A Survey, Taxonomy, and Analysis of Network Security Visualization Techniques

Network security visualization is a relatively new field and is quickly gaining momentum. Network security visualization allows the display and projection of the network or system data, in hope to efficiently monitor and protect the system from any intrusions or possible attacks. Intrusions and attacks are constantly continuing to increase in number, size, and complexity. Textually reading through log files or other textual sources is currently insufficient to secure a network or system. Using graphical visualization, security information is presented visually, and not only by text. Without network security visualization, reading through log files or other textual sources is an endless and aggravating task for network security analysts. Visualization provides a method of displaying large volume of information in a relatively small space. It also makes patterns easier to detect, recognize, and analyze. This can help security experts to detect problems that may otherwise be missed in reading text based log files. Network security visualization has become an active research field in the past six years and a large number of visualization techniques have been proposed. A comprehensive analysis of the existing techniques is needed to help network security designers make informed decisions about the appropriate visualization techniques under various circumstances. Moreover, a taxonomy of the existing visualization techniques is needed to classify the existing network security visualization techniques and present a high level overview of the field. In this thesis, the author surveyed the field of network security visualization. Specifically, the author analyzed the network security visualization techniques from the perspective of data model, visual primitives, security analysis tasks, user interaction, and other design issues. Various statistics were generated from the literatures. Based on this analysis, the author has attempted to generate useful guidelines and principles for designing effective network security visualization techniques. The author also proposed a taxonomy for the security visualization techniques. To the author’s knowledge, this is the first attempt to generate a taxonomy for network security visualization. Finally, the author evaluated the existing network security visualization techniques and discussed their characteristics and limitations. For future research, the author also discussed some open research problems in this field. This research is a step towards a thorough analysis of the problem space and the solution space in network security visualization

Sonification of Network Traffic Flow for Monitoring and Situational Awareness

Maintaining situational awareness of what is happening within a network is challenging, not least because the behaviour happens within computers and communications networks, but also because data traffic speeds and volumes are beyond human ability to process. Visualisation is widely used to present information about the dynamics of network traffic dynamics. Although it provides operators with an overall view and specific information about particular traffic or attacks on the network, it often fails to represent the events in an understandable way. Visualisations require visual attention and so are not well suited to continuous monitoring scenarios in which network administrators must carry out other tasks. Situational awareness is critical and essential for decision-making in the domain of computer network monitoring where it is vital to be able to identify and recognize network environment behaviours.Here we present SoNSTAR (Sonification of Networks for SiTuational AwaReness), a real-time sonification system to be used in the monitoring of computer networks to support the situational awareness of network administrators. SoNSTAR provides an auditory representation of all the TCP/IP protocol traffic within a network based on the different traffic flows between between network hosts. SoNSTAR raises situational awareness levels for computer network defence by allowing operators to achieve better understanding and performance while imposing less workload compared to visual techniques. SoNSTAR identifies the features of network traffic flows by inspecting the status flags of TCP/IP packet headers and mapping traffic events to recorded sounds to generate a soundscape representing the real-time status of the network traffic environment. Listening to the soundscape allows the administrator to recognise anomalous behaviour quickly and without having to continuously watch a computer screen.Comment: 17 pages, 7 figures plus supplemental material in Github repositor

Autonomic Parameter Tuning of Anomaly-Based IDSs: an SSH Case Study

Anomaly-based intrusion detection systems classify network traffic instances by comparing them with a model of the normal network behavior. To be effective, such systems are expected to precisely detect intrusions (high true positive rate) while limiting the number of false alarms (low false positive rate). However, there exists a natural trade-off between detecting all anomalies (at the expense of raising alarms too often), and missing anomalies (but not issuing any false alarms). The parameters of a detection system play a central role in this trade-off, since they determine how responsive the system is to an intrusion attempt. Despite the importance of properly tuning the system parameters, the literature has put little emphasis on the topic, and the task of adjusting such parameters is usually left to the expertise of the system manager or expert IT personnel. In this paper, we present an autonomic approach for tuning the parameters of anomaly-based intrusion detection systems in case of SSH traffic. We propose a procedure that aims to automatically tune the system parameters and, by doing so, to optimize the system performance. We validate our approach by testing it on a flow-based probabilistic detection system for the detection of SSH attacks

Spatiotemporal Patterns and Predictability of Cyberattacks

Y.C.L. was supported by Air Force Office of Scientific Research (AFOSR) under grant no. FA9550-10-1-0083 and Army Research Office (ARO) under grant no. W911NF-14-1-0504. S.X. was supported by Army Research Office (ARO) under grant no. W911NF-13-1-0141. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD