Robust Synthetic Data-Driven Detection of Living-Off-the-Land Reverse Shells
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916524113002496 |
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| author | Trizna, Dmitrijs Demetrio, Luca Biggio, Battista Roli, Fabio |
| author_facet | Trizna, Dmitrijs Demetrio, Luca Biggio, Battista Roli, Fabio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_18329 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Robust Synthetic Data-Driven Detection of Living-Off-the-Land Reverse Shells Trizna, Dmitrijs Demetrio, Luca Biggio, Battista Roli, Fabio Cryptography and Security Machine Learning 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. |
| title | Robust Synthetic Data-Driven Detection of Living-Off-the-Land Reverse Shells |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2402.18329 |