Shagufta Henna

Scopus Publications

Understanding and classification of innate immune response through weighted edge representation learning with dual hypergraph transformation
Mallikharjuna Rao Sakhamuri, Shagufta Henna, Leo Creedon, Kevin Meehan, and Marion McAfee
Elsevier BV

Graph representation federated learning for malware detection in Internet of health things
Mohamed Amjath, Shagufta Henna, and Upaka Rathnayake
Elsevier BV

EdgeSecureDP: Strengthening IoHTs Differential Privacy through Graphvariate Skellam
Mohamed Amjath and Shagufta Henna
Institute of Electrical and Electronics Engineers (IEEE)
The Internet of Health Things (IoHTs) has transformed healthcare systems, facilitating remote patient monitoring and personalized treatment. Federated Learning (FL) has emerged as a promising solution, enabling decentralized devices to collaboratively train machine learning models while ensuring privacy and security in healthcare applications. Differential Privacy (DP) is used to enhance privacy in FL frameworks by injecting controlled noise into data or model updates, preventing attackers from extracting specific information. However, existing DP mechanisms, such as the Gaussian mechanism and Univariate Skellam struggle to balance privacy-utility trade-off for graph-based data like drug-drug interactions. These mechanisms treat data points independently, failing to account for the complex interconnections between nodes (drugs) and edges (interactions), leaves the network vulnerable to structural attacks that can reverse-engineer relationships, thus limiting the security of collaborative drug discovery. To address these limitations, this work proposes Graphvariate Skellam, a novel DP approach that leverages graph structure information in FL settings, referred to as EdgeSecureDP. By exploiting the structural information encoded in graph edges, this method offers enhanced privacy protection. Experimental results and theoretical analysis demonstrate that Graphvariate Skellam effectively preserves privacy ( $15 \\lt \\epsilon \\leq 20$ ) while achieving 78% accuracy in IoHT environments, making it a robust solution for privacy-preserving healthcare applications.

Advancing water quality assessment and prediction using machine learning models, coupled with explainable artificial intelligence (XAI) techniques like shapley additive explanations (SHAP) for interpreting the black-box nature
Randika K. Makumbura, Lakindu Mampitiya, Namal Rathnayake, D.P.P. Meddage, Shagufta Henna, Tuan Linh Dang, Yukinobu Hoshino, and Upaka Rathnayake
Elsevier BV

A data-augmented approach to transfer learning for Covid-19 detection
Shagufta Henna, Stephen Azeez, Muhammad Bilal, and Aparna Reiji
AIP Publishing

Human activity recognition using ensemble machine learning classifiers
Shagufta Henna, David Aboga, Muhammad Bilal, and Stephen Azeez
AIP Publishing

Front-running Attack Detection in Blockchain using Conditional Packing Generative AI
Shagufta Henna and Mohamed Amjath
IEEE
Detecting front-running attacks in Ethereum blockchain transactions is crucial for maintaining security and integrity within decentralized ecosystems. However, existing models struggle to accurately model the complex distributions inherent in tabular data, particularly in the presence of class imbalance and mode collapse. This paper leverages the potentials of Conditional Tabular Generative Adversarial Networks and PacGAN, called a Conditional Packing GAN (cPacGAN), to address these challenges. cPacGAN effectively generates synthetic data that closely mimics the distribution of real transactions, thereby augmenting the dataset and improving the performance of front-running attack detection. PacGAN mitigates mode collapse by incorporating packed samples in the discriminator, improving the diversity of generated samples and improving the stability of the training process. Through experimental evaluations of a real-world Ethereum transactions dataset, cPacGAN demonstrates improved performance across all selected machine learning classifiers, particularly augmenting the effectiveness of Tabular Neural Networks (TabNet).

