With the increasing sophistication of Advanced Persistent Threats (APTs), the demand for effective detection and mitigation strategies and methods has escalated. Program execution leaves traces in the system audit log, which can be analyzed to detect malicious activities. However, collecting and analyzing large volumes of audit logs over extended periods is challenging, further compounded by insufficient labeling that hinders their usability. Addressing these challenges, this paper introduces SAGA (Synthetic Audit log Generation for APT campaigns), a novel approach for generating find-grained labeled synthetic audit logs that mimic real-world system logs while embedding stealthy APT attacks. SAGA generates configurable audit logs for arbitrary duration, blending benign logs from normal operations with malicious logs based on the definitions the MITRE ATT&CK framework. Malicious audit logs follow an APT lifecycle, incorporating various attack techniques at each stage. These synthetic logs can serve as benchmark datasets for training machine learning models and assessing diverse APT detection methods. To demonstrate the usefulness of synthetic audit logs, we ran established baselines of event-based technique hunting and APT campaign detection using various synthetic audit logs. In addition, we show that a deep learning model trained on synthetic audit logs can detect previously unseen techniques within audit logs.

In this article, we focus on the robustness of behavior-based malware analysis models, justified by the need to address the high mutation rates of malware executables that debilitate conventional signature-based approaches and even behavior-based AI solutions. In response to these challenges, we propose MAMBA + , an obfuscation-resistant dynamic analysis approach tailored for uncovering malware behavior. We have assembled a comprehensive collection of behavioral obfuscation attacks designed to undermine behavior-based models. The central concept behind MAMBA + involves treating obfuscated calls as perturbed data and introducing a novel loss function to effectively balance ground-truth predictions and the handling of these perturbations. To facilitate this approach, MAMBA + designs adapted embedding mechanisms to transform traces of API calls into high-dimensional vectors for attention calculations. Through a comprehensive empirical study with seven obfuscations and three unseen attacks, we reveal important qualitative properties of MAMBA + , and quantitatively demonstrate its superiority in performance and robustness to all compared methods.

The task of retrieving known Advanced Persistent Threat (APT) campaigns from system activity graphs, where nodes represent MITRE ATT&CK techniques and edges encode temporal or resource-level relationships, requires reasoning over structures. In operational settings, these target graphs are often noisy due to incomplete detection, technique misclassification, and benign-induced structural artifacts. To address this issue, we formulate the task as approximate subgraph matching between a known APT query graph and a noisy, partially observed technique graph. In this poster, we introduce a preliminary embedding-based retrieval method, aiming to promote it as a robust and practical framework for retrieving known APTs in real-world environments.

As Advanced Persistent Threats (APTs) grow increasingly sophisticated, the demand for effective detection methods has intensified. This study addresses the challenge of identifying APT campaign attacks through system event logs. A cascading approach, name SFM, combines Technique hunting and APT campaign attribution. The approach assumes that real-world system event logs contain a vast majority of normal events interspersed with few suspiciously malicious ones and that the logs are annotated with Techniques of MITRE ATT&CK framework for attack pattern recognition. After identifying Techniques from the log, we attribute APT campaign attacks by aligning detected Techniques with known attack sequences to determine the most likely APT campaign. Evaluations on five synthetic real-world APT campaigns indicate that the proposed approach demonstrates reliable performance.

This paper presents APILI, an innovative approach to behavior-based malware analysis that utilizes deep learning to locate the API calls corresponding to discovered malware techniques in dynamic execution traces. APILI defines multiple attentions between API calls, resources, and techniques, incorporating MITRE ATT&CK framework, adversary tactics, techniques and procedures, through a neural network. We employ fine-tuned BERT for arguments/resources embedding, SVD for technique representation, and several design enhancements, including layer structure and noise addition, to improve the locating performance. To the best of our knowledge, this is the first attempt to locate low-level API calls that correspond to high-level malicious behaviors (that is, techniques). Our evaluation demonstrates that APILI outperforms other traditional and machine learning techniques in both technique discovery and API locating. These results indicate the promising performance of APILI, thus allowing it to reduce the analysis workload.

Cyber threats are one of the most pressing issues in the digital age. There has been a consensus on deploying a proactive defense to effectively detect and respond to adversary threats. The key to success is understanding the characteristics of malware, including their activities and manipulated resources on the target machines. The MITRE ATT&CK framework (ATT&CK), a popular source of open source intelligence (OSINT), provides rich information and knowledge about adversary lifecycles and attack behaviors. The main challenges of this study involve knowledge collection from ATT&CK, malicious behavior identification using deep learning, and the identification of associated API calls. A MITRE ATT&CK based Malicious Behavior Analysis system (MAMBA) for Windows malware is proposed, which incorporates ATT&CK knowledge and considers attentions on manipulated resources and malicious activities in the neural network model. To synchronize ATT&CK updates in a timely manner, knowledge collection can be an automatic and incremental process. Given these features, MAMBA achieves the best performance of malicious behavior discovery among all the compared learning-based methods and rule-based approaches on all datasets; it also yields a highly interpretable mapping from the discovered malicious behaviors to relevant ATT&CK techniques, as well as to the related API calls.