2 Misleading Learners: Co-opting Your Spam Filter
نویسندگان
چکیده
Using statistical machine learning for making security decisions introduces new vulnerabilities in large scale systems. We show how an adversary can exploit statistical machine learning, as used in the SpamBayes spam filter, to render it useless—even if the adversary’s access is limited to only 1% of the spam training messages. We demonstrate three new attacks that successfully make the filter unusable, prevent victims from receiving specific email messages, and cause spam emails to arrive in the victim’s inbox.
منابع مشابه
Using AdaBoost and Decision Stumps to Identify Spam E-mail
An existing spam e-mail filter using the Naive Bayes decision engine was retrofitted with one based on the AdaBoost algorithm, using confidence-based weak learners. A comparison of results between the two is presented, with respect to both speed and accuracy.
متن کاملCoSpa: A Co-training Approach for Spam Review Identification with Support Vector Machine
Spam reviews are increasingly appearing on the Internet to promote sales or defame competitors by misleading consumers with deceptive opinions. This paper proposes a co-training approach called CoSpa (Co-training for Spam review identification) to identify spam reviews by two views: one is the lexical terms derived from the textual content of the reviews and the other is the PCFG (Probabilistic...
متن کاملIdentifying Spam Web Pages Based on Content Similarity
The Web provides its users with abundant information. Unfortunately, when a Web search is performed, both users and search engines are faced with an annoying problem: the presence of misleading Web pages, i.e., spam Web pages, that are ranked among legitimate Web pages. The mixed results downgrade the performance of search engines and frustrate users who are required to filter out useless infor...
متن کاملDiscriminative Topic Mining for Social Spam Detection
In the era of Social Web, there has been an explosive growth of user-contributed comments posted to various online social media. However, increasingly more misleading and deceptive user comments found at online social media have also been a great concern for consumers and merchants, and social spam have been brought to the attention by the legal circle in recent years. Social spam can cause tre...
متن کاملExploiting Machine Learning to Subvert Your Spam Filter
Using statistical machine learning for making security decisions introduces new vulnerabilities in large scale systems. This paper shows how an adversary can exploit statistical machine learning, as used in the SpamBayes spam filter, to render it useless—even if the adversary’s access is limited to only 1% of the training messages. We further demonstrate a new class of focused attacks that succ...
متن کامل