Rényi Differential Privacy Analysis of Skellam under Federated Learning in Internet of Health Things
Mohamed Amjath and Shagufta Henna
IEEE
Preserving privacy is a critical challenge when applying federated learning (FL) to sensitive healthcare data in the Internet of Health Things (IoHT) applications. Despite employing a range of differential privacy (DP) analysis methods to assess privacy in FL, achieving an optimal balance between privacy and utility continues to be challenging. An incorrect balance in the privacy-utility tradeoff can significantly compromise patient privacy and affect the reputation of healthcare organizations. To address this issue, this research investigates a robust privacy-preserving framework utilising the Skellam mechanism and Renyi Differential Privacy (RDP) technique. By utilizing the closed-under summation property of the Skellam mechanism, we address the challenges in aggregating noisy updates in FL. Further, through a rigorous RDP analysis, we demonstrate that selecting an appropriate RDP order $a$ and privacy budget ∊ enables a balanced tradeoff within the FL setup. Finally, we validate our approach through the development of a novel algorithm, DP-DotGAT. The algorithm takes function call graphs (FCGs) obtained from healthcare applications as input. Additionally, it effectively protects against membership inference attacks while maintaining an accuracy range of 70% to 73%. Our results indicate that this approach greatly improves the privacy of health care data and can lead to wider adoption of secure and privacy-preserving machine learning models in healthcare sectors.

Accelerating Deep Learning for Self-Calibration in Large-Scale Uncontrolled Wireless Sensor Networks for Environmental Monitoring
Asif Yar, Shagufta Henna, Marion McAfee, and Salem S. Gharbia
IEEE
Self-calibration poses one of the primary challenges in deploying wireless sensor networks (WSNs), particularly in uncontrolled environments. While existing deep learning methods have demonstrated their effectiveness in centralized scenarios, their efficiency diminishes when applied in distributed and uncontrolled environments. To tackle this issue, this work evaluates the application of extreme learning machines (ELM) and graph deep learning techniques for self-calibration. The experimental findings reveal that the graph learning approach outperforms state-of-the-art deep learning methods, including LSTM, MLP, and CNN, in terms of accuracy. Additionally, ELM showcases its effectiveness as a suitable machine learning approach under resource-constrained scenarios, with accelerated learning.

Molecular Adversarial Generative Graph Network Model for Large-scale Molecular Networks
Mallikharjuna Rao Sakhamuri, Shagufta Henna, Leo Creedon, and Kevin Meehan
IEEE
An understanding of the human innate immune response is essential to accelerate the progress and clinical trials of drugs and antibiotics. This requires efficient modelling of the molecular network. A lack of diverse and vast molecular data has been a major hurdle in molecular modelling research. Existing synthetic graph models such as variational auto encoders, Restricted Boltzmann Machines and auto regressive models are computationally expensive and experience inefficiencies due to their sequential architectural design. To address these issues this research paper introduces MAGNet - a Molecular Adversarial Graph Network based graph generative AI model to generate synthetic graph structures. MAGNet model architecture consists of two neural networks, a generator and discriminator which are involved in a minmax game to improve each other's performance. The model is trained on the graphs generated from unlabelled SMILES strings. The MAGNet architecture enabled the model to converge in fewer epochs and the generator was able to produce an accurate synthetic graph which is indistinguishable by the discriminator. The experimental results establish that the Graph Adversarial Network model is able to converge the discriminator loss within the first 15 epochs of the training.

Representation Learning for Spatial Reuse in IEEE 802.11ax-Compliance Edge Intelligence
Stephen Azeez and Shagufta Henna
Springer Nature Singapore

Ensemble consensus representation deep reinforcement learning for hybrid FSO/RF communication systems
Shagufta Henna, Abid Ali Minhas, Muhammad Saeed Khan, and Muhammad Shahid Iqbal
Elsevier BV

Rényi Differential Privacy Analysis of Graph-Based Federated Learning Under Internet of Health Things
Mohamed Amjath and Shagufta Henna
IEEE
The rise of the Internet of Health Things (IoHTs) has resulted in a significant increase in collaborative initiatives among healthcare organizations employing federated learning (FL). Even though FL trains models locally to protect privacy, exchanging model parameters still creates privacy risks, especially when working with non-Euclidean data like graphs. To address this issue, differential privacy (DP) is widely used, however, choosing appropriate privacy parameters remains difficult. Therefore, this research employs Rényi Differential Privacy (RDP) analysis, which extends the capabilities of traditional DP by providing more flexibility in the selection of privacy parameters. To measure, this research first models the malware dataset as a function call graph (FCG). Subsequently, the DP-SGD-enabled DotGAT model is utilized to classify both malware and benign applications, ensuring the preservation of privacy while maintaining model utility. Finally, We empirically demonstrate that selecting Rényi divergence (a) values between 2 and 2.5 optimises the balance between privacy and utility in graph-based models within the FL setup, improving healthcare collaboration privacy.

