{'fraction_non_alphanumeric': 0.027123483226266953, 'fraction_numerical': 0.0, 'mean_word_length': 4.817427385892116, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Practical In-Depth Analysis of IDS Alerts for Tracing and Identifying Potential Attackers on Darknet

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.