arXiv subjects

Mohammad Arif Hossain

Publications and source records attributed to Mohammad Arif Hossain.

18 recordsLinked to original sources

arXiv2026

Distributed Quantum-Assisted Robust AoII Minimization in Satellite-Ground Integrated Edge Networks

Mission-critical edge applications in 6G-and-beyond networks, such as autonomous systems, disaster response, and infrastructure monitoring, require that the edge decision-maker's estimate of a monitored process remain correct, not merely up to date. Satellite-ground integrated networks (SAGIN) often provide the only connectivity in infrastructure-limited or disaster-affected regions, yet satellite handover and shadowing interrupt links, during which the process may change state several times, leaving the edge node's estimate substantially wrong. Age of information (AoI) tracks only elapsed time and cannot distinguish a harmless delay from a dangerous error. We instead adopt the age of incorrect information (AoII), which penalizes both the duration and magnitude of estimation error, and formulate, to our knowledge, the first network-level, multi-node AoII minimization problem over SAGIN under stochastic handover and shadowing. We propose SENTINEL, a distributed hybrid quantum-classical framework that jointly schedules update rates, satellite-to-base-station associations, and bandwidth allocation to minimize the worst-case time-average AoII. Because AoII is history dependent, it resists per-slot optimization; a renewal-interval decomposition that separates source dynamics from channel disruption yields a closed-form AoII cost per inter-delivery interval. The resulting robust scheduling problem, VANGUARD, is formulated as a QUBO, mapped to an Ising Hamiltonian, and solved via distributed QAOA with ADMM-based coordination across satellite and ground domains. Every returned schedule carries a certified worst-case AoII over all disruption scenarios. Simulations show that SENTINEL outperforms learning-based and random baselines, matches a state-aware threshold policy in small networks while additionally providing a worst-case guarantee, and remains close to an exact minimax reference.

cs.NI↗

arXiv2026

Cognitive Graph Intelligence for Adaptive and Robust DDoS Attack Detection in Next Generation Networks

Distributed Denial-of-Service (DDoS) attacks threaten network availability, requiring a cognitive detection process that senses traffic, infers intent, and supports an adaptive response under severe class imbalance and non-stationary conditions. This paper proposes a Graph-based Generative Adversarial Network (GraphGAN) that serves as the cognitive detection engine for this task. GraphGAN captures the relational structure among traffic flows while addressing imbalance through adversarial generation of synthetic samples. Sequential flows are converted into $k$-nearest neighbor graphs using sliding windows to preserve feature-similarity and temporal dependencies among flows. The generator learns the distribution of DDoS attacks to synthesize realistic minority samples, while a Graph Convolutional Network (GCN)-based discriminator distinguishes real from synthetic graph data. A separate GCN classifier, trained on the balanced dataset, performs the final detection decision. Evaluations on four benchmark datasets show that GraphGAN achieves superior accuracy, precision, and recall compared to state-of-the-art approaches, particularly in data-scarce scenarios. By integrating temporal graph construction, adversarial augmentation, and GCN classification, GraphGAN effectively models coordinated attack behaviors and mitigates class imbalance, providing a robust and topology-aware solution for intrusion detection in data-constrained environments.

cs.AI↗

arXiv2026

SATLOCK: Handover-Coupled Scheduling for Weather-Resilient Quantum Key Distribution over LEO Constellations

Routing quantum keys over low-earth-orbit (LEO) satellite constellations is harder than classical routing: satellite handovers couple consecutive scheduling decisions, stochastic cloud cover can silently zero a ground link, and finite-key effects eliminate short, low-elevation passes entirely. We present SATLOCK, a handover-aware Quantum Key Distribution (QKD) routing framework that combines (i) a composite channel model incorporating atmospheric loss, pointing jitter, Markov cloud cover, decoy-state estimation, and finite-key correction; (ii) an integer linear program (ILP) giving a provable handover-aware throughput upper bound; and (iii) a decentralized deep Q-network (DQN) baseline for weather-adaptive online routing. We evaluate two contention regimes on a Walker constellation serving intercontinental demands. In low contention (16 satellites, 6 demands), the ILP delivers 1,311 Mbit while the strongest heuristics reach 95--96\% of ILP. In high contention (8 satellites, 12 demands), where handovers become binding, heuristics drop to 89.5\% of ILP. The DQN agent reaches 91.8\% and 84.6\% of ILP in the two regimes; it learns effective per-demand weather policies but is limited in aggregate by the lack of cross-demand coordination.