Global-Local Influence Maximization Subgraph Sampling-Based Graph Representation Learning for Innate Immune Response Classification
Mallikharjuna Rao Sakhamuri, Shagufta Henna, Leo Creedon, and Kevin Meehan
IEEE
An understanding of the human innate immune re-sponse has the potential to accelerate the development and clinical trials of drugs and antibiotics. This includes an understanding of T-cell responses, peptides, and the intricate interactions with Human Leukocyte Antigens (HLA). Graph based models capture the structural aspects of these interactions efficiently and graph based neural networks are the best choice of tools to analyse these datasets. However, the polymorphic nature of peptides, coupled with various influencing factors, results in the representation of HLA-peptide interactions as large complex graphs. Traditional graph-based neural networks often face challenges in processing large graphs and suffer due to their high learning and training time with reduced generalization capabilities. To address these challenges, this paper proposes an influence maximization sub-graph sampling classification approach to retain the structural information of large complex molecular graphs. This is achieved using global-local influence maximization (GLIM) that combines Page Rank with Eigenvector centrality. This unique combination enables local substructure connectivity, considering edge weights, which are critical in molecular contexts. The graph neural network (GNN) classification model achieved 0.82% accuracy on the main graphs and 0.83% accuracy on the subgraphs. More notably, the model consumed approximately 81% less memory and was about 72% faster per epoch for the subgraphs compared to the main graphs. The experimental results demonstrate the effectiveness of the subgraphs as they minimize memory resource utilization and processing time, making them a practical and efficient choice for HLA-peptide immunogenetic behavior analysis.

Extreme Learning Machines for Calibration and Prediction in Wireless Sensor Networks: Advancing Environmental Monitoring Efficiency
Asif Yar, Shagufta Henna, Marion McAfee, and Salem S. Gharbia
IEEE
Calibrating wireless sensor network deployments, especially in uncontrolled environments, poses a significant challenge. Existing deep learning approaches for calibration perform well when resource requirements are not constrained However, conventional deep learning models are not well suited for resource constraint environments due to their computational complexity and requirement of resources. To tackle this issue, this paper introduces an extreme learning machine (ELM)-based calibration solution. ELM leverages a single-layer neural network with random weight initialization, enabling faster training and inference. Experimental results demonstrate that ELM results in accelerated learning while maintaining competitive accuracy compared to deep learning approaches like neural networks (NNs).

A Graph Neural Network-based Security Posture-aware Cloud Service Provider Selection for Multi-cloud
D.S. Wijenayake, Shagufta Henna, and William Farrelly
IEEE
When bidirectional, data-intensive, scientific applications move through multiple cloud environments, the posture requirements change during transit. Security is crucial in this scenario as the applications and as well as infrastructure providers demand it. Previous studies were based on sub-optimal heuristics, and machine learning with no consideration for the bidirectional nature of the data workflows. This limitation is addressed in this work by adopting the graph structure and predicting cloud providers based on the specifications. GraphSAGE performed well with an AUC of 0.94 while having a trade-off between performance and cost. The research outcomes highlight the applicability of graph-based solutions to security-aware cloud service provider selection for bidirectional multi-cloud data workflows.

Air Pollution Monitoring Using Online Recurrent Extreme Learning Machine
Asif Yar, Shagufta Henna, Marion McAfee, and Salem S. Gharbia
IEEE
Air pollution, particularly high concentrations of Ozone (O3), poses a serious threat to human health and the environment. While deep learning algorithms have proven effective in air quality forecasting, current offline models struggle to capture the dynamic, time-evolving patterns generated by continuous air pollution monitoring data. Further, the time-consuming training process and computational demands hinder the practicality of these models. This paper presents a lightweight incremental learning model tailored for O3 forecasting. To evaluate its effectiveness, real data is employed and performance is evaluated using forecasting metrics and computational time. The results reveal that the incremental learning model surpasses the state-of-the-art model widely used in O3 and time series forecasting, demonstrating both superior accuracy and computational efficiency.

