Nazmul Islam

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.

Nazmul Islam, Mohammad Zulkernine

The rapid expansion of internet of things (IoT) devices has created a pervasive ecosystem where encrypted wireless communications serve as the primary privacy and security protection mechanism. While encryption effectively protects message content, contextual information from packet metadata and statistics inadvertently expose device identities. Various studies have exploited raw packet statistics and their visual representations for device fingerprinting and identification. However, these approaches remain confined to the spatial domain with limited feature representation. Therefore, this paper presents CONTEX-T, a novel framework that exploits device-level information from encrypted traffic metadata using temporal and spectral representation. The experiments show that time-frequency analysis provides new and rich feature representation, revealing a complex and expanding threat landscape that would require robust countermeasures for IoT security management. CONTEX-T first transforms raw packet-length sequences into temporal and spectral representations and then utilizes vision transformers (ViTs) for device identification. We systematically evaluated multiple time-frequency representation techniques and transformer-based models across encrypted traffic samples from various IoT devices. CONTEX-T achieved device classification accuracy exceeding 99% while operating passively on observable contextual metadata. This demonstrates that temporal and spectral signatures persist under strong encryption, highlighting a critical attack surface for IoT network security and management.

Israk Hasan Jone, D. M. Rafiun Bin Masud, Promit Sarker et al.

Shrimp is one of the most widely consumed aquatic species globally, valued for both its nutritional content and economic importance. Shrimp farming represents a significant source of income in many regions; however, like other forms of aquaculture, it is severely impacted by disease outbreaks. These diseases pose a major challenge to sustainable shrimp production. To address this issue, automated disease classification methods can offer timely and accurate detection. This research proposes a deep learning-based approach for the automated classification of shrimp diseases. A dataset comprising 1,149 images across four disease classes was utilized. Six pretrained deep learning models, ResNet50, EfficientNet, DenseNet201, MobileNet, ConvNeXt-Tiny, and Xception were deployed and evaluated for performance. The images background was removed, followed by standardized preprocessing through the Keras image pipeline. Fast Gradient Sign Method (FGSM) was used for enhancing the model robustness through adversarial training. While advanced augmentation strategies, including CutMix and MixUp, were implemented to mitigate overfitting and improve generalization. To support interpretability, and to visualize regions of model attention, post-hoc explanation methods such as Grad-CAM, Grad-CAM++, and XGrad-CAM were applied. Exploratory results demonstrated that ConvNeXt-Tiny achieved the highest performance, attaining a 96.88% accuracy on the test dataset. After 1000 iterations, the 99% confidence interval for the model is [0.953,0.971].

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.

Shinsuke Tamura, Hazim A. Haddad, Nazmul Islam et al.

Simplified verifiable re-encryption mix-net (SVRM) is revised and a scheme for e-voting systems is developed based on it. The developed scheme enables e-voting systems to satisfy all essential requirements of elections. Namely, they satisfy requirements about privacy, verifiability, fairness and robustness. It also successfully protects voters from coercers except cases where the coercers force voters to abstain from elections. In detail, voters can conceal correspondences between them and their votes, anyone can verify the accuracy of election results, and interim election results are concealed from any entity. About incoercibility, provided that erasable-state voting booths which disable voters to memorize complete information exchanged between them and election authorities for constructing votes are available, coercer C cannot know candidates that voters coerced by C had chosen even if the candidates are unique to the voters. In addition, elections can be completed without reelections even when votes were handled illegitimately.