cs.CR↗

arXiv2026

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models

Large Language Models (LLMs) have undergone a shift from stateless conversational interfaces to autonomous agents capable of multi-step planning, tool invocation, code execution, and maintaining persistent memory. When these agents operate with real-world privileges---calling APIs, modifying files, and querying databases---a compromised reasoning step can trigger unauthorized data access, irreversible state changes, or cascading failures, yet the security research community has not kept pace. To quantify the state of the field, we conducted a systematic literature review under PRISMA 2020 guidelines across six databases, screening 743 records and retaining 85 papers (2023--2025) on agentic LLM security. Attack research outpaces defense work by 3.9:1. Perception-layer vulnerabilities (prompt injection, jailbreaking, adversarial perturbations) dominate, accounting for 66\% of papers, while action-layer vulnerabilities (tool misuse, code injection, sandbox escape) appear in only 4.7\%, misaligned with real-world risk. Code execution security accounts for 3.5\%, and tool-augmented agents 12\%. We contribute a four-layer taxonomy mapping 13 vulnerability types across perception, brain, action, and interaction layers, and identify seven open problems centered on containment. Agentic LLM insecurity stems from architectural coupling, where weak isolation allows vulnerabilities to propagate across layers.

cs.CR↗

arXiv2026

An Empirical Study of Output-to-Input Loops for Black-Box Backdoor Detection in Fine-Tuned Open-Weight LLMs

Anyone can upload a fine-tuned large language model (LLM) to a public repository and claim it is safe. A backdoored model behaves normally on ordinary inputs until a hidden trigger fires, and a user with no training data, clean reference weights, or the trigger phrase has no clear way to check the model before using it. We introduce and empirically evaluate self-feeding, a black-box test method that feeds a model's own output back as its next input, so the text drifts away from the starting prompt and toward the data the model was fine-tuned on. We test self-feeding against a repeated same-prompt baseline on six open-weight LLMs (3B-15B parameters), each fine-tuned with backdoors spanning eleven attack categories, using twenty ordinary starting prompts and chains of up to ten steps. Self-feeding finds backdoors in five of six models at 92.0\% pooled precision, while the same-prompt baseline succeeds on only one of 120 prompt-model pairs; chains that begin with a joke request, an arithmetic question, or a coffee recipe all reach a trigger within a few steps. Recall per prompt is low (19.2\%), and we show why it still adds up to much higher detection at the model level once several starting prompts are used. We also report where the method falls short: one model was never triggered, and self-feeding produced two false positives that the same-prompt baseline cannot produce. Cutting the chains to four steps keeps every model-level detection at 100\% precision while using 60\% fewer queries. Needing only text-level query access and a way to recognize malicious output, self-feeding offers a cheap first check on a downloaded model.

cs.CR↗

arXiv2026

The Containment Gap: How Deployed Agentic AI Frameworks Fail Public-Facing Safety Requirements

Agentic large language model systems that autonomously invoke tools, maintain persistent memory, and execute multi-step plans are increasingly deployed in public-facing domains, including government services, healthcare triage, and financial advising. We ask whether the frameworks used to build these systems provide architectural-level structural safety guarantees. Applying six containment principles derived from a compositional model of agentic architectures, we audit three dominant frameworks (LangChain, AutoGPT, and OpenAI Agents SDK) and find no native compliance in any of them. Memory integrity, a defense against one of the most prevalent vulnerability classes, is not observed in any of the three evaluated frameworks. We validate these findings empirically: in a simulated government benefits agent built on LangChain, a single memory-poisoning write induces persistent targeted corruption across all tested seeds and backends, increasing the wrongful denial rate for targeted applicants to 88.9%. Under a complex five-factor policy, the same attack preserves aggregate accuracy while increasing targeted wrongful denials by 3.5x, rendering the corruption difficult to detect through standard monitoring. We then introduce two lightweight containment mechanisms: a memory integrity validator and a policy gate, which eliminate both attack vectors with sub-millisecond overhead (<0.2ms per call). We conclude that the current agentic framework ecosystem may not yet meet secure-by-default expectations for public-facing deployments and outline priority architectural interventions to enable trustworthy deployment in high-stakes, socially impactful applications.

cs.AI↗

arXiv2024

PhishGuard: A Multi-Layered Ensemble Model for Optimal Phishing Website Detection

