arXiv subjects

Jannatul Ferdous

Publications and source records attributed to Jannatul Ferdous.

13 recordsLinked to original sources

arXiv2026

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design

Ransomware detection is a security-critical task in which false negatives and false positives have unequal operational consequences. Conventional machine learning detectors often use symmetric objectives that penalize missed ransomware detections and benign false alarms equally, although a false negative can cause irreversible encryption, operational disruption, and high recovery cost, whereas a false positive is usually reversible. This study proposes a Security-Aware Deep Reinforcement Learning (SA-DRL) framework that embeds false-negative and false-positive cost asymmetry into the reinforcement learning reward signal to prioritize missed-detection reduction. The framework also introduces a Security-Optimal Model Selection (SOMS) criterion and an adaptive episode-level sample-ordering mechanism. Four deep reinforcement learning agents, DQN, DDQN, PPO, and A2C, were evaluated using a symmetric baseline reward (R1) and a security-aware asymmetric reward (R2). Experiments used four discount factors, five-fold cross-validation, and three random seeds, resulting in 480 training runs on a balanced ransomware detection dataset. The SOMS criterion selects models by prioritizing false-negative rate, followed by F1-score and training time. Results show that asymmetric reward shaping improves security-oriented detection performance. The SOMS-selected configuration, DDQN with R2 and gamma = 0.1, achieved a false-negative rate of 0.0080, an F1-score of 0.9915, and an AUC of 0.998, reducing missed detections by 67.6% compared with the best supervised baseline. Across all configurations, R2 reduced the mean false-negative rate by 43% relative to R1. These findings show that reward-function design is important for security-sensitive ransomware detection.

cs.CR↗

arXiv2026

Auditable Machine Unlearning for Privacy-Compliant Ransomware Detection Using Multi-Shard SISA and Deep Reinforcement Learning

Ransomware poses an escalating cybersecurity threat as attackers continuously modify behavioral patterns to evade static defenses. Although existing machine learning-based detectors often achieve strong predictive performance, they generally assume fixed training data and do not support the selective removal of previously learned samples. This limitation conflicts with privacy regulations such as the GDPR and CCPA, which require the removal of sensitive user data upon request. To address this challenge, we propose an auditable ransomware detection and unlearning framework that integrates deep reinforcement learning with multi-shard SISA retraining. In the proposed system, a Double Deep Q-Network (DDQN) learns a reward-guided detection policy from behavioral features under asymmetric security costs, while multi-shard SISA enables privacy-compliant selective sample removal through shard-level retraining. The framework was evaluated using four criteria: utility preservation, oracle-based forgetting validation, membership inference auditing, and computational efficiency. On a balanced Windows 11 behavioral dataset comprising 2,000 samples and 103 features, the baseline DDQN detector achieved an F1 score of 0.9925 and an AUC of 0.9983. The experimental results show that single-shard unlearning maintains minimal utility degradation and low oracle disagreement, whereas moderate shard counts (M = 5-10) provide the best efficiency-performance trade-off, reducing retraining time to 5-30 s compared with 80-330 s for full retraining. In addition, the membership inference scores remain close to 0.5 across most configurations, indicating limited privacy leakage after unlearning. These findings demonstrate that a privacy-compliant ransomware detection framework can jointly achieve high detection performance, auditable deletion verification, and efficient sample removal.

cs.CR↗

arXiv2026

Brain Tumor Classification in MRI Images: A Computationally Efficient Convolutional Neural Network

Improving patient outcomes depends on the prompt and accurate diagnosis of brain tumors, but manual MRI scan analysis is still time-consuming and unreliable. Although deep learning has shown promise, many of the models that are now in use are computationally intensive and have difficulty handling the intrinsic complexity and variety of different types of brain tumors. In this work, we propose a lightweight yet high-performing Convolutional Neural Network (CNN) for multi-class brain tumor classification, employing MRI images to target gliomas, meningiomas, pituitary tumors, and healthy (no tumor) instances. The model was rigorously evaluated on two publicly accessible datasets from Figshare and Kaggle. Leveraging efficient feature extraction and optimized training strategies, our CNN achieved classification accuracies of 99.03% and 99.28%, along with ROC scores of 99.88% and 99.94% on Dataset 1 and Dataset 2, respectively-all while utilizing significantly fewer parameters than popular pre-trained architectures. In contrast to cutting-edge models like DenseNet201, MobileNetV2, VGG19, Xception, InceptionV3, and ResNet50, our approach consistently demonstrated superior performance with reduced computational overhead. These findings highlight the potential of the proposed model as a practical and reliable diagnostic aid in clinical environments.

