Our editorial team has handpicked some great talks from Black Hat Conference - one of the largest IT Security Conference in the world.
Black Hat - built by and for the global InfoSec community - returns to Las Vegas for its 21st year providing attendees with the very latest in research, development and trends. This six day event begins with four days of intense technical training for security practitioners of all levels (August 4-7) followed by the two-day main conference featuring Briefings, Business Hall, Arsenal, and more (August 8-9)
(Source: Black Hat Conference USA 2018)
Speaker: Dani Goland, Ido Naor
In our research, we dived into these malware-scanning giants and built sophisticated Yara rules to capture non-malicious artifacts and dissect them from secrets you've never thought possible of getting out of their chamber. But that's not all. We will show the audience how we built an intelligence tool, that upon insertion of an API key, will auto-dissect a full dataset. In our talk, we reveal the awful truth about allowing internally installed security products to be romantically involved with online scanners.
Speaker: Amanda Rousseau, Richard Seymour
In a world of high volume malware and limited researchers, we need a dramatic improvement in our ability to process and analyze new and old malware at scale. Unfortunately, what is currently available to the community is incredibly cost prohibitive or does not rise to the challenge. As malware authors and distributors share code and prepackaged tool kits, the white hat community is dominated by solutions aimed at profit as opposed to augmenting capabilities available to the broader community. With that in mind, we are introducing our library for malware disassembly called Xori as an open source project. Xori is focused on helping reverse engineers analyze binaries, optimizing for time and effort spent per sample.
Speaker: Felipe Ducau, Richard Harang
Security is a constant cat-and-mouse game between those trying to keep abreast of and detect novel malware, and the authors attempting to evade detection. The introduction of the statistical methods of machine learning into this arms race allows us to examine an interesting question: how fast is malware being updated in response to the pressure exerted by security practitioners? The ability of machine learning models to detect malware is now well known; we introduce a novel technique that uses trained models to measure "concept drift" in malware samples over time as old campaigns are retired, new campaigns are introduced, and existing campaigns are modified. Through the use of both simple distance-based metrics and Fisher Information measures, we look at the evolution of the threat landscape over time, with some surprising findings. In parallel with this talk, we will also release the PyTorch-based tools we have developed to address this question, allowing attendees to investigate concept drift within their own data.
Speakers: Camille Mougey, Fabrice Desclaux
Miasm is a reverse engineering framework created in 2006 and first published in 2011 (GPL). Since then, it has been continuously improved through a daily use. The framework is made of several parts, including an assembler/disassembler for several architectures (x86, aarch64, arm, etc.), an human readable intermediate language describing their instructions' semantic, or sandboxing capabilities of Windows/Linux environment. On top of these foundations, higher level analysis are provided to address more complex tasks, such as variable backtracking and dynamic symbolic execution. In this talk, we will introduce some of these features. The journey will start with the basics of the framework, go through symbolic emulation and function divination (Sibyl), and end with various components useful for malware analysis.
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Speaker: Gabriel Landau, Joe Desimone
While attacker techniques have evolved to evade endpoint protections, the current state of the art in kernel malware detection has also advanced to hinder these new kernel mode threats. We will discuss these new defensive techniques to counter kernel mode threats, including real-time detection techniques that leverage hypervisors along with an innovative hardware assisted approach that utilizes performance monitoring units. In addition, we will discuss on-demand techniques that leverage page table entry remapping to hunt for kernel malware at scale.
Speakers: Andrew Blaich, Michael Flossman
In this talk, we will unveil the new in-house capabilities of a nation state actor who has been observed deploying both Android and iOS surveillance tooling, known as Stealth Mango and Tangelo. The actor behind these offensive capabilities has successfully compromised the devices of government officials and military personnel in numerous countries with some directly impacting Western interests. Our research indicates this capability has been created by freelance developers who primarily release commodity spouse-ware but moonlight by selling their own custom surveillanceware to state actors. One such state actor has been observed deploying Stealth Mango and this presentation will unveil the depth and breadth of their campaigns, detailing not only how we watched them grow and develop, test, QA, and deploy their offensive tooling, but also how operation security mistakes ultimately led to their attribution.
Speakers: Holly Stewart , Jugal Parikh, Randy Treit
We'll discuss several strategies to make machine learning models more tamper resilient. We'll compare the difficulty of tampering with cloud-based models and client-based models. We'll discuss research that shows how singular models are susceptible to tampering, and some techniques, like stacked ensemble models, can be used to make them more resilient. We also talk about the importance of diversity in base ML models and technical details on how they can be optimized to handle different threat scenarios. Lastly, we'll describe suspected tampering activity we've witnessed using protection telemetry from over half a billion computers, and whether our mitigations worked.
Speaker: Andrei Costin, Jonas Zaddach
In this talk, We start with mostly manual collection, archival, meta-information extraction and cross-validation of more than 637 unique resources related to IoT malware families. These resources relate to 60 1 IoT malware families, and include 260 resources related to 48 unique vulnerabilities used in the disclosed or detected IoT malware attacks. We then use the extracted information to establish as accurately as possible the timeline of events related to each IoT malware family and relevant vulnerabilities, and to outline important insights and statistics.Finally, to help validate our work as well as to motivate its continuous growth and improvement by the research community, we open-source our datasets and release our IoT malware analysis framework and our IoT malware analysis framework.
Speakers: Dhilung Kirat, Jiyong Jang, Marc Ph. Stoecklin
In this talk, we describe DeepLocker, a novel class of highly targeted and evasive attacks powered by artificial intelligence (AI). As cybercriminals increasingly weaponize AI, cyber defenders must understand the mechanisms and implications of the malicious use of AI in order to stay ahead of these threats and deploy appropriate defenses.
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