Artificial immune system for the Internet

We investigate the usability of the Artificial Immune Systems (AIS) approach for solving selected problems in computer networks. Artificial immune systems are created by using the concepts and algorithms inspired by the theory of how the Human Immune System (HIS) works. We consider two applications: detection of routing misbehavior in mobile ad hoc networks, and email spam filtering. In mobile ad hoc networks the multi-hop connectivity is provided by the collaboration of independent nodes. The nodes follow a common protocol in order to build their routing tables and forward the packets of other nodes. As there is no central control, some nodes may defect to follow the common protocol, which would have a negative impact on the overall connectivity in the network. We build an AIS for the detection of routing misbehavior by directly mapping the standard concepts and algorithms used for explaining how the HIS works. The implementation and evaluation in a simulator shows that the AIS mimics well most of the effects observed in the HIS, e.g. the faster secondary reaction to the already encountered misbehavior. However, its effectiveness and practical usability are very constrained, because some particularities of the problem cannot be accounted for by the approach, and because of the computational constrains (reported also in AIS literature) of the used negative selection algorithm. For the spam filtering problem, we apply the AIS concepts and algorithms much more selectively and in a less standard way, and we obtain much better results. We build the AIS for antispam on top of a standard technique for digest-based collaborative email spam filtering. We notice un advantageous and underemphasized technological difference between AISs and the HIS, and we exploit this difference to incorporate the negative selection in an innovative and computationally efficient way. We also improve the representation of the email digests used by the standard collaborative spam filtering scheme. We show that this new representation and the negative selection, when used together, improve significantly the filtering performance of the standard scheme on top of which we build our AIS. Our complete AIS for antispam integrates various innate and adaptive AIS mechanisms, including the mentioned specific use of the negative selection and the use of innate signalling mechanisms (PAMP and danger signals). In this way the AIS takes into account users' profiles, implicit or explicit feedback from the users, and the bulkiness of spam. We show by simulations that the overall AIS is very good both in detecting spam and in avoiding misdetection of good emails. Interestingly, both the innate and adaptive mechanisms prove to be crucial for achieving the good overall performance. We develop and test (within a simulator) our AIS for collaborative spam filtering in the case of email communications. The solution however seems to be well applicable to other types of Internet communications: Internet telephony, chat/sms, forum, news, blog, or web. In all these cases, the aim is to allow the wanted communications (content) and prevent those unwanted from reaching the end users and occupying their time and communication resources. The filtering problems, faced or likely to be faced in the near future by these applications, have or are likely to have the settings similar to those that we have in the email case: need for openness to unknown senders (creators of content, initiators of the communication), bulkiness in receiving spam (many recipients are usually affected by the same spam content), tolerance of the system to a small damage (to small amounts of unfiltered spam), possibility to implicitly or explicitly and in a cheap way obtain a feedback from the recipients about the damage (about spam that they receive), need for strong tolerance to wanted (non-spam) content. Our experiments with the email spam filtering show that our AIS, i.e. the way how we build it, is well fitted to such problem settings

Analyzing Social and Stylometric Features to Identify Spear phishing Emails

Spear phishing is a complex targeted attack in which, an attacker harvests information about the victim prior to the attack. This information is then used to create sophisticated, genuine-looking attack vectors, drawing the victim to compromise confidential information. What makes spear phishing different, and more powerful than normal phishing, is this contextual information about the victim. Online social media services can be one such source for gathering vital information about an individual. In this paper, we characterize and examine a true positive dataset of spear phishing, spam, and normal phishing emails from Symantec's enterprise email scanning service. We then present a model to detect spear phishing emails sent to employees of 14 international organizations, by using social features extracted from LinkedIn. Our dataset consists of 4,742 targeted attack emails sent to 2,434 victims, and 9,353 non targeted attack emails sent to 5,912 non victims; and publicly available information from their LinkedIn profiles. We applied various machine learning algorithms to this labeled data, and achieved an overall maximum accuracy of 97.76% in identifying spear phishing emails. We used a combination of social features from LinkedIn profiles, and stylometric features extracted from email subjects, bodies, and attachments. However, we achieved a slightly better accuracy of 98.28% without the social features. Our analysis revealed that social features extracted from LinkedIn do not help in identifying spear phishing emails. To the best of our knowledge, this is one of the first attempts to make use of a combination of stylometric features extracted from emails, and social features extracted from an online social network to detect targeted spear phishing emails.Comment: Detection of spear phishing using social media feature

Exploiting 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 successfully prevent victims from receiving specific email messages. Finally, we introduce two new types of defenses against these attacks.

BlogForever: D2.5 Weblog Spam Filtering Report and Associated Methodology

This report is written as a first attempt to define the BlogForever spam detection strategy. It comprises a survey of weblog spam technology and approaches to their detection. While the report was written to help identify possible approaches to spam detection as a component within the BlogForver software, the discussion has been extended to include observations related to the historical, social and practical value of spam, and proposals of other ways of dealing with spam within the repository without necessarily removing them. It contains a general overview of spam types, ready-made anti-spam APIs available for weblogs, possible methods that have been suggested for preventing the introduction of spam into a blog, and research related to spam focusing on those that appear in the weblog context, concluding in a proposal for a spam detection workflow that might form the basis for the spam detection component of the BlogForever software