Phishing attacks are a growing cybersecurity threat, leveraging deceptive techniques to steal sensitive information through malicious websites. To combat these attacks, this paper introduces PhishGuard, an optimal custom ensemble model designed to improve phishing site detection. The model combines multiple machine learning classifiers, including Random Forest, Gradient Boosting, CatBoost, and XGBoost, to enhance detection accuracy. Through advanced feature selection methods such as SelectKBest and RFECV, and optimizations like hyperparameter tuning and data balancing, the model was trained and evaluated on four publicly available datasets. PhishGuard outperformed state-of-the-art models, achieving a detection accuracy of 99.05% on one of the datasets, with similarly high results across other datasets. This research demonstrates that optimization methods in conjunction with ensemble learning greatly improve phishing detection performance.

cs.CR↗

arXiv2022

AI in 6G: Energy-Efficient Distributed Machine Learning for Multilayer Heterogeneous Networks

Adept network management is key for supporting extremely heterogeneous applications with stringent quality of service (QoS) requirements; this is more so when envisioning the complex and ultra-dense 6G mobile heterogeneous network (HetNet). From both the environmental and economical perspectives, non-homogeneous QoS demands obstruct the minimization of the energy footprints and operational costs of the envisioned robust networks. As such, network intelligentization is expected to play an essential role in the realization of such sophisticated aims. The fusion of artificial intelligence (AI) and mobile networks will allow for the dynamic and automatic configuration of network functionalities. Machine learning (ML), one of the backbones of AI, will be instrumental in forecasting changes in network loads and resource utilization, estimating channel conditions, optimizing network slicing, and enhancing security and encryption. However, it is well known that ML tasks themselves incur massive computational burdens and energy costs. To overcome such obstacles, we propose a novel layer-based HetNet architecture which optimally distributes tasks associated with different ML approaches across network layers and entities; such a HetNet boasts multiple access schemes as well as device-to-device (D2D) communications to enhance energy efficiency via collaborative learning and communications.

cs.NI↗

arXiv2018

Radio Resource Management for Dynamic Channel Borrowing Scheme in Wireless Networks

Provisioning of Quality of Service (QoS) is the key concern for Radio Resource Management now-a-days. In this paper, an efficient dynamic channel borrowing architecture has been proposed that ensures better QoS. The proposed scheme lessens the problem of excessive overall call blocking probability without sacrificing bandwidth utilization. If a channel is borrowed from adjacent cells and causing interference, we also propose architecture that diminishes the interference problem. The numerical results show comparison between the proposed scheme and the conventional scheme before channel borrowing process. The results show a satisfactory performance that are in favor of the proposed scheme, in case of overall call blocking probability, bandwidth utilization and interference management.

cs.NI↗

arXiv2018

Interference Declination for Dynamic Channel Borrowing Scheme in Wireless Network

In modern days, users in the wireless networks are increasing drastically. It has become the major concern for researchers to manage the maximum users with limited radio resource. Interference is one of the biggest hindrances to reach the goal. In this paper, being deep apprehension of the issue, an efficient dynamic channel borrowing scheme is proposed that ensures better Quality of Service (QoS) with interference declination. We propose that if channels are borrowed from adjacent cells, cell bifurcation will be introduced that ensures interference declination when the borrowed channels have same frequency band. We also propose a scheme that inactivates the unoccupied interfering channels of adjacent cells, instead of cell bifurcation for interference declination. The simulation outcomes show acceptable performances in terms of SINR level, system capacity, and outage probability compared to conventional scheme without interference declination that may attract the considerable interest for the users.

cs.NI↗

arXiv2018

A New Guard-Band Call Admission Control Policy Based on Acceptance Factor for Wireless Cellular Networks

To ensure the maximum utilization of the limited bandwidth resources and improved quality of service (QoS) is the key issue for wireless communication networks. Excessive call blocking is a constraint to attain the desired QoS. In cellular network, as the traffic arrival rate increases, call blocking probability (CBP) increases considerably. Paying profound concern, we proposed a scheme that reduces the call blocking probability with approximately steady call dropping probability (CDP). Our proposed scheme also introduces the acceptance factor in specific guard channel where originating calls get access according to the acceptance factor. The analytical performance proves better performance than the conventional new-call bounding scheme in case of higher and lower traffic arrival rate.

