Seokjoo Shin

Nazmul Islam, Seokjoo Shin

Differentiating malware is important to determine their behaviors and level of threat; as well as to devise defensive strategy against them. In response, various anti-malware systems have been developed to distinguish between different malwares. However, most of the recent malware families are Artificial Intelligence (AI) enable and can deceive traditional anti-malware systems using different obfuscation techniques. Therefore, only AI-enabled anti-malware system is robust against these techniques and can detect different features in the malware files that aid in malicious activities. In this study we review two AI-enabled techniques for detecting malware in Windows and Android operating system, respectively. Both the techniques achieved perfect accuracy in detecting various malware families.

Ijaz Ahmad, Seokjoo Shin

The traditional communication model based on chain of multiple independent processing blocks is constraint to efficiency and introduces artificial barriers. Thus, each individually optimized block does not guarantee end-to-end performance of the system. Recently, end-to-end learning of communications systems through machine learning (ML) have been proposed to optimize the system metrics jointly over all components. These methods show performance improvements but has a limitation that it requires a differentiable channel model. In this study, we have summarized the existing approaches that alleviates this problem. We believe that this study will provide better understanding of the topic and an insight into future research in this field.

Ijaz Ahmad, Seokjoo Shin

The performance of an edge detector can be improved when assisted with an effective edge map quality measure. Several evaluation methods have been proposed resulting in different performance score for the same candidate edge map. However, an effective measure is the one that can be automated and which correlates with human judgement perceived quality of the edge map. Distance-based edge map measures are widely used for assessment of edge map quality. These methods consider distance and statistical properties of edge pixels to estimate a performance score. The existing methods can be automated; however, they lack perceptual features. This paper presents edge map quality measure based on Just-Noticeable-Difference (JND) feature of human visual system, to compensate the shortcomings of distance-based edge measures. For this purpose, we have designed constant stimulus experiment to measure the JND value for two spatial alternative. Experimental results show that JND based distance calculation outperforms existing distance-based measures according to subjective evaluation.

Nazmul Islam, Seokjoo Shin

Deep learning (DL) has revolutionized wireless communication systems by introducing datadriven end-to-end (E2E) learning, where the physical layer (PHY) is transformed into DL architectures to achieve peak optimization. Leveraging DL for E2E optimization in PHY significantly enhances its adaptability and performance in complex wireless environments, meeting the demands of advanced network systems such as 5G and beyond. Furthermore, this evolution of data-driven PHY optimization has also enabled advanced semantic applications across various modalities, including text, image, audio, video, and multimodal transmissions. These applications elevate communication from bit-level to semantic-level intelligence, making it capable of discerning context and intent. Although the PHY, as a DL architecture, plays a crucial role in enabling semantic communication (SemCom) systems, comprehensive studies that integrate both E2E communication and SemCom systems remain significantly underexplored. This highlights the novelty and potential of these integrative fields, marking them as a promising research domain. Therefore, this article provides a comprehensive review of the emerging field of data-driven PHY for E2E communication systems, emphasizing their role in enabling semantic applications across various modalities. It also identifies key challenges and potential research directions, serving as a crucial guide for future advancements in DL for E2E communication and SemCom systems.

Ijaz Ahmad, Seokjoo Shin

In the recent years, pixel-based perceptual algorithms have been successfully applied for privacy-preserving deep learning (DL) based applications. However, their security has been broken in subsequent works by demonstrating a chosen-plaintext attack. In this paper, we propose an efficient pixel-based perceptual encryption method. The method provides a necessary level of security while preserving the intrinsic properties of the original image. Thereby, can enable deep learning (DL) applications in the encryption domain. The method is substitution based where pixel values are XORed with a sequence (as opposed to a single value used in the existing methods) generated by a chaotic map. We have used logistic maps for their low computational requirements. In addition, to compensate for any inefficiency because of the logistic maps, we use a second key to shuffle the sequence. We have compared the proposed method in terms of encryption efficiency and classification accuracy of the DL models on them. We have validated the proposed method with CIFAR datasets. The analysis shows that when classification is performed on the cipher images, the model preserves accuracy of the existing methods while provides better security.