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Ijaz Ahmad

Publications and source records attributed to Ijaz Ahmad.

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Autonomy in Check: Governor-Mediated Adaptive Security at the Edge

Adaptive security at the network edge increasingly relies on automated planners, including rule-based controllers, learned policies, and LLM-assisted agents, that translate observations into enforcement actions. Once such a planner can influence live policy state, syntactic validity is not enough. A semantically wrong action, produced from incomplete or manipulated observations, can be faithfully executed by an enforcement substrate that cannot judge mission context. We address this problem by treating the boundary between planner output and kernel enforcement input as the primary security object. We propose a split-control architecture in which an untrusted planner emits typed security intents, a deterministic governor checks each intent against safety, resource, temporal-stability, and proportionality invariants, and only admitted actions are bound to signed receipts and compiled into pre-installed eBPF map updates. The paper formalizes this trust-boundary problem, defines three threat classes, develops the governor admission predicate, and reports an end-to-end prototype. Across rule-based and LLM-assisted planners on a Raspberry Pi 5 testbed connected to the university 5G Test Network, the governor admits, rejects, and bounds intents at microsecond cost without disrupting protected-flow regularity. The contribution is conceptual as much as empirical: adaptive security does not need to trust the author of an action. It needs a mediation boundary that decides whether the action is admissible.

cs.CR

Adaptive Security at the Edge for 6G-Enabled Healthcare IoT

Healthcare IoT services increasingly rely on edge gateways to relay routine telemetry and deliver rare but timecritical alarms. Even short traffic bursts can inflate worstcase delay and interfere with urgent messages. We present NANOEDGEGUARD, a kernel-plane closed-loop controller that observes per-source traffic intensity at the edge and enforces an auditable, multi-tier rate policy using in-kernel traffic-control hooks. Unlike static firewall rules or user-space control loops, our design prioritizes fast actuation and explicit recovery through hysteresis, and it records policy transitions for auditability. Using a Raspberry Pi gateway hosting an MQTT broker and two ESP32 endpoints generating vitals, alarms, and a timed burst, we show that adaptive kernel-plane rate control reduces the 99th-percentile alarm RTT by 13.3% compared to a user-space firewall baseline while maintaining no-enforcement-level RTT, and it reduces excess admitted burst traffic by 46% compared to no enforcement. These early results indicate that adaptive, auditable enforcement at the gateway can improve resilience for healthcare IoT, and it can be extended toward on-demand policy deployment in future edge intelligence.

cs.CR

Advanced Multimodal Learning for Seizure Detection and Prediction: Concept, Challenges, and Future Directions

Epilepsy is a chronic neurological disorder characterized by recurrent unprovoked seizures, affects over 50 million people worldwide, and poses significant risks, including sudden unexpected death in epilepsy (SUDEP). Conventional unimodal approaches, primarily reliant on electroencephalography (EEG), face several key challenges, including low SNR, nonstationarity, inter- and intrapatient heterogeneity, portability, and real-time applicability in clinical settings. To address these issues, a comprehensive survey highlights the concept of advanced multimodal learning for epileptic seizure detection and prediction (AMLSDP). The survey presents the evolution of epileptic seizure detection (ESD) and prediction (ESP) technologies across different eras. The survey also explores the core challenges of multimodal and non-EEG-based ESD and ESP. To overcome the key challenges of the multimodal system, the survey introduces the advanced processing strategies for efficient AMLSDP. Furthermore, this survey highlights future directions for researchers and practitioners. We believe this work will advance neurotechnology toward wearable and imaging-based solutions for epilepsy monitoring, serving as a valuable resource for future innovations in this domain.

cs.NE

Adaptive Lightweight Security for Performance Efficiency in Critical Healthcare Monitoring

