2019/12/31 by Constantinos Patsakis, Fran Casino
Computer Science · #Advanced Malware Detection Techniques #Adversarial system #Blacklisting #Botnet #Domain (mathematical analysis) #Identification (biology) #Malware #Network Security and Intrusion Detection #Obfuscation #Probabilistic logic #Randomness #Spam and Phishing Detection #cs.CR
paper · pdf · doi:10.1016/j.jisa.2020.102725
published as Journal of Information Security and Applications, Volume 58, 2021
openalex created_date 2019/12/26 · openalex publication_date 2021/01/14 · arxiv created 2021/01/22 · arxiv updated 2021/01/25 · openalex updated_date 2026/08/05
Nowadays, malware campaigns have reached a high level of sophistication, thanks to the use of cryptography and covert communication channels over traditional protocols and services. In this regard, a typical approach to evade botnet identification and takedown mechanisms is the use of domain fluxing through the use of Domain Generation Algorithms (DGAs). These algorithms produce an overwhelming amount of domain names that the infected device tries to communicate with to find the Command and Control server, yet only a small fragment of them is actually registered. Due to the high number of domain names, the blacklisting approach is rendered useless. Therefore, the botmaster may pivot the control dynamically and hinder botnet detection mechanisms. To counter this problem, many security mechanisms result in solutions that try to identify domains from a DGA based on the randomness of their name. In this work, we explore hard to detect families of DGAs, as they are constructed to bypass these mechanisms. More precisely, they are based on the use of dictionaries so the domains seem to be user-generated. Therefore, the corresponding generated domains pass many filters that look for, e.g. high entropy strings. To address this challenge, we propose an accurate and efficient probabilistic approach to detect them. We test and validate the proposed solution through extensive experiments with a sound dataset containing all the wordlist-based DGA families that exhibit this behaviour and compare it with other state-of-the-art methods, practically showing the efficacy and prevalence of our proposal.