eess.IV↗

arXiv2026

TL-RL-FusionNet: An Adaptive and Efficient Reinforcement Learning-Driven Transfer Learning Framework for Detecting Evolving Ransomware Threats

Modern ransomware exhibits polymorphic and evasive behaviors by frequently modifying execution patterns to evade detection. This dynamic nature disrupts feature spaces and limits the effectiveness of static or predefined models. To address this challenge, we propose TL-RL-FusionNet, a reinforcement learning (RL)-guided hybrid framework that integrates frozen dual transfer learning (TL) backbones as feature extractors with a lightweight residual multilayer perceptron (MLP) classifier. The RL agent supervises training by adaptively reweighting samples in response to variations in observable ransomware behavior. Through reward and penalty signals, the agent prioritizes complex cases such as stealthy or polymorphic ransomware employing obfuscation, while down-weighting trivial samples including benign applications with simple file I/O operations or easily classified ransomware. This adaptive mechanism enables the model to dynamically refine its strategy, improving resilience against evolving threats while maintaining strong classification performance. The framework utilizes dynamic behavioral features such as file system activity, registry changes, network traffic, API calls, and anti-analysis checks, extracted from sandbox-generated JSON reports. These features are transformed into RGB images and processed using frozen EfficientNetB0 and InceptionV3 models to capture rich feature representations efficiently. Final classification is performed by a lightweight residual MLP guided by an RL (Q-learning) agent. Experiments on a balanced dataset of 1,000 samples (500 ransomware, 500 benign) show that TL-RL-FusionNet achieves 99.1% accuracy, 98.6% precision, 99.6% recall, and 99.74% AUC, outperforming non-RL baselines by up to 2.5% in accuracy and 3.1% in recall. Efficiency analysis shows 55% lower training time and 59% reduced RAM usage, demonstrating suitability for real-world deployment.

cs.CR↗

arXiv2026

Privacy-Aware Machine Unlearning with SISA for Reinforcement Learning-Based Ransomware Detection

Ransomware detection systems increasingly rely on behavior-based machine learning to address evolving attack strategies. However, emerging privacy compliance, data governance, and responsible AI deployment demand not only accurate detection but also the ability to efficiently remove the influence of specific training samples without retraining the models from scratch. In this study, we present a privacy-aware machine unlearning evaluation framework for reinforcement learning (RL)-based ransomware detection built on Sharded, Isolated, Sliced, and Aggregated (SISA) training. The framework enables efficient data deletion by retraining only the affected model shards rather than the entire detector, reducing the retraining cost while preserving detection performance. We conduct a controlled comparative study using value-based RL agents, including Deep Q-Network (DQN) and Double Deep Q-Network (DDQN), under identical experimental settings with a cost-sensitive reward design and 5-fold cross-validation on Windows 11 ransomware dataset. Detection confidence is evaluated using a continuous Q-score margin, enabling ROC-AUC analysis beyond binary predictions. For unlearning, the dataset is partitioned into five shards with majority-vote aggregation, and a fast-unlearning path is evaluated by deleting 5% of the samples from a single shard and retraining only that shard. Results show that SISA-based unlearning incurs negligible utility degradation (<= 0.05 percent F1 drop) while substantially reducing retraining time relative to full SISA retraining. DDQN exhibits slightly improved stability and lower utility loss than DQN, while both agents maintain near identical in-distribution performance after unlearning. These findings indicate that SISA provides an efficient unlearning mechanism for RL-based ransomware detection, supporting privacy-aware deployment without compromising security effectiveness.

