Android malware detection remains a critical challenge as adversaries increasingly employ evasion strategies to bypass traditional defenses. This study introduces a novel ensemble-based detection framework that transforms APK components into RGB Markov images, encoding both structural and statistical byte patterns. A deep ensemble of EfficientNet-B0, ConvNeXt-Small, and Swin-Base models processes these images, integrating their predictions through majority voting to provide reliable decision support. A balanced dataset, KindiDroid, was constructed comprising 95,400 images, including 16,100 unobfuscated samples and 79,300 obfuscated variants generated with thirteen Obfuscapk techniques. The ensemble achieved an F1-score of 99.13% and an AUC of 99.86% on clean data, while preserving over 96% performance across all obfuscation strategies despite being trained solely on unobfuscated samples. Furthermore, resilience against adversarial evasion was demonstrated, with adversarial training restoring performance above 97% under FGSM attacks and above 94% under PGD attacks applied to both clean and obfuscated inputs. These results establish a new benchmark, underscoring the ability of the framework to provide robust defense under realistic black-box and white-box scenarios.

In the rapidly advancing field of wireless communication, Unmanned Aerial Vehicles (UAVs) have become indispensable due to their extensive coverage capabilities and ability to access remote locations. Whether deployed as mobile base stations (BSs) or relays, UAVs significantly enhance network throughput and reliability. Alongside UAVs, Intelligent Reflecting Surfaces (IRS) have emerged as a cost-effective solution for improving communication quality through passive modulation arrays. Despite these advancements, the potential misuse of UAVs poses serious security risks, particularly in the form of communication eavesdropping. To address these challenges, this paper introduces a novel communication framework that integrates a UAV equipped with an adaptive IRS. The primary aim is to boost communication secrecy between BSs and multiple users, even in the presence of several UAV eavesdroppers. This objective is formulated as an optimization problem focused on maximizing the secrecy rate while considering UAV mobility constraints. To solve this non-convex problem, we propose a hybrid strategy that combines Genetic Algorithms and Gradient Descent techniques. This innovative approach efficiently determines suboptimal reflection angles and UAV trajectories for IRS-equipped UAVs, thereby enhancing the security of the communication network. This method not only addresses the complexity of the optimization but also provides a practical pathway to secure communications in environments with high eavesdropping risks.

With the exponential growth of mobile applications, Android systems have become a significant source of big data which provides both vast opportunities and substantial privacy challenges. This makes it essential to adopt secure learning approaches like Federated Learning (FL). FL is a decentralized approach that trains models across distributed data without centralizing sensitive information. However, FL still faces security threats in the scope of big data, where the volume and variety of data increase the risks of sophisticated attacks such as the LOKI attacks. This attack exploits shared model updates in FL to infer and leak sensitive data, even in a decentralized setup. In this paper, we simulate the LOKI attacks within an FL environment using a real-world Android malware detection dataset characterized by dynamic analysis features. We propose a defense mechanism that combines differential privacy and anomaly detection to reduce the impact of LOKI attacks. While this mechanism is designed for mobile systems, where the large volume of data generated by numerous applications mirrors the complexities of big data environments, this approach is adaptable and can be applied to other big data contexts. Through extensive experiments, we demonstrate the effectiveness of the proposed mechanism in enhancing data privacy and securing FL for applications where big data privacy is foremost.

In the dynamic realm of wireless communication, Unmanned Aerial Vehicles (UAVs) have gained increasing prominence due to their exceptional capabilities, which include expensive coverage of large areas and access to challenging and hazardous locations. When employed as mobile base stations or relays, UAVs have shown remarkable enhancements in system throughput and reliability. In addition, Intelligent Reflecting Surfaces (IRS) present a very low cost solution that efficiently enhances wireless communication quality using passive modulation arrays. Nevertheless, the use of UAVs for malicious intents can also introduce heightened security challenges such as communication eavesdropping. In response to these challenges, we present in this paper, a communication framework that incorporates a UAV equipped with an adaptive IRS aiming to enhance the communication secrecy between the Base Station (BS) and Bob in the presence of several UAV eavesdroppers. We formulate the objective as an optimization problem that aims to maximize the secrecy rate while considering the mobility constraints. To address this complex non-convex problem, our innovative solution harnesses a hybrid approach that combines Genetic Algorithms and Gradient Descent techniques, resulting in an efficient computation of suboptimal reflection angles and UAV trajectories for IRS-equipped UAVs towards a more secure communication.

