arXiv subjects

Ejaz Ahmed

Publications and source records attributed to Ejaz Ahmed.

At least 19 recordsLinked to original sources

arXiv2026

SaliMory: Orchestrating Cognitive Memory for Conversational Agents

Conversational agents that serve as lifelong companions must maintain persistent memory across all interactions. However, simply expanding context windows with raw retrieval degrades reasoning quality, while training memory agents via standard reinforcement learning creates a severe credit assignment bottleneck in a multi-stage pipeline. To solve this, we introduce SALIMORY, a framework that trains a single language model to manage a cognitively-structured memory-spanning user facts, preferences, and working memory. By introducing a hierarchical stage-wise process reward and reward-decomposed contrastive refinement, SALIMORY provides isolated supervision for distinct memory operations (selective filtering, consolidation, and cue-driven recall) end-to-end. SALIMORY cuts memory-attributed failures by one-third, outperforms the state-of-the-art by over 10% in end-to-end accuracy, and more than doubles the Good Personalization rate.

cs.CL↗

arXiv2026

A Multi-Level Integrity Evaluation Framework for Quantum Circuits under Controlled Anomaly Injection

Ensuring the integrity of quantum circuits is a significant challenge in the Noisy Intermediate-Scale Quantum (NISQ) era, where circuits are subject to compilation transformations, hardware constraints, and potential adversarial modifications. Existing validation approaches typically rely on either structural analysis or behavioral evaluation, leading to incomplete assessment of circuit correctness. In this work, we investigate the relationship between structural, interaction-level, and behavioral perspectives of circuit integrity, demonstrating that a single aspect of integrity is insufficient to guarantee circuit integrity; structural similarity alone does not ensure behavioral equivalence. To address this problem, we use a three-layer metric framework that combines the Structural Integrity Score (SIS), the Operational Integrity Score (OIS), and the Interaction Graph Semantic-Logical Score (IGS). SIS captures global structural properties, OIS quantifies behavioral divergence using Jensen-Shannon distance, and IGS models interaction patterns and dependencies in a pre-execution setting. Through controlled anomaly injection on benchmark quantum circuits, we demonstrate that each metric captures a different aspect of circuit deviation. In particular, structural blind-spot cases (SIS >= 0.95) reveal a clear limitation of structural analysis, where OIS detects anomalies in 93.85% of instances, while IGS detects 72.58%. These results highlight that the metrics provide complementary insights and that a single metric is insufficient for reliable circuit validation.

quant-ph↗

arXiv2025

Prompting the Priorities: A First Look at Evaluating LLMs for Vulnerability Triage and Prioritization

Security analysts face increasing pressure to triage large and complex vulnerability backlogs. Large Language Models (LLMs) offer a potential aid by automating parts of the interpretation process. We evaluate four models (ChatGPT, Claude, Gemini, and DeepSeek) across twelve prompting techniques to interpret semi-structured and unstructured vulnerability information. As a concrete use case, we test each model's ability to predict decision points in the Stakeholder-Specific Vulnerability Categorization (SSVC) framework: Exploitation, Automatable, Technical Impact, and Mission and Wellbeing. Using 384 real-world vulnerabilities from the VulZoo dataset, we issued more than 165,000 queries to assess performance under prompting styles including one-shot, few-shot, and chain-of-thought. We report F1 scores for each SSVC decision point and Cohen's kappa (weighted and unweighted) for the final SSVC decision outcomes. Gemini consistently ranked highest, leading on three of four decision points and yielding the most correct recommendations. Prompting with exemplars generally improved accuracy, although all models struggled on some decision points. Only DeepSeek achieved fair agreement under weighted metrics, and all models tended to over-predict risk. Overall, current LLMs do not replace expert judgment. However, specific LLM and prompt combinations show moderate effectiveness for targeted SSVC decisions. When applied with care, LLMs can support vulnerability prioritization workflows and help security teams respond more efficiently to emerging threats.

