Reconstructing past events in IT systems is a critical bottleneck in forensic investigations, consuming valuable time from investigators. It requires meticulous analysis of complex digital traces in an environment where attackers may try to erase traces. For example, deletion of digital artifacts is an anti-forensic technique used to jeopardize the success of forensic investigations.To address these challenges, we introduce Investigator Copilot, a novel framework that automates post-mortem event reconstruction using explainable machine learning. To overcome the general scarcity of datasets, Investigator Copilot replays realistic events on virtual machines, and creates datasets by extracting, normalizing and labeling traces from corresponding hard disks. Using these datasets, Investigator Copilot trains human-interpretable decision tree stumps that evaluate digital evidence and combines these binary classifiers in Forensic Forests. Forensic Forests utilize an adjusted voting scheme to provide robust event reconstruction even when faced with deleted evidence.We evaluate our approach by executing 2100 events on 50 virtual machines, training Forensic Forests and measuring their event reconstruction performance on previously unseen data. Our results demonstrate that tree-based classifiers perform exceedingly well in event reconstruction. When measuring reconstruction performance on manipulated evidence, we observe that Forensic Forests significantly outperform the state-of-the-art, which positions them as a valuable tool for investigators. Our findings indicate that automated frameworks such as Investigator Copilot can contribute to the efficiency and robustness of forensic analyses, and may save scarce resources of human investigators.
Fuzzy support vector machine (FSVM) effectively enhances the model’s robustness to common noise and outliers by introducing a sample membership mechanism. To further improve the model’s performance in complex scenarios such as data poisoning and adversarial noise, researchers have proposed the curriculum learning-based fuzzy support vector machine (CLFSVM), which optimizes the training process through a curriculum learning strategy. However, the performance of this model heavily depends on the quality of the initial core sample set, and an improper initial selection can lead to a significant degradation in model performance. To address this issue, we propose an innovative framework, the granular ball and curriculum learning-based FSVM (GB-CLFSVM), which includes three key components. First, it constructs a multi-stage training paradigm from easy to hard based on curriculum learning theory, ensuring the model gradually adapts to complex data distributions. Second, it employs granular balls to automatically select a set of core samples, building a small and high-quality training set and generating a robust standard SVM as the initialization baseline for FSVM. Finally, it designs an adaptive composite weighting mechanism to dynamically calculate sample fuzzy memberships. By comprehensively considering local sample density and class information, this mechanism assigns fine-grained weights to samples of varying complexity, thereby optimizing the iterative process of curriculum learning. Experimental results show that GB-CLFSVM significantly outperforms the CLFSVM baseline model in both classification accuracy and noise resistance, demonstrating exceptional generalization ability, especially in data scenarios with noisy features and labels.
Black-box adversarial attacks bypass internal target model information, broadening their applicability and making them a critical area in deep learning security. However, most existing methods prioritize attack success over perturbation imperceptibility, resulting in visually detectable perturbations. Addressing this, we propose the Grad-CAM-guided Perceptibility-Reduced Ensemble Attack (GPEA), integrating ensemble learning and Gradient-weighted Class Activation Mapping (Grad-CAM) to improve both attack success and perturbation imperceptibility. GPEA comprises two modules: Dynamic Weight Ensemble (DWE) and Grad-CAM-Based Perturbation Restriction (GPR). DWE introduces a novel weight initialization and adjustment scheme, optimizing surrogate model contributions to enhance black-box attack success. GPR applies Grad-CAM to restrict perturbations to critical image areas, markedly reducing perceptibility. Experimental results demonstrate the effectiveness of GPEA in balancing attack success and imperceptibility across various datasets and models. On VOC dataset, GPEA improves the black-box attack success rate by up to 8.4% over state-of-the-art methods; across five target models, it achieves average improvements of 18.5%, 16%, 36.4%, 33.9%, and 26.8% in SSIM, PSNR, LPIPS, l(0), and l(2) metrics, respectively.
Containers have become a widely used mechanism for isolation of software workloads on the same physical platform in cloud and edge computing scenarios. However, the isolation guarantees provided by containers result from multiple, interacting mechanisms for process isolation and virtualization at the kernel level. These mechanisms are often configured through a container engine with its own engine-level configuration semantics. Consequently, deciding whether a particular container configuration provides the level of isolation required in a given scenario is a non-trivial taskThe present work proposes an approach to assess the isolation characteristics of a container in an engine-agnostic way. To this end, we evaluate the container and host views of the configuration of process isolation mechanisms for a given container at the kernel user-space API-level. The approach for configuration assessment we propose makes the isolation characteristics that are provided by a container transparent. This enables, for instance, container engine administrators to assess container configurations prior to large-scale deployment in an efficient manner. Also, the proposed approach makes container isolation characteristics comparable among container engines and enables verification of the container isolation configuration at runtime.
Large vision-language models (LVLMs) have demonstrated remarkable capabilities across diverse multimodal tasks, yet remain vulnerable to adversarial attacks, posing significant challenges to their secure deployment. To reveal their vulnerabilities and promote robust multimodal learning, we propose TSRP, a targeted and transferable attack framework utilizing two-stage region-specific perturbations. In Stage One, TSRP optimizes perturbations in critical regions using Grad-CAM-guided contrastive learning on eight large CLIP variants, enhanced with extensive image data augmentation to improve robustness and transferability. In Stage Two, it refines perturbations in non-critical regions via the vision encoder of GLM-4V, ensuring efficient use of the image space while preserving perceptual similarity. Extensive experiments on multiple state-of-the-art LVLMs show that TSRP achieves an average attack success rate of 89.9% with the lowest LPIPS score of 0.1536, significantly outperforming existing methods. These results demonstrate that region-specific, multi-stage perturbation optimization substantially enhances both attack effectiveness and transferability, offering new insights for adversarial research on LVLMs.
