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Over the past year, more than 10,000 people participated in the Matasano crypto challenges, a staged learning exercise where participants implemented 48 different attacks against realistic cryptographic constructions. In the process, we collected crypto exploit code in dozens of different languages, ranging from X86 assembly to Haskell.

Our challenges cover crypto concepts from block cipher mode selection to public key agreement algorithms. Up until this point, little has been done to help researchers expose vulnerabilities within the systems that implement the protocol.

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Many point-of-sale breaches occurred in the past year and many organizations are still vulnerable against the simplest exploits. One of the most common threats is memory scraping, which is a difficult issue to solve. Hence, I would like to share with you a demonstration of how it works and what can be done in order to minimize this threat.

The secure BYOD hype is growing and Virtual Desktop Infrastructure (VDI) is considered the alternative solution for secure containers. In a nutshell, VDI solutions provide a remote workstation offering so that no data is stored locally.

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We decided to examine the architecture and see for ourselves whether VDI delivers on its security promise. In this engaging session, we demonstrate a proof-of-concept attack where a malicious app leverages screen scraping to exfiltrate data through common VDI platforms.

While keeping the espionage activity invisible both from client-side and server-side malware detection measures, the attacker can automate the process and ultimately render the VDI solution ineffective. If we could recover this shared-code network, we could provide much needed context for and insight into newly observed malware.

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Various approaches have been proposed to see through malware packing and obfuscation to identify code sharing. A significant limitation of these existing approaches, however, is that they are either scalable but easily defeated or that they are complex but do not scale to millions of malware samples.

A final issue is that even the more complex approaches described in the research literature tend to only exploit one "feature domain," be it malware instruction sequences, call graph structure, application binary interface metadata, or dynamic API call traces, leaving these methods open to defeat by intelligent adversaries. How, then, do we assess malware similarity and "newness" in a way that both scales to millions of samples and is resilient to the zoo of obfuscation techniques that malware authors employ?

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To make this algorithm scale, we use an approximate feature counting technique and a feature-hashing trick drawn from the machine-learning domain, allowing for the fast feature extraction and fast retrieval of sample "near neighbors" even when handling millions of binaries. Our algorithm was developed over the course of three years and has been evaluated both internally and by an independent test team at MIT Lincoln Laboratories: we scored the highest on these tests against four competing malware cluster recognition techniques and we believe this was because of our unique "ensemble" approach.

In the presentation, I will give details on how to implement the algorithm and will go over these algorithm results in a series of large-scale interactive malware visualizations. As part of the algorithm description I will walk through a Python machine learning library that we will be releasing in the conference material which allows users to detect feature frequencies over billions of items on commodity hardware.

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Automotive security concerns have gone from the fringe to the mainstream with security researchers showing the susceptibility of the modern vehicle to local and remote attacks. A malicious attacker leveraging a remote vulnerability could do anything from enabling a microphone for eavesdropping to turning the steering wheel to disabling the brakes.

Unfortunately, research has only been presented on three or four particular vehicles. Each manufacturer designs their fleets differently; therefore analysis of remote threats must avoid generalities.

This talk takes a step back and examines the automotive network of a large number of different manufacturers from a security perspective. From this larger dataset, we can begin to answer questions like: Are some cars more secure from remote compromise than others?

Has automotive network security changed for the better (or worse) in the last five years? What does the future of automotive security hold and how can we protect our vehicles from attack moving forward? Consumer Premise Equipment (CPE) has become common, nearly ubiquito