The widespread usage of Android-powered devices in the Internet of Things (IoT) makes them susceptible to evolving cybersecurity threats. Most healthcare devices in IoT networks, such as smart watches, smart thermometers, biosensors, and more, are powered by the Android operating system, where preserving the privacy of user-sensitive data is of utmost importance. Detecting Android malware is thus vital for protecting sensitive information and ensuring the reliability of IoT networks. This article focuses on AI-enabled Android malware detection for improving zero trust security in IoT networks, which requires Android applications to be verified and authenticated before providing access to network resources. The zero trust security model requires strict identity verification for every entity trying to access resources on a private network, regardless of whether they are inside or outside the network perimeter. Our proposed solution, DP-RFECV-FNN, an innovative approach to Android malware detection that employs Differential Privacy (DP) within a Feedforward Neural Network (FNN) designed for IoT networks under the zero trust model. By integrating DP, we ensure the confidentiality of data during the detection process, setting a new standard for privacy in cybersecurity solutions. By combining the strengths of DP and zero trust security with the powerful learning capacity of the FNN, DP-RFECV-FNN demonstrates the ability to identify both known and novel malware types and achieves higher accuracy while maintaining strict privacy controls compared with recent papers. DP-RFECV-FNN achieves an accuracy ranging from 97.78% to 99.21% while utilizing static features and 93.49% to 94.36% for dynamic features of Android applications to detect whether it is malware or benign. These results are achieved under varying privacy budgets, ranging from ϵ=0.1 to ϵ=1.0. Furthermore, our proposed feature selection pipeline enables us to outperform the state-of-the-art by significantly reducing the number of selected features and training time while improving accuracy. To the best of our knowledge, this is the first work to categorize Android malware based on both static and dynamic features through a privacy-preserving neural network model.

The rapid evolutions in microcomputing, mini-hardware manufacturing, and machine-to-machine (M2M) communications have enabled novel Internet-of-Things (IoT) solutions to reshape many networking applications. Healthcare systems are among these applications that have been revolutionized with IoT, introducing an IoT branch known as the Internet-of-Medical Things (IoMT) systems. IoMT systems allow remote monitoring of patients with chronic diseases. Thus, it can provide timely patients' diagnostic that can save their life in case of emergencies. However, security in these critical systems is a major challenge facing their wide utilization. In this article, we present state-of-the-art techniques to secure IoMT systems' data during collection, transmission, and storage. We comprehensively overview IoMT systems' potential attacks, including physical and network attacks. Our findings reveal that most security techniques do not consider various types of attacks. Hence, we propose a security framework that combines several security techniques. The framework covers IoMT security requirements and can mitigate most of its known attacks.

Internet of Things (IoT) devices frequently utilize wireless networks operating in the Industrial, Scientific, and Medical (ISM) Sub‐1 GHz spectrum bands. Compared with higher frequency bands, the Sub‐1 GHz band provides broader coverage and lower power consumption, which are desirable properties for low‐cost IoT applications. However, low‐power and low‐cost IoT modules cause high variability in network performance. The varying influence from real‐world environments additionally undermines wireless propagation and aggravates this variability. We explore these influences and provide a checklist of potential factors affecting wireless network performance in real‐world environments. Using multiple low‐cost IoT modules, we conduct multiple experiments in five real‐world scenarios: indoor, street, open field, ground‐to‐drone (G2D), and drone‐to‐drone (D2D). Specifically, the tests are conducted inside a building, on a straight street with wooded sidewalks and aligned houses, on an open field golf course, and high up in the air between drones. To understand the difficulty of reproducibility in IoT deployments, we studied the effect of factors in four categories. This includes the effect of path (line of sight, distance, and obstruction), configuration (transmit power level), weather (precipitation, temperature, and humidity), and installation (IoT module mobility and position). We find that some of the factors in the path and weather categories have the most influence among all the factors, while the rest have moderate to low impacts. In the end, we provide a complete checklist of all the tested factors, which we believe would be constructive not only to academics but also to industrial practitioners working on wireless IoT systems.

ABSTRACT We present XFPM‐RBAC (XML‐based formal policy language for mobility with role‐based access control), an XML‐based specification language for specification of domain and interdomain security policies with location and mobility constraints based on role‐based access control. XFPM‐RBAC supports specification of locations, mobility, interdomain access rights, role mapping, and separation of duty (SOD) aspects of security policies. XFPM‐RBAC builds upon the FPM‐RBAC security policy model that we have recently proposed. XFPM‐RBAC consists of XML schemas, which define domain security policy, interdomain security policy, locations, mobility, and SOD constructs. A Security Policy Management Interface application is also developed for specification and administration of security policies as a prototype implementation of XFPM‐RBAC. XFPM‐RBAC supports extraction of formal specifications from security policies for the purpose of automated verification of security policies. Automated extraction of formal specifications is based on XSLT (Extensible Stylesheet Language Transformations). Formal specification of security policies together with location and mobility constraints within security policy rules are based on ambient calculus and ambient logic. Copyright © 2012 John Wiley & Sons, Ltd.