cs.NI↗

arXiv2018

Class-Based Interference Management in Wireless Networks

Technological advancement has brought revolutionary change in the converged wireless networks. Due to the existence of different types of traffic, provisioning of Quality of Service (QoS) becomes a challenge in the wireless networks. In case of a congested network, resource allocation has emerged as an effective way to provide the excessive users with desirable QoS. Since QoS for non-real-time traffic are not as strict as for real-time traffic, the unoccupied channels of the adjacent cells can be assigned to the non-real-time traffic to retain QoS for real-time traffic. This results in the intensified bandwidth utilization as well as less interference for the real-time traffic. In this paper, we propose an effective radio resource management scheme that relies on the dynamically assigned bandwidth allocation process. In case of interference management, we classify the traffic into real-time traffic and non-real-time traffic and give priority to the real-time traffic. According to our scheme, the real-time traffic among the excessive number of users are reassigned to the original channels which have been occupied by non-real-time traffic and the non-real-time traffic are allocated to the assigned channels of those real-time traffic. The architecture allows improved signal to interference plus noise ratio (SINR) for real-time traffic along with intensification in the bandwidth utilization of the network. Besides, the increased system capacity and lower outage probability of the network bear the significance of the proposed scheme.

cs.NI↗

arXiv2018

On Demand Cell Sectoring Based Fractional Frequency Reuse in Wireless Networks

In this paper, a dynamic channel assigning along with dynamic cell sectoring model has been proposed that focuses on the Fractional Frequency Reuse (FFR) not only for interference mitigation but also for enhancement of overall system capacity in wireless networks. We partition the cells in a cluster into two part named centre user part (CUP) and edge user part (EUP). Instead of huge traffic, there may be unoccupied channels in the EUPs of the cells. These unoccupied channels of the EUPs can assist the excessive number of users if these channels are assigned with proper interference management. If the number of traffic of a cell surpasses the number of channels of the EUP, then the cell assigns the channels from the EUP of other cells in the cluster. To alleviate the interference, we propose a dynamic cell sectoring scheme. The scheme sectors the EUP of the cell which assigns channels that the assigned channels are provided to the sectored part where these channels receive negligible interference. The performance analysis illustrates reduced call blocking probability as well as better signal to interference plus noise ratio (SINR) without sacrificing bandwidth utilization. Besides, the proposed model ensures lower outage probability.

cs.NI↗

arXiv2016

Survey of Promising Technologies for 5G Networks

As an enhancement of cellular networks, the future-generation 5G network can be considered an ultra-high-speed technology. The proposed 5G network might include all types of advanced dominant technologies to provide remarkable services. Consequently, new architectures and service management schemes for different applications of the emerging technologies need to be recommended to solve issues related to data traffic capacity, high data rate, and reliability for ensuring QoS. Cloud computing, Internet of things (IoT), and software-defined networking (SDN) have become some of the core technologies for the 5G network. Cloud-based services provide flexible and efficient solutions for information and communications technology by reducing the cost of investing in and managing information technology infrastructure. In terms of functionality, SDN is a promising architecture that decouples control planes and data planes to support programmability, adaptability, and flexibility in ever-changing network architectures. However, IoT combines cloud computing and SDN to achieve greater productivity for evolving technologies in 5G by facilitating interaction between the physical and human world. The major objective of this study provides a lawless vision on comprehensive works related to enabling technologies for the next generation of mobile systems and networks, mainly focusing on 5G mobile communications.

cs.NI↗

arXiv2016

Performance analysis of smart digital signage system based on software-defined IoT and invisible image sensor communication

Everything in the world is being connected, and things are becoming interactive. The future of the interactive world depends on the future Internet of Things (IoT). Software-defined networking (SDN) technology, a new paradigm in the networking area, can be useful in creating an IoT because it can handle interactivity by controlling physical devices, transmission of data among them, and data acquisition. However, digital signage can be one of the promising technologies in this era of technology that is progressing toward the interactive world, connecting users to the IoT network through device-to-device communication technology. This article illustrates a novel prototype that is mainly focused on a smart digital signage system comprised of software-defined IoT (SD-IoT) and invisible image sensor communication technology. We have proposed an SDN scheme with a view to initiating its flexibility and compatibility for an IoT network-based smart digital signage system. The idea of invisible communication can make the users of the technology trendier to it, and the usage of unused resources such as images and videos can be ensured. In addition, this communication has paved the way for interactivity between the user and digital signage, where the digital signage and the camera of a smartphone can be operated as a transmitter and a receiver, respectively. The proposed scheme might be applicable to real-world applications because SDN has the flexibility to adapt with the alteration of network status without any hardware modifications while displays and smartphones are available everywhere. A performance analysis of this system showed the advantages of an SD-IoT network over an Internet protocol-based IoT network considering a queuing analysis for a dynamic link allocation process in the case of user access to the IoT network.

cs.NI↗

arXiv2016

Design and Implementation of a Novel Compatible Encoding Scheme in the Time Domain for Image Sensor Communication

