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We present PROVNINJA, a framework designed to generate adversarial attacks that aim to elude provenance-based Machine Learning (ML) security detectors. PROVNINJA is designed to identify and craft adversarial attack vectors that statistically mimic and impersonate system programs. Leveraging the benign execution profile of system processes commonly observed across a multitude of hosts and networks, our research proposes an efficient and effective method to probe evasive alternatives and devise stealthy attack vectors that are difficult to distinguish from benign system behaviors. PROVNINJA's suggestions for evasive attacks, originally derived in the feature space, are then translated into system actions, leading to the realization of actual evasive attack sequences in the problem space. When evaluated against State-of-The-Art (SOTA) detector models using two realistic Advanced Persistent Threat (APT) scenarios and a large collection of fileless malware samples, PROVNINJA could generate and realize evasive attack variants, reducing the detection rates by up to 59%. We also assessed PROVNINJA under varying assumptions on adversaries' knowledge and capabilities. While PROVNINJA primarily considers the black-box model, we also explored two contrasting threat models that consider blind and whitebox attack scenarios.
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This content will become publicly available on May 18, 2027
AI Assisted Electromagnetic Based Signature Construction in Malware Blacklisting
We propose a malware-centric blacklisting framework for side-channel security monitoring that identifies the type of malware present on embedded and IoT devices using electromagnetic (EM) signatures. In contrast to traditional reference model-based monitoring that primarily learns benign behavior and identifies deviations, our approach employs an Inverse One-Class Classification strategy grounded in the Complexity Hypothesis. By utilizing a 1D CNN-Autoencoder with a constrained bottleneck, we model the reconstruction residual as a Signal Complexity Indicator. This allows the framework to distinguish between high-entropy attack signals and low-dimensional benign executions without exhaustive benign profiling. At run time, the monitor analyzes short side-channel windows and outputs either a matched malware type or a principled no-match decision. We evaluate the proposed framework on the Arty-A7 100t FPGA platform using the NIST SARD Juliet Test Suite. Experimental results demonstrate that SC2V achieves an attack detection accuracy of 94.34% and robust malware-type identification, providing actionable, type-aware alerts for incident response.
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- PAR ID:
- 10708569
- Publisher / Repository:
- IEEE
- Date Published:
- Subject(s) / Keyword(s):
- Side-channel Measurement Neural Network Embedded System Security Malware Detection
- Format(s):
- Medium: X
- Location:
- Albany, New York, USA
- Sponsoring Org:
- National Science Foundation
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We present PROVNINJA, a framework designed to generate adversarial attacks that aim to elude provenance-based Machine Learning (ML) security detectors. PROVNINJA is designed to identify and craft adversarial attack vectors that statistically mimic and impersonate system programs. Leveraging the benign execution profile of system processes commonly observed across a multitude of hosts and networks, our research proposes an efficient and effective method to probe evasive alternatives and devise stealthy attack vectors that are difficult to distinguish from benign system behaviors. PROVNINJA's suggestions for evasive attacks, originally derived in the feature space, are then translated into system actions, leading to the realization of actual evasive attack sequences in the problem space. When evaluated against State-of-The-Art (SOTA) detector models using two realistic Advanced Persistent Threat (APT) scenarios and a large collection of fileless malware samples, PROVNINJA could generate and realize evasive attack variants, reducing the detection rates by up to 59%. We also assessed PROVNINJA under varying assumptions on adversaries' knowledge and capabilities. While PROVNINJA primarily considers the black-box model, we also explored two contrasting threat models that consider blind and white-box attack scenarios.more » « less
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We present PROVNINJA, a framework designed to generate adversarial attacks that aim to elude provenance-based Machine Learning (ML) security detectors. PROVNINJA is designed to identify and craft adversarial attack vectors that statistically mimic and impersonate system programs. Leveraging the benign execution profile of system processes commonly observed across a multitude of hosts and networks, our research proposes an efficient and effective method to probe evasive alternatives and devise stealthy attack vectors that are difficult to distinguish from benign system behaviors. PROVNINJA's suggestions for evasive attacks, originally derived in the feature space, are then translated into system actions, leading to the realization of actual evasive attack sequences in the problem space. When evaluated against State-of-The-Art (SOTA) detector models using two realistic Advanced Persistent Threat (APT) scenarios and a large collection of fileless malware samples, PROVNINJA could generate and realize evasive attack variants, reducing the detection rates by up to 59%. We also assessed PROVNINJA under varying assumptions on adversaries' knowledge and capabilities. While PROVNINJA primarily considers the black-box model, we also explored two contrasting threat models that consider blind and white-box attack scenarios.more » « less
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