cs.CR↗

arXiv2025

AssoMem: Scalable Memory QA with Multi-Signal Associative Retrieval

Accurate recall from large scale memories remains a core challenge for memory augmented AI assistants performing question answering (QA), especially in similarity dense scenarios where existing methods mainly rely on semantic distance to the query for retrieval. Inspired by how humans link information associatively, we propose AssoMem, a novel framework constructing an associative memory graph that anchors dialogue utterances to automatically extracted clues. This structure provides a rich organizational view of the conversational context and facilitates importance aware ranking. Further, AssoMem integrates multi-dimensional retrieval signals-relevance, importance, and temporal alignment using an adaptive mutual information (MI) driven fusion strategy. Extensive experiments across three benchmarks and a newly introduced dataset, MeetingQA, demonstrate that AssoMem consistently outperforms SOTA baselines, verifying its superiority in context-aware memory recall.

cs.CL↗

arXiv2025

Imbibition of Oil in Dry and Prewetted Calcite Nanopores

Fluid imbibition into porous media featuring nanopores is ubiquitous in applications such as oil recovery from unconventional reservoirs and material processing. While the imbibition of pure fluids has been extensively studied, the imbibition of fluid mixture is little explored. Here we report the molecular dynamics study of the imbibition of model crude oil into nanometer-wide mineral pores, both when pore walls are dry and prewetted by a residual water film. Results show the fastest imbibition and quickest propagation of molecularly thin precursor films ahead of the oil meniscus in the dry pore system. The presence of a thin water film on pore walls corresponding to an environmental relative humidity of 30% slows down but still allows the spontaneous imbibition of single-component oil. Introducing polar components into the oil slows down the imbibition into dry nanopores, due partly to the clogging of the pore entrance. Strong selectivity toward nonpolar oil is evident. The slowdown of imbibition by polar oil is less significant in the prewetted pores than in dry pores, but the selectivity toward nonpolar oil remains strong.

cond-mat.soft↗

arXiv2025

Enhancing Malware Fingerprinting through Analysis of Evasive Techniques

As malware detection evolves, attackers adopt sophisticated evasion tactics. Traditional file-level fingerprinting, such as cryptographic and fuzzy hashes, is often overlooked as a target for evasion. Malware variants exploit minor binary modifications to bypass detection, as seen in Microsoft's discovery of GoldMax variations (2020-2021). However, no large-scale empirical studies have assessed the limitations of traditional fingerprinting methods on real-world malware samples or explored improvements. This paper fills this gap by addressing three key questions: (a) How prevalent are file variants in malware samples? Analyzing 4 million Windows Portable Executable (PE) files, 21 million sections, and 48 million resources, we find up to 80% deep structural similarities, including common APIs and executable sections. (b) What evasion techniques are used? We identify resilient fingerprints (clusters of malware variants with high similarity) validated via VirusTotal. Our analysis reveals non-functional mutations, such as altered section numbers, virtual sizes, and section names, as primary evasion tactics. We also classify two key section types: malicious sections (high entropy >5) and camouflage sections (entropy = 0). (c) How can fingerprinting be improved? We propose two novel approaches that enhance detection, improving identification rates from 20% (traditional methods) to over 50% using our refined fingerprinting techniques. Our findings highlight the limitations of existing methods and propose new strategies to strengthen malware fingerprinting against evolving threats.

cs.CR↗

arXiv2024

VulMatch: Binary-level Vulnerability Detection Through Signature

Similar vulnerability repeats in real-world software products because of code reuse, especially in wildly reused third-party code and libraries. Detecting repeating vulnerabilities like 1-day and N-day vulnerabilities is an important cyber security task. Unfortunately, the state-of-the-art methods suffer from poor performance because they detect patch existence instead of vulnerability existence and infer the vulnerability signature directly from binary code. In this paper, we propose VulMatch to extract precise vulnerability-related binary instructions to generate the vulnerability-related signature. VulMatch detects vulnerability existence based on binary signatures. Unlike previous approaches, VulMatch accurately locates vulnerability-related instructions by utilizing source and binary codes. Our experiments were conducted using over 1000 vulnerable instances across seven open-source projects. VulMatch significantly outperformed the baseline tools Asm2vec and Palmtree. Besides the performance advantages over the baseline tools, VulMatch offers a better feature by providing explainable reasons during vulnerability detection. Our empirical studies demonstrate that VulMatch detects fine-grained vulnerability that the state-of-the-art tools struggle with. Our experiment on commercial firmware demonstrates VulMatch is able to find vulnerabilities in real-world scenario.

