This page has only limited features, please log in for full access.
The darknet (i.e., a set of unused IP addresses) is a very useful solution for observing the global trends of cyber threats and analyzing attack activities on the Internet. Since the darknet is not connected with real systems, in most cases, the incoming packets on the darknet (‘the darknet traffic’) do not contain a payload. This means that we are unable to get real malware from the darknet traffic. This situation makes it difficult for security experts (e.g., academic researchers, engineers, operators, etc.) to identify whether the source hosts of the darknet traffic are infected by real malware or not. In this paper, we present the overall procedure of the in-depth analysis between the darknet traffic and IDS alerts using real data collected at the Science and Technology Cyber Security Center (S&T CSC) in Korea and provide the detailed in-depth analysis results. The ultimate goal of this paper is to provide practical experience, insight and know-how to security experts so that they are able to identify and trace the root cause of the darknet traffic. The experimental results show that correlation analysis between the darknet traffic and IDS alerts is very useful to discover potential attack hosts, especially internal hosts, and to find out what kinds of malware infected them.
Jungsuk Song; Younsu Lee; Jang-Won Choi; Joon-Min Gil; Jaekyung Han; Sang-Soo Choi. Practical In-Depth Analysis of IDS Alerts for Tracing and Identifying Potential Attackers on Darknet. Sustainability 2017, 9, 262 .
AMA StyleJungsuk Song, Younsu Lee, Jang-Won Choi, Joon-Min Gil, Jaekyung Han, Sang-Soo Choi. Practical In-Depth Analysis of IDS Alerts for Tracing and Identifying Potential Attackers on Darknet. Sustainability. 2017; 9 (2):262.
Chicago/Turabian StyleJungsuk Song; Younsu Lee; Jang-Won Choi; Joon-Min Gil; Jaekyung Han; Sang-Soo Choi. 2017. "Practical In-Depth Analysis of IDS Alerts for Tracing and Identifying Potential Attackers on Darknet." Sustainability 9, no. 2: 262.
This special issue presents state-of-the-art research in the area of multimedia applications for Intelligent Fusion Computing. Multimedia data are used more and more widely and frequently in our daily life, e.g., 3D computer games, multimedia search, videoconferencing, visual telephone, YouTube, IPTV, IP Cam, video surveillance, etc. Recently, various intelligent fusion computing techniques have been invented, such as neural networks, fuzzy theory, evolutionary algorithm, and machine learning, which bring significant performance improvements to practical applications. For multimedia computing, these techniques can push applications closer to users’ semantic meanings, e.g., the semantic based multimedia retrieval and specific human action detection, etc. The goals of this special issue are to establish multimedia applications for intelligence fusion computing as a more prominent field of study and industry, and to exchange the latest discoveries on the strategic intelligence fusion that ...
Soo-Kyun Kim; Fan Liu; Sang-Soo Choi. Multimedia applications for intelligent fusion computing. Multimedia Tools and Applications 2015, 74, 3273 -3276.
AMA StyleSoo-Kyun Kim, Fan Liu, Sang-Soo Choi. Multimedia applications for intelligent fusion computing. Multimedia Tools and Applications. 2015; 74 (10):3273-3276.
Chicago/Turabian StyleSoo-Kyun Kim; Fan Liu; Sang-Soo Choi. 2015. "Multimedia applications for intelligent fusion computing." Multimedia Tools and Applications 74, no. 10: 3273-3276.