cs.CR↗

arXiv2025

Bigger is Faster in the Adaptive Immune Response

Zoonotic pathogens represent a growing global risk, yet the speed of adaptive immune activation across mammalian species remains poorly understood. Despite orders-of-magnitude differences in size and metabolic rate, we show that the time to initiate adaptive immunity is remarkably consistent across species. To understand this invariance, we analyse empirical data showing how the numbers and sizes of lymph nodes scale with body mass, finding that larger animals have both more and larger lymph nodes. Using scaling theory and our mathematical model, we show that larger lymph nodes enable faster search times, conferring an advantage to larger animals that otherwise face slower biological times. This enables mammals to maintain, or even accelerate, the time to initiate the adaptive immune response as body size increases. We validate our analysis in simulations and compare it to empirical data.

physics.bio-ph↗

arXiv2025

SARS-CoV-2 Wastewater Genomic Surveillance: Approaches, Challenges, and Opportunities

During the SARS-CoV-2 pandemic, wastewater-based genomic surveillance (WWGS) emerged as an efficient viral surveillance tool that takes into account asymptomatic cases and can identify known and novel mutations and offers the opportunity to assign known virus lineages based on the detected mutations profiles. WWGS can also hint towards novel or cryptic lineages, but it is difficult to clearly identify and define novel lineages from wastewater (WW) alone. While WWGS has significant advantages in monitoring SARS-CoV-2 viral spread, technical challenges remain, including poor sequencing coverage and quality due to viral RNA degradation. As a result, the viral RNAs in wastewater have low concentrations and are often fragmented, making sequencing difficult. WWGS analysis requires advanced computational tools that are yet to be developed and benchmarked. The existing bioinformatics tools used to analyze wastewater sequencing data are often based on previously developed methods for quantifying the expression of transcripts or viral diversity. Those methods were not developed for wastewater sequencing data specifically, and are not optimized to address unique challenges associated with wastewater. While specialized tools for analysis of wastewater sequencing data have also been developed recently, it remains to be seen how they will perform given the ongoing evolution of SARS-CoV-2 and the decline in testing and patient-based genomic surveillance. Here, we discuss opportunities and challenges associated with WWGS, including sample preparation, sequencing technology, and bioinformatics methods.

q-bio.GN↗

arXiv2024

Observation of anomalous classical-to-quantum transitions in many-body systems

The correspondence principle bridges the quantum and classical worlds by establishing a direct link between their dynamics. This well-accepted tenant of quantum physics has been explored in quantum systems wherein the number of particles is increased to macroscopic scales. However, theoretical investigations of nanoscale structures have revealed discrepancies when attempting to bridge classical and quantum physics. Here, we report on the experimental observation of anomalous classical-to-quantum transitions in open many-body optical systems. We demonstrate, for the first time, the lack of classical-to-quantum correspondence between a macroscopic optical system and its constituent quantum multiphoton subsystems. In contrast to common belief, we demonstrate that the coherence dynamics of many-body quantum subsystems with up to forty particles can indeed be opposite to that exhibited by the hosting macroscopic system. By employing complex-Gaussian statistics, we show that these effects are universal for open many-body systems. Consequently, our work can have important implications for other fields of physics ranging from condensed matter to nuclear physics.

quant-ph↗

arXiv2024

Multiphoton Quantum Imaging using Natural Light

It is thought that schemes for quantum imaging are fragile against realistic environments in which the background noise is often stronger than the nonclassical signal of the imaging photons. Unfortunately, it is unfeasible to produce brighter quantum light sources to alleviate this problem. Here, we overcome this paradigmatic limitation by developing a quantum imaging scheme that relies on the use of natural sources of light. This is achieved by performing conditional detection on the photon number of the thermal light field scattered by a remote object. Specifically, the conditional measurements in our scheme enable us to extract quantum features of the detected thermal photons to produce quantum images with improved signal-to-noise ratios. This technique shows a remarkable exponential enhancement in the contrast of quantum images. Surprisingly, this measurement scheme enables the possibility of producing images from the vacuum fluctuations of the light field. This is experimentally demonstrated through the implementation of a single-pixel camera with photon-number-resolving capabilities. As such, we believe that our scheme opens a new paradigm in the field of quantum imaging. It also unveils the potential of combining natural light sources with nonclassical detection schemes for the development of robust quantum technologies.