The healthcare infrastructure requires robust security procedures, technologies, and policies due to its critical nature. Since the Internet of Things (IoT) with its diverse technologies has become an integral component of future healthcare systems, its security requires a thorough analysis due to its inherent security limitations that arise from resource constraints. Existing communication technologies used for IoT connectivity, such as 5G, provide communications security with the underlying communication infrastructure to a certain level. However, the evolving healthcare paradigm requires adaptive security procedures and technologies that can adapt to the varying resource constraints of IoT devices. This need for adaptive security is particularly pronounced when considering components outside the security sandbox of 5G, such as IoT nodes and M2M connections, which introduce additional security challenges. This article brings forth the unique healthcare monitoring requirements and studies the existing encryption-based security approaches to provide the necessary security. Furthermore, this research introduces a novel approach to optimizing security and performance in IoT in healthcare, particularly in critical use cases such as remote patient monitoring. Finally, the results from the practical implementation demonstrate a marked improvement in the system performance.

cs.CR

Energy Profiling and Analysis of 5G Private Networks: Evaluating Energy Consumption Patterns

Private 5G networks provide enhanced security, a wide range of optimized services through network slicing, reduced latency, and support for many IoT devices in a specific area, all under the owner's full control. Higher security and privacy to protect sensitive data is the most significant advantage of private networks, in e.g., smart hospitals. For long-term sustainability and cost-effectiveness of private 5G networks, analyzing and understanding the energy consumption variation holds a greater significance in reaching toward green private network architecture for 6G. This paper addresses this research gap by providing energy profiling of network components using an experimental laboratory setup that mimics real private 5G networks under various network conditions, which is a missing aspect in the existing literature.

eess.SY

Deep Learning Frameworks for Cognitive Radio Networks: Review and Open Research Challenges

Deep learning has been proven to be a powerful tool for addressing the most significant issues in cognitive radio networks, such as spectrum sensing, spectrum sharing, resource allocation, and security attacks. The utilization of deep learning techniques in cognitive radio networks can significantly enhance the network's capability to adapt to changing environments and improve the overall system's efficiency and reliability. As the demand for higher data rates and connectivity increases, B5G/6G wireless networks are expected to enable new services and applications significantly. Therefore, the significance of deep learning in addressing cognitive radio network challenges cannot be overstated. This review article provides valuable insights into potential solutions that can serve as a foundation for the development of future B5G/6G services. By leveraging the power of deep learning, cognitive radio networks can pave the way for the next generation of wireless networks capable of meeting the ever-increasing demands for higher data rates, improved reliability, and security.

cs.NI

Adaptive Security in 6G for Sustainable Healthcare

6G will fulfill the requirements of future digital healthcare systems through emerging decentralized computing and secure communications technologies. Digital healthcare solutions employ numerous low-power and resource-constrained connected things, such as the Internet of Medical Things (IoMT). However, the current digital healthcare solutions will face two major challenges. First, the proposed solutions are based on the traditional IoT-Cloud model that will experience latency and reliability challenges to meet the expectations and requirements of digital healthcare, while potentially inflicting heavy network load. Second, the existing digital healthcare solutions will face security challenges due to the inherent limitations of IoMT caused by the lack of resources for proper security in those devices. Therefore, in this research, we present a decentralized adaptive security architecture for the successful deployment of digital healthcare. The proposed architecture leverages the edge-cloud continuum to meet the performance, efficiency, and reliability requirements. It can adapt the security solution at run-time to meet the limited capacity of IoMT devices without compromising the security of critical data. Finally, the research outlines comprehensive methodologies for validating the proposed security architecture.

cs.CR

Channel model for end-to-end learning of communications systems: A survey

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.

cs.LG

Just-Noticeable-Difference Based Edge Map Quality Measure

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.

cs.CV

A Pixel-based Encryption Method for Privacy-Preserving Deep Learning Models

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.

cs.CR

Overview of Security of Virtual Mobile Networks

5G is enabling different services over the same physical infrastructure through the concepts and technologies of virtualization, softwarization, slicing and cloud computing. Virtual Mobile Networks (VMNs), using these concepts, provide an opportunity to share the same physical infrastructure among multiple operators. Each VMN Operator (VMNO) can have own distinct operating and support systems. However, the technologies used to enable VMNs have their own explicit security challenges and solutions. The integrated environment built upon virtualization, softwarization, and cloudification, thus, will have complex security requirements and implications. In this vain, this article provides an overview of the security challenges and potential solutions for VMNs.

