More than ever before the Internet is changing computing as we know it. Global access to information and resources is becoming an integral part of nearly every aspect of our lives. Unfortunately, with this global network access comes increased chances of malicious attack and intrusion. In an effort to confront the new threats unveiled by the networking revolution of the past few years reliable, rapid, and unintrusive means for automatically recognizing the identity of individuals are now being sought. In this paper we examine an emerging non-static biometric technique that aims to identify users based on analyzing habitual rhythm patterns in the way they type.

Fine-grained address space layout randomization (ASLR) has recently been proposed as a method of efficiently mitigating runtime attacks. In this paper, we introduce the design and implementation of a framework based on a novel attack strategy, dubbed just-in-time code reuse, that undermines the benefits of fine-grained ASLR. Specifically, we derail the assumptions embodied in fine-grained ASLR by exploiting the ability to repeatedly abuse a memory disclosure to map an application's memory layout on-the-fly, dynamically discover API functions and gadgets, and JIT-compile a target program using those gadgets-all within a script environment at the time an exploit is launched. We demonstrate the power of our framework by using it in conjunction with a real-world exploit against Internet Explorer, and also provide extensive evaluations that demonstrate the practicality of just-in-time code reuse attacks. Our findings suggest that fine-grained ASLR may not be as promising as first thought.

The academic community has long acknowledged the existence of malicious botnets, however to date, very little is known about the behavior of these distributed computing platforms. To the best of our knowledge, botnet behavior has never been methodically studied, botnet prevalence on the Internet is mostly a mystery, and the botnet life cycle has yet to be modeled. Uncertainty abounds. In this paper, we attempt to clear the fog surrounding botnets by constructing a multifaceted and distributed measurement infrastructure. Throughout a period of more than three months, we used this infrastructure to track 192 unique IRC botnets of size ranging from a few hundred to several thousand infected end-hosts. Our results show that botnets represent a major contributor to unwanted Internet traffic-27% of all malicious connection attempts observed from our distributed darknet can be directly attributed to botnetrelated spreading activity. Furthermore, we discovered evidence of botnet infections in 11% of the 800,000 DNS domains we examined, indicating a high diversity among botnet victims. Taken as a whole, these results not only highlight the prominence of botnets, but also provide deep insights that may facilitate further research to curtail this phenomenon.

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Home-based IoT devices have a bleak reputation regarding their security practices. On the surface, the insecurities of IoT devices seem to be caused by integration problems that may be addressed by simple measures, but this work finds that to be a naive assumption. The truth is, IoT deployments, at their core, utilize traditional compute systems, such as embedded, mobile, and network. These components have many unexplored challenges such as the effect of over-privileged mobile applications on embedded devices.Our work proposes a methodology that researchers and practitioners could employ to analyze security properties for home-based IoT devices. We systematize the literature for homebased IoT using this methodology in order to understand attack techniques, mitigations, and stakeholders. Further, we evaluate 45 devices to augment the systematized literature in order to identify neglected research areas. To make this analysis transparent and easier to adapt by the community, we provide a public portal to share our evaluation data and invite the community to contribute their independent findings.

We propose a technique to reliably generate a cryptographic key from a user's voice while speaking a password.The key resists cryptanalysis even against an attacker who captures all system information related to generating or veribing the cryptographic key. Moreover; the technique is suficiently robust to enable the user to reliably regenerate the key by uttering her password again. We describe an empirical evaluation of this technique using 250 utterances recorded from 50 users.

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We present two provably secure and efficient schemes for performing conjunctive keyword searches over symmetrically encrypted data. Our first scheme is based on Shamir Secret Sharing and provides the most efficient search technique in this context to date. Although the size of its trapdoors is linear in the number of documents being searched, we empirically show that this overhead remains reasonable in practice. Nonetheless, to address this limitation we provide an alternative based on bilinear pairings that yields constant size trapdoors. This latter construction is not only asymptotically more efficient than previous secure conjunctive keyword search schemes in the symmetric setting, but incurs significantly less storage overhead. Additionally, unlike most previous work, our constructions are proven secure in the standard model.