CCS

Machine learning has been widely used for defensive security. Numerous approaches have been devised that make use of learning techniques for detecting attacks and malicious software. By contrast, only very few research has studied how machine learning ...

Many social networks are predicated on the assumption that a member's online information reflects his or her real identity. In such networks, members who fill their name fields with fictitious identities, company names, phone numbers, or just gibberish ...

As the amount of content users publish on social networking sites rises, so do the danger and costs of inadvertently sharing content with an unintended audience. Studies repeatedly show that users frequently misconfigure their policies or misunderstand ...

We introduce GOTCHAs (Generating panOptic Turing Tests to Tell Computers and Humans Apart) as a way of preventing automated offline dictionary attacks against user selected passwords. A GOTCHA is a randomized puzzle generation protocol, which involves ...

We investigate possible improvements in online fraud detection based on information about users and their interactions. We develop, apply, and evaluate our methods in the context of Skype. Specifically, in Skype, we aim to provide tools that identify ...

The number of malicious applications targeting the Android system has literally exploded in recent years. While the security community, well aware of this fact, has proposed several methods for detection of Android malware, most of these are based on ...

In this work we investigate a new approach for detecting attacks which aim to degrade the network's Quality of Service (QoS). To this end, a new network-based intrusion detection system (NIDS) is proposed. Most contemporary NIDSs take a passive approach ...

Detection methods based on n-gram models have been widely studied for the identification of attacks and malicious software. These methods usually build on one of two learning schemes: anomaly detection, where a model of normality is constructed from n-...

An increasing number of machine learning applications involve detecting the malicious behavior of an attacker who wishes to avoid detection. In such domains, attackers modify their behavior to evade the classifier while accomplishing their goals as ...

Clustering algorithms have been increasingly adopted in security applications to spot dangerous or illicit activities. However, they have not been originally devised to deal with deliberate attack attempts that may aim to subvert the clustering process ...

In this position paper, we argue that to be of practical interest, a machine-learning based security system must engage with the human operators beyond feature engineering and instance labeling to address the challenge of drift in adversarial ...