Malware detection and classification has been the focus of extensive research over many years. However, less effort has been devoted to developing post-detection systems that identify specific malicious capabilities (or behaviors) in malware. Such systems play a critical part in identifying and mitigating the damage caused by malware attacks. Unfortunately, current methods for identifying malware capabilities involve substantial manual reverse engineering efforts and context switching between multiple tools, which slows down an investigation and gives attackers an advantage.In this paper, we propose DEEPCAPA, an automated post-detection system that uses machine learning to identify potentially malicious capabilities in malware in the form of MITRE ATT&CK techniques. Our system operates on sequences of API calls, extracted from the memory snapshots taken at key points during the (sandboxed) execution of malware. Our results demonstrate that DEEPCAPA can accurately identify malicious capabilities, achieving a precision of 95.80% and a recall of 93.76% across 29 different techniques.

With tools like GitHub Copilot, automatic code suggestion is no longer a dream in software engineering. These tools, based on large language models, are typically trained on massive corpora of code mined from unvetted public sources. As a result, these models are susceptible to data poisoning attacks where an adversary manipulates the model’s training by injecting malicious data. Poisoning attacks could be designed to influence the model’s suggestions at run time for chosen contexts, such as inducing the model into suggesting insecure code payloads. To achieve this, prior attacks explicitly inject the insecure code payload into the training data, making the poison data detectable by static analysis tools that can remove such malicious data from the training set. In this work, we demonstrate two novel attacks, Covert and TrojanPuzzle, that can bypass static analysis by planting malicious poison data in out-of-context regions such as docstrings. Our most novel attack, TrojanPuzzle, goes one step further in generating less suspicious poison data by never explicitly including certain (suspicious) parts of the payload in the poison data, while still inducing a model that suggests the entire payload when completing code (i.e., outside docstrings). This makes TrojanPuzzle robust against signature-based dataset-cleansing methods that can filter out suspicious sequences from the training data. Our evaluation against models of two sizes demonstrates that both Covert and TrojanPuzzle have significant implications for practitioners when selecting code used to train or tune code-suggestion models.

Security monitoring systems detect potentially malicious activities in IT infrastructures, by either looking for known signatures or for anomalous behaviors. Security operators investigate these events to determine whether they pose a threat to their organization. In many cases, a single event may be insufficient to determine whether certain activity is indeed malicious. Therefore, a security operator frequently needs to correlate multiple events to identify if they pose a real threat. Unfortunately, the vast number of events that need to be correlated often overload security operators, forcing them to ignore some events and, thereby, potentially miss attacks. This work studies how to automatically correlate security events and, thus, automate parts of the security operator workload. We design and evaluate DEEPCASE, a system that leverages the context around events to determine which events require further inspection. This approach reduces the number of events that need to be inspected. In addition, the context provides valuable insights into why certain events are classified as malicious. We show that our approach automatically filters 86.72% of the events and reduces the manual workload of security operators by 90.53%, while underestimating the risk of potential threats in less than 0.001% of cases.

Software is continually increasing in size and complexity, and therefore, vulnerability discovery would benefit from techniques that identify potentially vulnerable regions within large code bases, as this allows for easing vulnerability detection by reducing the search space. Previous work has explored the use of conventional code-quality and complexity metrics in highlighting suspicious sections of (source) code. Recently, researchers also proposed to reduce the vulnerability search space by studying code properties with neural networks. However, previous work generally failed in leveraging the rich metadata that is available for long-running, large code repositories.

A recent source of concern for the security of neural networks is the emergence of clean-label dataset poisoning attacks, wherein correctly labeled poison samples are injected into the training dataset. While these poison samples look legitimate to the human observer, they contain malicious characteristics that trigger a targeted misclassification during inference. We propose a scalable and transferable clean-label poisoning attack against transfer learning, which creates poison images with their center close to the target image in the feature space. Our attack, Bullseye Polytope, improves the attack success rate of the current state-of-the-art by 26.75% in end-to-end transfer learning, while increasing attack speed by a factor of 12. We further extend Bullseye Polytope to a more practical attack model by including multiple images of the same object (e.g., from different angles) when crafting the poison samples. We demonstrate that this extension improves attack transferability by over 16% to unseen images (of the same object) without using extra poison samples.

Malware analysis is an essential task to understand infection campaigns, the behavior of malicious codes, and possible ways to mitigate threats. Malware analysis also allows better assessment of attackers’ capabilities, techniques, and processes. Although a substantial amount of previous work provided a comprehensive analysis of the international malware ecosystem, research on regionalized, country-, and population-specific malware campaigns have been scarce. Moving towards addressing this gap, we conducted a longitudinal (2012-2020) and comprehensive (encompassing an entire population of online banking users) study of MS Windows desktop malware that actually infected Brazilian banks’ users. We found that the Brazilian financial desktop malware has been evolving quickly: it started to make use of a variety of file formats instead of typical PE binaries, relied on native system resources, and abused obfuscation techniques to bypass detection mechanisms. Our study on the threats targeting a significant population on the ecosystem of the largest and most populous country in Latin America can provide invaluable insights that may be applied to other countries’ user populations, especially those in the developing world that might face cultural peculiarities similar to Brazil’s. With this evaluation, we expect to motivate the security community/industry to seriously consider a deeper level of customization during the development of next-generation anti-malware solutions, as well as to raise awareness towards regionalized and targeted Internet threats.

Malware detection plays a vital role in computer security. Modern machine learning approaches have been centered around domain knowledge for extracting malicious features. However, many potential features can be used, and it is time consuming and difficult to manually identify the best features, especially given the diverse nature of malware.

While online review services provide a two-way conversation between brands and consumers, malicious actors, including misbehaving businesses, have an equal opportunity to distort the reviews for their own gains. We propose OneReview, a method for locating fraudulent reviews, correlating data from multiple crowd-sourced review sites. Our approach utilizes Change Point Analysis to locate points at which a business' reputation shifts. Inconsistent trends in reviews of the same businesses across multiple websites are used to identify suspicious reviews. We then extract an extensive set of textual and contextual features from these suspicious reviews and employ supervised machine learning to detect fraudulent reviews.