cs.NI

Improving Software Defined Cognitive and Secure Networking

Traditional communication networks consist of large sets of vendor-specific manually configurable devices which are hardwired with specific control logic or algorithms. The resulting networks comprise distributed control plane architectures that are complex in nature, difficult to integrate and operate, and are least efficient in terms of resource usage. However, the rapid increase in data traffic requires an integrated use of diverse access technologies and autonomic network operations with increased efficiency. Therefore, the concepts of Software Defined Networking (SDN) are proposed that decouple the network control plane from the data-forwarding plane. The SDN control plane can integrate a diverse set of devices, and tune them at run-time through vendor-agnostic programmable Application Programming Interfaces (APIs). This thesis proposes software defined cognitive networking to enable intelligent use of network resources. Different radio access technologies, including cognitive radios, are integrated through a common control platform to increase the overall network performance. The architectural framework of software defined cognitive networking is presented alongside the experimental performance evaluation. Since SDN enables applications to change the network behavior and centralizes the network control plane to oversee the whole network, it is highly important to investigate security of SDNs. Therefore, this thesis finds potential security vulnerabilities in SDN, studies proposed security platforms and architectures for those vulnerabilities, and presents future directions for unresolved security vulnerabilities. Furthermore, this thesis also investigates the potential security challenges and their solutions for the enabling technologies of 5G, such as SDN, cloud technologies, and virtual network functions, and provides key insights into increasing the security of 5G networks.

cs.NI

Challenges of AI in Wireless Networks for IoT

The Internet of Things (IoT), hailed as the enabler of the next industrial revolution, will require ubiquitous connectivity, context-aware and dynamic service mobility, and extreme security through the wireless network infrastructure. Artificial Intelligence (AI), thus, will play a major role in the underlying network infrastructure. However, a number of challenges will surface while using the concepts, tools and algorithms of AI in wireless networks used by IoT. In this article, the main challenges in using AI in the wireless network infrastructure that facilitate end-to-end IoT communication are highlighted with potential generalized solution and future research directions.

cs.NI

6G White Paper on Machine Learning in Wireless Communication Networks

The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research has led enable a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is possible as a result of the availability of advanced ML models, large datasets, and high computational power. On the other hand, the ever-increasing demand for connectivity will require a lot of innovation in 6G wireless networks, and ML tools will play a major role in solving problems in the wireless domain. In this paper, we provide an overview of the vision of how ML will impact the wireless communication systems. We first give an overview of the ML methods that have the highest potential to be used in wireless networks. Then, we discuss the problems that can be solved by using ML in various layers of the network such as the physical layer, medium access layer, and application layer. Zero-touch optimization of wireless networks using ML is another interesting aspect that is discussed in this paper. Finally, at the end of each section, important research questions that the section aims to answer are presented.

cs.IT

Orchestrating Service Migration for Low Power MEC-Enabled IoT Devices

Multi-Access Edge Computing (MEC) is a key enabling technology for Fifth Generation (5G) mobile networks. MEC facilitates distributed cloud computing capabilities and information technology service environment for applications and services at the edges of mobile networks. This architectural modification serves to reduce congestion, latency, and improve the performance of such edge colocated applications and devices. In this paper, we demonstrate how reactive service migration can be orchestrated for low-power MEC-enabled Internet of Things (IoT) devices. Here, we use open-source Kubernetes as container orchestration system. Our demo is based on traditional client-server system from user equipment (UE) over Long Term Evolution (LTE) to the MEC server. As the use case scenario, we post-process live video received over web real-time communication (WebRTC). Next, we integrate orchestration by Kubernetes with S1 handovers, demonstrating MEC-based software defined network (SDN). Now, edge applications may reactively follow the UE within the radio access network (RAN), expediting low-latency. The collected data is used to analyze the benefits of the low-power MEC-enabled IoT device scheme, in which end-to-end (E2E) latency and power requirements of the UE are improved. We further discuss the challenges of implementing such schemes and future research directions therein.

cs.NI