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2020-10-06
Kalwar, Abhishek, Bhuyan, Monowar H., Bhattacharyya, Dhruba K., Kadobayashi, Youki, Elmroth, Erik, Kalita, Jugal K..  2019.  TVis: A Light-weight Traffic Visualization System for DDoS Detection. 2019 14th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP). :1—6.

With rapid growth of network size and complexity, network defenders are facing more challenges in protecting networked computers and other devices from acute attacks. Traffic visualization is an essential element in an anomaly detection system for visual observations and detection of distributed DoS attacks. This paper presents an interactive visualization system called TVis, proposed to detect both low-rate and highrate DDoS attacks using Heron's triangle-area mapping. TVis allows network defenders to identify and investigate anomalies in internal and external network traffic at both online and offline modes. We model the network traffic as an undirected graph and compute triangle-area map based on incidences at each vertex for each 5 seconds time window. The system triggers an alarm iff the system finds an area of the mapped triangle beyond the dynamic threshold. TVis performs well for both low-rate and high-rate DDoS detection in comparison to its competitors.

2018-04-02
Barrere, M., Steiner, R. V., Mohsen, R., Lupu, E. C..  2017.  Tracking the Bad Guys: An Efficient Forensic Methodology to Trace Multi-Step Attacks Using Core Attack Graphs. 2017 13th International Conference on Network and Service Management (CNSM). :1–7.

In this paper, we describe an efficient methodology to guide investigators during network forensic analysis. To this end, we introduce the concept of core attack graph, a compact representation of the main routes an attacker can take towards specific network targets. Such compactness allows forensic investigators to focus their efforts on critical nodes that are more likely to be part of attack paths, thus reducing the overall number of nodes (devices, network privileges) that need to be examined. Nevertheless, core graphs also allow investigators to hierarchically explore the graph in order to retrieve different levels of summarised information. We have evaluated our approach over different network topologies varying parameters such as network size, density, and forensic evaluation threshold. Our results demonstrate that we can achieve the same level of accuracy provided by standard logical attack graphs while significantly reducing the exploration rate of the network.