Filtering spam with behavioral blacklisting
By Anirudh Ramachandran, N. Feamster, Santosh Srinivas Vempala. Conference on Computer and Communications Security, 2007
302 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems
View Paper: https://www.semanticscholar.org/paper/c1c25b67fc5cad3f584167be435957d626b1467e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Battista Biggio
- Neil Zhenqiang Gong
- Fabio Roli
- Blaine Nelson
- Binghui Wang
- Prateek Mittal
- Giorgio Fumera
- Bo Li
In Computer Networks and Communications
- Neil Zhenqiang Gong
- Binghui Wang
- Blaine Nelson
- Fabio Roli
- Battista Biggio
- Gang Wang
- Prateek Mittal
- Benjamin I. P. Rubinstein
In Information Systems
- Neil Zhenqiang Gong
- Binghui Wang
- Gang Wang
- Blaine Nelson
- Prateek Mittal
- Kurt Thomas
- Benjamin I. P. Rubinstein
- Peng Gao
Citations in the corpus
- ANTIDOTE: understanding and defending against poisoning of anomaly detectors (2009, background): “…Huang2 Anthony D. Joseph1,2 Shing-hon Lau1 Satish Rao1 Nina Taft2 J. D. Tygar1 1Computer Science Division, University of California, Berkeley 2Intel Labs Berkeley ABSTRACT Statistical machine learning techniques have recently garnered increased popularity as a means to improve network design…”
- SybilFuse: Combining Local Attributes with Global Structure to Perform Robust Sybil Detection (2018, background): “These approaches span a large category of schemes, including blacklisting [24], whitelisting [25], URL filtering [7], as well as various machine learning methods, such as Bayesian reasoning, Support Vector Machines, and clustering [26]–[28].”
- Behind Phishing: An Examination of Phisher Modi Operandi (2008, unknown): “Thus, techniques to defend against spam would help alleviating phishing as well, including those of Ramachandran et al. [ 22 ].”
- Suspended accounts in retrospect: an analysis of twitter spam (2011, background): “As previous research has shown, existing blacklists are too slow at identifying threats appearing on social networks, as well as often inaccurate with respect to both false positives and negatives [6, 15, 16].”
- BotSniffer: Detecting Botnet Command and Control Channels in Network Traffic (2008, methodology): “We describe each BotSniffer component in the following sections.”
- A Survey on Machine Learning Techniques for Cyber Security in the Last Decade (2020, methodology): “Collaborative filtering [284], machine learning [285], and blacklisting [286] methods are also used to achieve the same results.”
- Man vs. Machine: Practical Adversarial Detection of Malicious Crowdsourcing Workers (2014, background): “email spam [35, 36], network intrusion attacks [22, 54], and Internet worms [29].”
- Aiding the Detection of Fake Accounts in Large Scale Social Online Services (2012, background): “It relies on a strong assumption that requires balanced graphs [56] and costsO(sn logn) (s is the number of seeds, referred to as ticket sources).”
- Behavior of Machine Learning Algorithms in Adversarial Environments (2010, methodology): “For instance, the IP-based approach of Ramachandran et al. [2007] uses a technique they call behavioral blacklisting to identify (and blacklist) likely sources of spam.”
- Combating Attacks and Abuse in Large Online Communities (2016, background): “email spam [179, 182], network intrusion attacks [129, 250] , and Internet worms [164].”