quant-ph↗

arXiv2024

Emergence of multiphoton quantum coherence by light propagation

The modification of the quantum properties of coherence of photons through their interaction with matter lies at the heart of the quantum theory of light. Indeed, the absorption and emission of photons by atoms can lead to different kinds of light with characteristic quantum statistical properties. As such, different types of light are typically associated with distinct sources. Here, we report on the observation of the modification of quantum coherence of multiphoton systems in free space. This surprising effect is produced by the scattering of thermal multiphoton wavepackets upon propagation. The modification of the excitation mode of a photonic system and its associated quantum fluctuations result in the formation of different light fields with distinct quantum coherence properties. Remarkably, we show that these processes of scattering can lead to multiphoton systems with sub-shot-noise quantum properties. Our observations are validated through the nonclassical formulation of the emblematic van Cittert-Zernike theorem. We believe that the possibility of producing quantum systems with modified properties of coherence, through linear propagation, can have dramatic implications for diverse quantum technologies.

quant-ph↗

arXiv2023

Malware Resistant Data Protection in Hyper-connected Networks: A survey

Data protection is the process of securing sensitive information from being corrupted, compromised, or lost. A hyperconnected network, on the other hand, is a computer networking trend in which communication occurs over a network. However, what about malware. Malware is malicious software meant to penetrate private data, threaten a computer system, or gain unauthorised network access without the users consent. Due to the increasing applications of computers and dependency on electronically saved private data, malware attacks on sensitive information have become a dangerous issue for individuals and organizations across the world. Hence, malware defense is critical for keeping our computer systems and data protected. Many recent survey articles have focused on either malware detection systems or single attacking strategies variously. To the best of our knowledge, no survey paper demonstrates malware attack patterns and defense strategies combinedly. Through this survey, this paper aims to address this issue by merging diverse malicious attack patterns and machine learning (ML) based detection models for modern and sophisticated malware. In doing so, we focus on the taxonomy of malware attack patterns based on four fundamental dimensions the primary goal of the attack, method of attack, targeted exposure and execution process, and types of malware that perform each attack. Detailed information on malware analysis approaches is also investigated. In addition, existing malware detection techniques employing feature extraction and ML algorithms are discussed extensively. Finally, it discusses research difficulties and unsolved problems, including future research directions.

cs.CR↗

arXiv2022

Observation of Robust Zero Energy Extended States

Topological edge states arise at the interface of two topologically-distinct structures and have two distinct features: they are localized and robust against symmetry protecting disorder. On the other hand, conventional transport in one dimension is associated with extended states, which typically do not have topological robustness. In this paper, using lossy coupled resonators in one dimension, we demonstrate both theoretically and experimentally the existence of robust states residing in the bulk. We show that they are unusually robust against disorders in coupling between adjacent sites and losses. Our work paves the way to a new form of robust transport that is not limited to boundary phenomena and can be accessed more easily from far field.

cond-mat.mes-hall↗

arXiv2020

MatriVasha: A Multipurpose Comprehensive Database for Bangla Handwritten Compound Characters

At present, recognition of the Bangla handwriting compound character has been an essential issue for many years. In recent years there have been application-based researches in machine learning, and deep learning, which is gained interest, and most notably is handwriting recognition because it has a tremendous application such as Bangla OCR. MatrriVasha, the project which can recognize Bangla, handwritten several compound characters. Currently, compound character recognition is an important topic due to its variant application, and helps to create old forms, and information digitization with reliability. But unfortunately, there is a lack of a comprehensive dataset that can categorize all types of Bangla compound characters. MatrriVasha is an attempt to align compound character, and it's challenging because each person has a unique style of writing shapes. After all, MatrriVasha has proposed a dataset that intends to recognize Bangla 120(one hundred twenty) compound characters that consist of 2552(two thousand five hundred fifty-two) isolated handwritten characters written unique writers which were collected from within Bangladesh. This dataset faced problems in terms of the district, age, and gender-based written related research because the samples were collected that includes a verity of the district, age group, and the equal number of males, and females. As of now, our proposed dataset is so far the most extensive dataset for Bangla compound characters. It is intended to frame the acknowledgment technique for handwritten Bangla compound character. In the future, this dataset will be made publicly available to help to widen the research.

cs.CV↗