cs.CR↗

arXiv2023

Open Source-based Over-The-Air 5G New Radio Sidelink Testbed

The focus of this paper is to demonstrate an over-the-air (OTA) 5G new radio (NR) sidelink communication prototype. 5G NR sidelink communications allow NR UEs to transfer data independently without the assistance of a base station (gNB), which enables V2X communications, including platooning, autonomous driving, sensor extension, industrial IoT, public safety communication and much more. Our design leverages the open-source OpenAirInterface5G (OAI) software, which operates on software-defined radios (SDRs) and can be easily extended for mesh networking. The software includes all signal processing components specified by the 3GPP 5G sidelink standards, including Low-Density Parity Check (LDPC) encoding/decoding, polar encoding/decoding, data and control multiplexing, modulation/demodulation, and orthogonal frequency-division multiplexing (OFDM) modulation/demodulation. It can be configured to operate with different bands, bandwidths, and antenna settings. The first milestone in this work was to demonstrate the completed Physical Sidelink Broadcast Channel (PSBCH) development, which conducts synchronization between a Synchronization Reference (SyncRef) UE and a nearby UE. The SyncRef UE broadcasts a sidelink synchronization signal block (S-SSB) periodically, which the nearby UE detects and uses to synchronize its timing and frequency components with the SyncRef UE. Once a connection is established, the next developmental milestone is to transmit real data (text messages) via the Physical Sidelink Shared Channel (PSSCH). Our PHY sidelink framework is tested using both an RF simulator and an OTA testbed with multiple nearby UEs. Beyond the development of synchronization and data transmission/reception in 5G sidelink, we conclude with various performance tests and validation experiments. The results of these metrics show that our simulator is comparable to the OTA testbed.

cs.NI↗

arXiv2023

Response to "On the giant deformation and ferroelectricity of guanidinium nitrate" by Marek Szafrański and Andrzej Katrusiak

Following a well-established practice of publishing commentaries to articles of other authors who work on materials that were earlier studied by them (n.b. six published comments[1-6]), Marek Szafrański(MS) and Andrzej Katrusiak (AK) have filed on the preprint server arXiv a manuscript entitled "On the giant deformation and ferroelectricity of guanidinium nitrate"[7] with comments on our article "Exceptionally high work density of a ferroelectric dynamic organic crystal around room temperature" published in Nature Communications (2022, 13, 2823).[8] Both in the submitted comment as well as in the required (by the journal) direct communication with us preceding its posting, MS and AK have expressed dissatisfaction with the choice of literature references in our article, for which they felt that their previous work on this material has not been cited to a sufficient extent. In their comment, they summarize their other remarks on our article as "the structural determinations of GN [guanidinium nitrate] crystals, their phase transitions and associated giant deformation, as well as its detailed structural mechanism, the molecular dynamics and dielectric properties were reported before, while the semiconductivity, ferroelectricity, and fatigue resistance of the GN [guanidinium nitrate] crystals cannot be confirmed."[7] Apart from the sentiments of MS and AK on our choice of cited literature, we find their comments on the scientific content of our article to be strongly biased towards their own results and unfounded. Below, we provide a detailed response to their comments.

cond-mat.mtrl-sci↗

arXiv2023

DeepMPR: Enhancing Opportunistic Routing in Wireless Networks through Multi-Agent Deep Reinforcement Learning

Opportunistic routing relies on the broadcast capability of wireless networks. It brings higher reliability and robustness in highly dynamic and/or severe environments such as mobile or vehicular ad-hoc networks (MANETs/VANETs). To reduce the cost of broadcast, multicast routing schemes use the connected dominating set (CDS) or multi-point relaying (MPR) set to decrease the network overhead and hence, their selection algorithms are critical. Common MPR selection algorithms are heuristic, rely on coordination between nodes, need high computational power for large networks, and are difficult to tune for network uncertainties. In this paper, we use multi-agent deep reinforcement learning to design a novel MPR multicast routing technique, DeepMPR, which is outperforming the OLSR MPR selection algorithm while it does not require MPR announcement messages from the neighbors. Our evaluation results demonstrate the performance gains of our trained DeepMPR multicast forwarding policy compared to other popular techniques.

