12,357 research outputs found
Making the Most of Tweet-Inherent Features for Social Spam Detection on Twitter
Social spam produces a great amount of noise on social media services such as
Twitter, which reduces the signal-to-noise ratio that both end users and data
mining applications observe. Existing techniques on social spam detection have
focused primarily on the identification of spam accounts by using extensive
historical and network-based data. In this paper we focus on the detection of
spam tweets, which optimises the amount of data that needs to be gathered by
relying only on tweet-inherent features. This enables the application of the
spam detection system to a large set of tweets in a timely fashion, potentially
applicable in a real-time or near real-time setting. Using two large
hand-labelled datasets of tweets containing spam, we study the suitability of
five classification algorithms and four different feature sets to the social
spam detection task. Our results show that, by using the limited set of
features readily available in a tweet, we can achieve encouraging results which
are competitive when compared against existing spammer detection systems that
make use of additional, costly user features. Our study is the first that
attempts at generalising conclusions on the optimal classifiers and sets of
features for social spam detection over different datasets
Analyzing the Social Structure and Dynamics of E-mail and Spam in Massive Backbone Internet Traffic
E-mail is probably the most popular application on the Internet, with
everyday business and personal communications dependent on it. Spam or
unsolicited e-mail has been estimated to cost businesses significant amounts of
money. However, our understanding of the network-level behavior of legitimate
e-mail traffic and how it differs from spam traffic is limited. In this study,
we have passively captured SMTP packets from a 10 Gbit/s Internet backbone link
to construct a social network of e-mail users based on their exchanged e-mails.
The focus of this paper is on the graph metrics indicating various structural
properties of e-mail networks and how they evolve over time. This study also
looks into the differences in the structural and temporal characteristics of
spam and non-spam networks. Our analysis on the collected data allows us to
show several differences between the behavior of spam and legitimate e-mail
traffic, which can help us to understand the behavior of spammers and give us
the knowledge to statistically model spam traffic on the network-level in order
to complement current spam detection techniques.Comment: 15 pages, 20 figures, technical repor
Evaluating Third-Party Bad Neighborhood Blacklists for Spam Detection
The distribution of malicious hosts over the IP address space is far from being uniform. In fact, malicious hosts tend to be concentrate in certain portions of the IP address space, forming the so-called Bad Neighborhoods. This phenomenon has been previously exploited to filter Spam by means of Bad Neighborhood blacklists. In this paper, we evaluate how much a network administrator can rely upon different Bad Neighborhood blacklists generated by third-party sources to fight Spam. One could expect that Bad Neighborhood blacklists generated from different sources contain, to a varying degree, disjoint sets of entries. Therefore, we investigate (i) how specific a blacklist is to its source, and (ii) whether different blacklists can be interchangeably used to protect a target from Spam. We analyze five Bad Neighborhood blacklists generated from real-world measurements and study their effectiveness in protecting three production mail servers from Spam. Our findings lead to several operational considerations on how a network administrator could best benefit from Bad Neighborhood-based Spam filtering
Artificial intelligence in the cyber domain: Offense and defense
Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41
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