behavioral aspects of cybersecurity. It is more vital to focus on social and behavioral issues to improve the current situation. This paper is an effort to provide a review of relevant theories and principles, and gives insights including an interdisciplinary framework that combines behavioral cybersecurity, human factors, and modeling and simulation.

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In today's interconnected world of computer networks, there exists a need to provide secure and safe transactions through the use of firewalls, Intrusion Detection Systems (IDSs), encryption, authentication, and other hardware and software solutions. Many IDS variants exist which allow security managers and engineers to identify attack network packets primarily through the use of signature detection; i.e., the IDS "recognizes" attack packets due to their well-known "fingerprints" or signatures as those packets cross the network's gateway threshold. On the other hand, anomalybased ID systems determine what is normal traffic within a network and reports abnormal traffic behavior. We report the findings of our research in the area of anomaly-based intrusion detection systems using data-mining techniques described in section 3.3 to create a decision tree model of our network using the 1999 DARPA Intrusion Detection Evaluation data set. After the model was created, we gathered more data from our local campus network and ran the new data through the model.

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