Machine Learning
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Awarded Best Paper! - Scalable Centralized Bayesian Spam Mitigation with Bogofilter
LISA '04 Proceedings of the 18th USENIX conference on System administration
Fast statistical spam filter by approximate classifications
SIGMETRICS '06/Performance '06 Proceedings of the joint international conference on Measurement and modeling of computer systems
Understanding the network-level behavior of spammers
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
Multi-evidence, multi-criteria, lazy associative document classification
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Lazy Associative Classification
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Spam and the ongoing battle for the inbox
Communications of the ACM - Spam and the ongoing battle for the inbox
Workload models of spam and legitimate e-mails
Performance Evaluation
Proceedings of the 2007 conference on Applications, technologies, architectures, and protocols for computer communications
Anti-Spam Measures: Analysis and Design
Anti-Spam Measures: Analysis and Design
A distributed content independent method for spam detection
HotBots'07 Proceedings of the first conference on First Workshop on Hot Topics in Understanding Botnets
A case study of the rustock rootkit and spam bot
HotBots'07 Proceedings of the first conference on First Workshop on Hot Topics in Understanding Botnets
Characterizing Spam Traffic and Spammers
ICCIT '07 Proceedings of the 2007 International Conference on Convergence Information Technology
Exploiting network structure for proactive spam mitigation
SS'07 Proceedings of 16th USENIX Security Symposium on USENIX Security Symposium
Characterizing botnets from email spam records
LEET'08 Proceedings of the 1st Usenix Workshop on Large-Scale Exploits and Emergent Threats
Spamming botnets: signatures and characteristics
Proceedings of the ACM SIGCOMM 2008 conference on Data communication
Thwarting E-mail Spam Laundering
ACM Transactions on Information and System Security (TISSEC)
SS'08 Proceedings of the 17th conference on Security symposium
Inferring Spammers in the Network Core
PAM '09 Proceedings of the 10th International Conference on Passive and Active Network Measurement
Studying spamming botnets using Botlab
NSDI'09 Proceedings of the 6th USENIX symposium on Networked systems design and implementation
Detecting Spam at the Network Level
EUNICE '09 Proceedings of the 15th Open European Summer School and IFIP TC6.6 Workshop on The Internet of the Future
Modern Information Retrieval
Detecting spammers with SNARE: spatio-temporal network-level automatic reputation engine
SSYM'09 Proceedings of the 18th conference on USENIX security symposium
Spam classification using supervised learning techniques
Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in India
Demand-driven tag recommendation
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Can network characteristics detect spam effectively in a stand-alone enterprise?
PAM'11 Proceedings of the 12th international conference on Passive and active measurement
Associative tag recommendation exploiting multiple textual features
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
An empirical study of behavioral characteristics of spammers: Findings and implications
Computer Communications
Blocking spam by separating end-user machines from legitimate mail server machines
Proceedings of the 8th Annual Collaboration, Electronic messaging, Anti-Abuse and Spam Conference
Computer Networks: The International Journal of Computer and Telecommunications Networking
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Despite the large variety and wide adoption of different techniques to detect and filter unsolicited messages (spams), the total amount of such messages over the Internet remains very large. Some reports point out that around 80% of all emails are spams. As a consequence, significant amounts of network resources are still wasted as filtering strategies are usually performed only at the email destination server. Moreover, a considerable part of these unsolicited messages is sent by users who are unaware of their spamming activity and may thus inadvertently be classified as spammers. In this case, these oblivious users act as spambots, i.e., members of a spamming botnet. This paper proposes a new method for detecting spammers at the source network, whether they are individual malicious users or oblivious members of a spamming botnet. Our method, called SpaDeS, is based on a supervised classification technique and relies only on network-level metrics, thus not requiring inspection of message content. We evaluate SpaDeS using real datasets collected from a Brazilian broadband ISP. Our results show that our method is quite effective, correctly classifying the vast majority (87%) of the spammers while misclassifying only around 2% of the legitimate users.