cs.NI↗

arXiv2022

Unraveling Threat Intelligence Through the Lens of Malicious URL Campaigns

The daily deluge of alerts is a sombre reality for Security Operations Centre (SOC) personnel worldwide. They are at the forefront of an organisation's cybersecurity infrastructure, and face the unenviable task of prioritising threats amongst a flood of abstruse alerts triggered by their Security Information and Event Management (SIEM) systems. URLs found within malicious communications form the bulk of such alerts, and pinpointing pertinent patterns within them allows teams to rapidly deescalate potential or extant threats. This need for vigilance has been traditionally filled with machine-learning based log analysis tools and anomaly detection concepts. To sidestep machine learning approaches, we instead propose to analyse suspicious URLs from SIEM alerts via the perspective of malicious URL campaigns. By first grouping URLs within 311M records gathered from VirusTotal into 2.6M suspicious clusters, we thereafter discovered 77.8K malicious campaigns. Corroborating our suspicions, we found 9.9M unique URLs attributable to 18.3K multi-URL campaigns, and that worryingly, only 2.97% of campaigns were found by security vendors. We also confer insights on evasive tactics such as ever lengthier URLs and more diverse domain names, with selected case studies exposing other adversarial techniques. By characterising the concerted campaigns driving these URL alerts, we hope to inform SOC teams of current threat trends, and thus arm them with better threat intelligence.

cs.CR↗

arXiv2021

DeepCQ+: Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for Highly Dynamic Networks

Highly dynamic mobile ad-hoc networks (MANETs) remain as one of the most challenging environments to develop and deploy robust, efficient, and scalable routing protocols. In this paper, we present DeepCQ+ routing protocol which, in a novel manner integrates emerging multi-agent deep reinforcement learning (MADRL) techniques into existing Q-learning-based routing protocols and their variants and achieves persistently higher performance across a wide range of topology and mobility configurations. While keeping the overall protocol structure of the Q-learning-based routing protocols, DeepCQ+ replaces statically configured parameterized thresholds and hand-written rules with carefully designed MADRL agents such that no configuration of such parameters is required a priori. Extensive simulation shows that DeepCQ+ yields significantly increased end-to-end throughput with lower overhead and no apparent degradation of end-to-end delays (hop counts) compared to its Q-learning based counterparts. Qualitatively, and perhaps more significantly, DeepCQ+ maintains remarkably similar performance gains under many scenarios that it was not trained for in terms of network sizes, mobility conditions, and traffic dynamics. To the best of our knowledge, this is the first successful application of the MADRL framework for the MANET routing problem that demonstrates a high degree of scalability and robustness even under environments that are outside the trained range of scenarios. This implies that our MARL-based DeepCQ+ design solution significantly improves the performance of Q-learning based CQ+ baseline approach for comparison and increases its practicality and explainability because the real-world MANET environment will likely vary outside the trained range of MANET scenarios. Additional techniques to further increase the gains in performance and scalability are discussed.

cs.NI↗

arXiv2021

Ridge-Type Shrinkage Estimators in Low and High Dimensional Beta Regression Model with Application in Econometrics and Medicine

Beta regression model is useful in the analysis of bounded continuous outcomes such as proportions. It is well known that for any regression model, the presence of multicollinearity leads to poor performance of the maximum likelihood estimators. The ridge type estimators have been proposed to alleviate the adverse effects of the multicollinearity. Furthermore, when some of the predictors have insignificant or weak effects on the outcomes, it is desired to recover as much information as possible from these predictors instead of discarding them all together. In this paper we proposed ridge type shrinkage estimators for the low and high dimensional beta regression model, which address the above two issues simultaneously. We compute the biases and variances of the proposed estimators in closed forms and use Monte Carlo simulations to evaluate their performances. The results show that, both in low and high dimensional data, the performance of the proposed estimators are superior to ridge estimators that discard weak or insignificant predictors. We conclude this paper by applying the proposed methods for two real data from econometric and medicine.

stat.ME↗

arXiv2021

Characterizing Malicious URL Campaigns

URLs are central to a myriad of cyber-security threats, from phishing to the distribution of malware. Their inherent ease of use and familiarity is continuously abused by attackers to evade defences and deceive end-users. Seemingly dissimilar URLs are being used in an organized way to perform phishing attacks and distribute malware. We refer to such behaviours as campaigns, with the hypothesis being that attacks are often coordinated to maximize success rates and develop evasion tactics. The aim is to gain better insights into campaigns, bolster our grasp of their characteristics, and thus aid the community devise more robust solutions. To this end, we performed extensive research and analysis into 311M records containing 77M unique real-world URLs that were submitted to VirusTotal from Dec 2019 to Jan 2020. From this dataset, 2.6M suspicious campaigns were identified based on their attached metadata, of which 77,810 were doubly verified as malicious. Using the 38.1M records and 9.9M URLs within these malicious campaigns, we provide varied insights such as their targeted victim brands as well as URL sizes and heterogeneity. Some surprising findings were observed, such as detection rates falling to just 13.27% for campaigns that employ more than 100 unique URLs. The paper concludes with several case-studies that illustrate the common malicious techniques employed by attackers to imperil users and circumvent defences.

