Robust Synthetic Data-Driven Detection of Living-Off-the-Land Reverse Shells

• URL: http://arxiv.org/abs/2402.18329v2
• Date: Mon, 16 Dec 2024 15:43:48 GMT
• Title: Robust Synthetic Data-Driven Detection of Living-Off-the-Land Reverse Shells
• Authors: Dmitrijs Trizna, Luca Demetrio, Battista Biggio, Fabio Roli,
• Abstract summary: Living-off-the-land (LOTL) techniques pose a significant challenge to security operations. We present a robust augmentation framework for cyber defense systems as Security Information and Event Management (SIEM) solutions.
• Score: 14.710331873072146
• License:
• Abstract: Living-off-the-land (LOTL) techniques pose a significant challenge to security operations, exploiting legitimate tools to execute malicious commands that evade traditional detection methods. To address this, we present a robust augmentation framework for cyber defense systems as Security Information and Event Management (SIEM) solutions, enabling the detection of LOTL attacks such as reverse shells through machine learning. Leveraging real-world threat intelligence and adversarial training, our framework synthesizes diverse malicious datasets while preserving the variability of legitimate activity, ensuring high accuracy and low false-positive rates. We validate our approach through extensive experiments on enterprise-scale datasets, achieving a 90\% improvement in detection rates over non-augmented baselines at an industry-grade False Positive Rate (FPR) of $10^{-5}$. We define black-box data-driven attacks that successfully evade unprotected models, and develop defenses to mitigate them, producing adversarially robust variants of ML models. Ethical considerations are central to this work; we discuss safeguards for synthetic data generation and the responsible release of pre-trained models across four best performing architectures, including both adversarially and regularly trained variants: https://huggingface.co/dtrizna/quasarnix. Furthermore, we provide a malicious LOTL dataset containing over 1 million augmented attack variants to enable reproducible research and community collaboration: https://huggingface.co/datasets/dtrizna/QuasarNix. This work offers a reproducible, scalable, and production-ready defense against evolving LOTL threats.

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