The continuous threat of malware against digital systems exists because its attack methods develop rapidly, reducing the effectiveness of traditional detection systems. Current static and dynamic analysis methods for malware detection face challenges with scalability and robustness when handling large and complex malware samples. Computer vision now shows that malware binaries contain specific structural patterns when displayed as grayscale images, which can be used for classification. This study investigates GCViT for malware detection through its application to the Malimg dataset, which contains 9,337 samples from 25 malware families. The dataset underwent preprocessing through a two-step... More >

VR empirical data include the sensitive affective and behavioral data (e.g. gaze patterns, physiological signals, facial expressions), placing a person at the risk of re-identification. Tailored privacy-preserving approaches to immersive data have not, however, received much attention in the recent affective-computing investigations. In the present paper, a differentially-privacy (DP) framework is presented as that allows anonymizing VR-based studies of emotion without losing scientific value. The first one is the quantification of re-identification risks through empirical linkage and inference attacks and then the mitigation of the risk through DP mechanisms such as Laplace noise, that soug... More >

This paper presents the Singularity Cipher, a novel cryptographic-steganographic framework that integrates topological transformations and visual paradoxes to achieve multidimensional security. Inspired by the non-orientable properties of the Klein bottle—constructed from two Möbius strips—the cipher applies symbolic twist functions to simulate topological traversal, producing high confusion and diffusion in the ciphertext. The resulting binary data is then encoded using perceptual illusions, such as the missing square paradox, to visually obscure the presence of encrypted content. Unlike conventional ciphers that rely solely on algebraic complexity, the Singularity Cipher introduces a... More >

The emerging field of underwater sensor networks (UWSN) has a vital potential shaping the modern and future landscape that assists in measuring water quality, pollution tracking, and identification of underwater habitats. The challenging conditions in the UWSN environments raise data and security concerns in terms of outliers related to the complicated communication system, poor visibility, and limited resources. The data quality and network efficiency may be affected due to these unwanted conditions, giving rise to certain malicious activities in the network. This study aims to enhance the outlier identification process in terms of security and quality perspectives using the Long Short-Term... More >

The exponential growth in security and storage requirements has led to increasing demand for high-performance solutions, particularly in managing large-scale data objects such as big binaries. Virtual Machines (VMs), a prime example of such binaries, are widely employed across desktops and dynamic server environments to deliver isolated execution contexts while minimizing hardware overhead. However, the proliferation of big binaries imposes significant challenges on both storage infrastructure and secure data transmission mechanisms. To address these issues, this paper proposes a novel "Combined Big Binaries Compression and Encryption" (CBBCE) scheme that jointly performs compression and enc... More >

Presents the inaugural editorial for this issue of the publication. More >