This paper presents a modulation scheme in the time domain based on On-Off-Keying and proposes various compatible supports for different types of image sensors. The content of this article is a sub-proposal to the IEEE 802.15.7r1 Task Group (TG7r1) aimed at Optical Wireless Communication (OWC) using an image sensor as the receiver. The compatibility support is indispensable for Image Sensor Communications (ISC) because the rolling shutter image sensors currently available have different frame rates, shutter speeds, sampling rates, and resolutions. However, focusing on unidirectional communications (i.e., data broadcasting, beacons), an asynchronous communication prototype is also discussed in the paper. Due to the physical limitations associated with typical image sensors (including low and varying frame rates, long exposures, and low shutter speeds), the link speed performance is critically considered. Based on the practical measurement of camera response to modulated light, an operating frequency range is suggested along with the similar system architecture, decoding procedure, and algorithms. A significant feature of our novel data frame structure is that it can support both typical frame rate cameras (in the oversampling mode) as well as very low frame rate cameras (in the error detection mode for a camera whose frame rate is lower than the transmission packet rate). A high frame rate camera, i.e., no less than 20 fps, is supported in an oversampling mode in which a majority voting scheme for decoding data is applied. A low frame rate camera, i.e., when the frame rate drops to less than 20 fps at some certain time, is supported by an error detection mode in which any missing data sub-packet is detected in decoding and later corrected by external code. Numerical results and valuable analysis are also included to indicate the capability of the proposed schemes.

cs.OH↗

arXiv2015

Radio Resource Management Based on Reused Frequency Allocation for Dynamic Channel Borrowing Scheme in Wireless Networks

In the modern era, cellular communication consumers are exponentially increasing as they find the system more user-friendly. Due to enormous users and their numerous demands, it has become a mandate to make the best use of the limited radio resources that assures the highest standard of Quality of Service (QoS). To reach the guaranteed level of QoS for the maximum number of users, maximum utilization of bandwidth is not only the key issue to be considered, rather some other factors like interference, call blocking probability etc. are also needed to keep under deliberation. The lower performances of these factors may retrograde the overall cellular networks performances. Keeping these difficulties under consideration, we propose an effective dynamic channel borrowing model that safeguards better QoS, other factors as well. The proposed scheme reduces the excessive overall call blocking probability and does interference mitigation without sacrificing bandwidth utilization. The proposed scheme is modeled in such a way that the cells are bifurcated after the channel borrowing process if the borrowed channels have the same type of frequency band (i.e. reused frequency). We also propose that the unoccupied interfering channels of adjacent cells can also be inactivated, instead of cell bifurcation for interference mitigation. The simulation endings show satisfactory performances in terms of overall call blocking probability and bandwidth utilization that are compared to the conventional scheme without channel borrowing. Furthermore, signal to interference plus noise ratio (SINR) level, capacity, and outage probability are compared to the conventional scheme without interference mitigation after channel borrowing that may attract the considerable concentration to the operators.

cs.NI↗

arXiv2013

For Solving Linear Equations Recombination is a Needless Operation in Time-Variant Adaptive Hybrid Algorithms

Recently hybrid evolutionary computation (EC) techniques are successfully implemented for solving large sets of linear equations. All the recently developed hybrid evolutionary algorithms, for solving linear equations, contain both the recombination and the mutation operations. In this paper, two modified hybrid evolutionary algorithms contained time-variant adaptive evolutionary technique are proposed for solving linear equations in which recombination operation is absent. The effectiveness of the recombination operator has been studied for the time-variant adaptive hybrid algorithms for solving large set of linear equations. Several experiments have been carried out using both the proposed modified hybrid evolutionary algorithms (in which the recombination operation is absent) and corresponding existing hybrid algorithms (in which the recombination operation is present) to solve large set of linear equations. It is found that the number of generations required by the existing hybrid algorithms (i.e. the Gauss-Seidel-SR based time variant adaptive (GSBTVA) hybrid algorithm and the Jacobi-SR based time variant adaptive (JBTVA) hybrid algorithm) and modified hybrid algorithms (i.e. the modified Gauss-Seidel-SR based time variant adaptive (MGSBTVA) hybrid algorithm and the modified Jacobi-SR based time variant adaptive (MJBTVA) hybrid algorithm) are comparable. Also the proposed modified algorithms require less amount of computational time in comparison to the corresponding existing hybrid algorithms. As the proposed modified hybrid algorithms do not contain recombination operation, so they require less computational effort, and also they are more efficient, effective and easy to implement.

cs.NE↗