Machine learning-based Android malware detectors are vulnerable to black-box adversarial attacks, yet existing methods typically suffer from low query efficiency and limited transferability. This study introduces a novel, two-stage transfer attack that systematically enhances adversarial transferability under stringent, query-limited conditions. We propose a new methodology that decomposes adversarial perturbations into model-specific &#x201C;key perturbations&#x201D; and transferable &#x201C;perturbation noise.&#x201D; By augmenting the minimal key perturbations with strategically sampled perturbation noise, our method generates a diverse set of highly effective adversarial examples. Extensive experiments demonstrate the attack&#x2019;s potency, achieving evasion rates of up to 99.53% with an extremely low budget of 1-10 hard-label queries. Our method proves its versatility by successfully targeting models trained on both Boolean and Markov Chain-based features, and importantly, in challenging cross-dimensional scenarios where the attacker&#x2019;s feature knowledge is limited. Furthermore, a case study on VirusTotal confirms its practical efficacy, reducing detection rates of modern malware by commercial engines by up to 49.7%. This work reveals a new class of potent, query-efficient black-box threats, underscoring the urgent need for more robust malware detection defenses. 

Noise-Augmented Transferability: A Low-Query-Budget Transfer Attack on Android Malware Detectors

Kernel-space rootkits provide adversaries with permanent high-privileged access to compromised systems and are often a key element of sophisticated attack chains. At the same time, they enable stealthy operation and are thus difficult to detect. Thereby, they inject code into kernel functions to appear invisible to users, for example, by manipulating file enumerations. Existing detection approaches are insufficient, because they rely on signatures that are unable to detect novel rootkits or require domain knowledge about the rootkits to be detected. To overcome this challenge, our approach leverages the fact that runtimes of kernel functions targeted by rootkits increase when additional code is executed. The framework outlined in this paper injects probes into the kernel to measure time stamps of functions within relevant system calls, computes distributions of function execution times, and uses statistical tests to detect time shifts. The evaluation of our open-source implementation on publicly available data sets indicates high detection accuracy with an F1 score of 98.7% across five scenarios with varying system states. 

Trace of the Times: Rootkit Detection through Temporal Anomalies in Kernel Activity

AI-Powered Malware Analysis in Military Cybersecurity: A Deep Learning Approach

Microgrids (MGs) are increasingly pivotal for integrating distributed energy resources (DERs) such as photovoltaic and wind systems to enhance sustainability, resilience, and efficiency. However, the variability of renewables necessitates advanced Energy Management Systems (EMS) to ensure reliable, cost-effective, and stable operations. This review evaluates EMS frameworks namely, centralized, decentralized, and distributed, and control strategies including Model Predictive Control (MPC), Artificial Intelligence (AI)-based methods, stochastic and robust models, Multi-Agent Systems (MAS), classical optimization, bio-inspired heuristics, and hybrid algorithms. Findings reveal that centralized EMS achieves global optimization but is limited by scalability and single-point vulnerabilities, decentralized EMS enhances autonomy but sacrifices holistic coordination, and distributed EMS, particularly MAS, offers scalability and resilience when supported by robust communication. While MPC and AI-driven models provide strong adaptability and forecasting accuracy, they are computationally demanding, whereas stochastic and robust methods improve reliability under uncertainty but face efficiency challenges. Hybrid EMS emerges as the most effective, integrating complementary approaches to optimize costs, renewable utilization, emissions reduction, and resilience. The study also underscores the growing vulnerability of intelligent EMS to cyberattacks, emphasizing the need for cyber-resilient architectures with detection and self-healing mechanisms. Furthermore, emerging communication innovations, including IoT networks and 5G technologies, are highlighted as critical enablers of scalability, reliability, and secure coordination in future EMS. Advancing adaptive AI-driven hybrid EMS, resilient communication infrastructures, and experimental validation will be key to achieving intelligent, secure, and sustainable microgrid operations. 

A review of intelligent control strategies for energy management systems in microgrids

Machine learning has witnessed tremendous growth in its adoption and advancement in the last decade. The evolution of machine learning from traditional algorithms to modern deep learning architectures has shaped the way today's technology functions. Its unprecedented ability to discover knowledge/patterns from unstructured data and automate the decision-making process led to its application in wide domains. High flying machine learning arena has been recently pegged back by the introduction of adversarial attacks. Adversaries are able to modify data, maximizing the classification error of the models. The discovery of blind spots in machine learning models has been exploited by adversarial attackers by generating subtle intentional perturbations in test samples. Increasing dependency on data has paved the blueprint for ever-high incentives to camouflage machine learning models. To cope with probable catastrophic consequences in the future, continuous research is required to find vulnerabilities in form of adversarial and design remedies in systems. This survey aims at providing the encyclopedic introduction to adversarial attacks that are carried out against malware detection systems. The paper will introduce various machine learning techniques used to generate adversarial and explain the structure of target files. The survey will also model the threat posed by the adversary and followed by brief descriptions of widely accepted adversarial algorithms. Work will provide a taxonomy of adversarial evasion attacks on the basis of attack domain and adversarial generation techniques. Adversarial evasion attacks carried out against malware detectors will be discussed briefly under each taxonomical headings and compared with concomitant researches. Analyzing the current research challenges in an adversarial generation, the survey will conclude by pinpointing the open future research directions.

A Survey on Adversarial Attacks for Malware Analysis

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A Survey on Adversarial Attacks for Malware Analysis