Spamming botnets: signatures and characteristics

By Yinglian Xie, Fang Yu, Kannan Achan, Rina Panigrahy, Geoff Hulten, Ivan Osipkov. Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, 2008

423 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/31d5f34dc933fa403b59a7c009f0a9628e193b3f

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Citations in the corpus

Detecting algorithmically generated malicious domain names (2010, methodology): “With regards to botnet detection, [14, 15] perform correlation of network activity in time and space at campus network edges, and Xie et al in [33] focus on detecting spamming botnets by developing regular expression based signatures from a dataset of spam URLs .”
Click Trajectories: End-to-End Analysis of the Spam Value Chain (2011, background): “These include botnets [13], [20], [56], Webmail spam [9], and IP prefix hijacking [45].”
Misinformation in Social Media: Definition, Manipulation, and Detection (2019, background): “For example, while the content of a post varies, researchers find a group of misinformation spreaders may embed URLs in their posts that are directing to the same target [53].”
Shady paths: leveraging surfing crowds to detect malicious web pages (2013, background): “We describe them in detail in the next section.”
Behavioral Clustering of HTTP-Based Malware and Signature Generation Using Malicious Network Traces (2010, background): “The vast majority of malware needs a network connection in order to perpetrate their malicious activities (e.g., sending spam, exfiltrating private data, downloading malware updates, etc.).”
Trafficking Fraudulent Accounts: The Role of the Underground Market in Twitter Spam and Abuse (2013, methodology): “Our approach for generating these expressions is similar to previous techniques for identifying spam emails based on URL patterns [32] or spam text templates [22, 23].”
@spam: the underground on 140 characters or less (2010, background): “blacklisting [23, 25, 27], and filtering on email contents [19].”
Heterogeneous Graph Neural Networks for Malicious Account Detection (2018, background): “[22] proposed “spam payload” and “spam server traffic” properties for generating high quality regular expression signatures.”
PREDATOR: Proactive Recognition and Elimination of Domain Abuse at Time-Of-Registration (2016, methodology): “A related approach is to use various lexical and host-based features of the URL for detection, but exclude Web page content [38, 39, 65].”
Detecting Spammers on Twitter (2010, background): “Consequently, a number of detection and combating strategies have been proposed [6, 16, 22, 29].”
Proactive Discovery of Phishing Related Domain Names (2012, methodology): “The variable part of regular expression is exploited to generate new URLs instead of only compare existing URLs to extracted patterns like in [26].”