cs.CR↗

arXiv2021

Joint Linear Trend Recovery Using L1 Regularization

This paper studies the recovery of a joint piece-wise linear trend from a time series using L1 regularization approach, called L1 trend filtering (Kim, Koh and Boyd, 2009). We provide some sufficient conditions under which a L1 trend filter can be well-behaved in terms of mean estimation and change point detection. The result is two-fold: for the mean estimation, an almost optimal consistent rate is obtained; for the change point detection, the slope change in direction can be recovered in a high probability. In addition, we show that the weak irrepresentable condition, a necessary condition for LASSO model to be sign consistent (Zhao and Yu, 2006), is not necessary for the consistent change point detection. The performance of the L1 trend filter is evaluated by some finite sample simulations studies.

stat.ME↗

arXiv2021

Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for MANETs

Highly dynamic mobile ad-hoc networks (MANETs) are continuing to serve as one of the most challenging environments to develop and deploy robust, efficient, and scalable routing protocols. In this paper, we present DeepCQ+ routing which, in a novel manner, integrates emerging multi-agent deep reinforcement learning (MADRL) techniques into existing Q-learning-based routing protocols and their variants, and achieves persistently higher performance across a wide range of MANET configurations while training only on a limited range of network parameters and conditions. Quantitatively, DeepCQ+ shows consistently higher end-to-end throughput with lower overhead compared to its Q-learning-based counterparts with the overall gain of 10-15% in its efficiency. Qualitatively and more significantly, DeepCQ+ maintains remarkably similar performance gains under many scenarios that it was not trained for in terms of network sizes, mobility conditions, and traffic dynamics. To the best of our knowledge, this is the first successful demonstration of MADRL for the MANET routing problem that achieves and maintains a high degree of scalability and robustness even in the environments that are outside the trained range of scenarios. This implies that the proposed hybrid design approach of DeepCQ+ that combines MADRL and Q-learning significantly increases its practicality and explainability because the real-world MANET environment will likely vary outside the trained range of MANET scenarios.

cs.NI↗

arXiv2019

Energy Harvesting in 5G Networks: Taxonomy, Requirements, Challenges, and Future Directions

Consciousness of energy saving is increasing in fifth-generation (5G) wireless networks due to the high energy consumption issue. Energy harvesting technology is a possible appealing solution for ultimately prolonging the lifetime of devices and networks. Although considerable research efforts have been conducted in the context of using energy harvesting technology in 5G wireless networks, these efforts are in their infancy, and a tutorial on this topic is still lacking. This study aims to discuss the beneficial role of energy harvesting technology in 5G networks. We categorize and classify the literature available on energy harvesting in 5G networks by devising a taxonomy based on energy sources; energy harvesting devices, phases, and models; energy conversion methods, and energy propagation medium. The key requirements for enabling energy harvesting in 5G networks are also outlined. Several core research challenges that remain to be addressed are discussed. Furthermore, future research directions are provided.

cs.NI↗

arXiv2016

Adaptive Beaconing Approaches for Vehicular ad hoc Networks: A Survey

Vehicular communication requires vehicles to self-organize through the exchange of periodic beacons. Recent analysis on beaconing indicates that the standards for beaconing restrict the desired performance of vehicular applications. This situation can be attributed to the quality of the available transmission medium, persistent change in the traffic situation and the inability of standards to cope with application requirements. To this end, this paper is motivated by the classifications and capability evaluations of existing adaptive beaconing approaches. To begin with, we explore the anatomy and the performance requirements of beaconing. Then, the beaconing design is analyzed to introduce a design-based beaconing taxonomy. A survey of the state-of-the-art is conducted with an emphasis on the salient features of the beaconing approaches. We also evaluate the capabilities of beaconing approaches using several key parameters. A comparison among beaconing approaches is presented, which is based on the architectural and implementation characteristics. The paper concludes by discussing open challenges in the field.

cs.NI↗