Deep Reinforcement Learning for Advanced Persistent Threat Detection in Wireless Networks
Kazeem Saheed and Shagufta Henna
IEEE
Recent cyberattacks have shifted their focus from causing financial loss or service disruption to covertly exfil-trating confidential data. Advanced Persistent Threats (APTs) pose a significant challenge due to their dynamic and sophis-ticated attack mechanisms. Unlike other cyberattacks, APTs are coordinated and targeted, executed by high-profile hackers who exploit identified vulnerabilities and deliver novel malware through phishing attacks to infiltrate networks. Traditional deep learning approaches for APT detection are static and lack adaptability, making them unsuitable for handling the dynamic and evolving attack scenarios commonly found in uncertain network traffic flows, such as multi-stage APT attacks. To address these challenges, this study proposes a Deep Reinforcement Learning approach for APT detection, referred to as APT-DRL. This approach dynamically learns from interactions with the environment, continuously adapting to emerging attack patterns. Performance evaluations demonstrate that APT-DRL effectively learns from dynamic network interactions, enabling it to formulate new policies for APT detection. Consequently, APT-DRL learns faster and achieves better accuracy compared to Feed Forward Neural Network (FNN) models, which lack the adaptability and learning capabilities of the proposed APT-DRL approach.

Wireless Sensor Networks Calibration using Attention-based Gated Recurrent Units for Air Pollution Monitoring
Shagufta Henna, Asif Yar, Kazeem Saheed, and Paulson Grigarichan
IEEE
Calibration in wireless sensor networks (WSNs) poses a significant challenge, particularly in uncontrolled environmental deployments for environmental monitoring, such as air pollution. Traditional calibration methods rely on centralized reference stations, which are costly to maintain, offer limited coverage, and calculate measurements as averages. However, with the rise of the Internet of Things (IoT), sensors present a cost-effective alternative for calibration compared to fixed reference stations. Nevertheless, in uncontrolled environments, sensors require self-recalibration to ensure accurate measurements for the reliable operation of WSNs without human intervention. Existing calibration approaches, such as LSTM, are computationally expensive, have higher memory requirements, and exhibit training instability, making them unsuitable for resource-constrained WSNs. This paper proposes a self-calibration approach for WSNs using the Gated Recurrent Unit (GRU) coupled with the attention mechanism (Attention-GRU). The Attention-GRU selectively focuses on relevant features while capturing long-term dependencies, akin to Recurrent Neural Networks (RNNs), thereby mitigating overfitting. Experimental results demonstrate that the Attention-GRU model outperforms other models with an R-squared value of 0.97 and accelerated learning. These accurate sensor recalibration predictions promote sustainability by supporting IoT-enabled air pollution monitoring efforts.

Heterogeneous Graph Transformer for Advanced Persistent Threat Classification in Wireless Networks
Kazeem Saheed and Shagufta Henna
IEEE
Advanced Persistent Threats (APTs) have significantly impacted organizations over an extended period with their coordinated and sophisticated cyberattacks. Unlike signature-based tools such as antivirus and firewalls that can detect and block other types of malware, APTs exploit zero-day vulnerabilities to generate new variants of undetectable malware. Additionally, APT adversaries engage in complex relationships and interactions within network entities, necessitating the learning of interactions in network traffic flows, such as hosts, users, or IP addresses, for effective detection. However, traditional deep neural networks often fail to capture the inherent graph structure and overlook crucial contextual information in network traffic flows. To address these issues, this research models APTs as heterogeneous graphs, capturing the diverse features and complex interactions in network flows. Consequently, a hetero-geneous graph transformer (HGT) model is used to accurately distinguish between benign and malicious network connections. Experiment results reveal that the HGT model achieves better performance, with 100 % accuracy and accelerated learning time, outperferming homogeneous graph neural network models.

Representation Learning with Attention for Spatial Reuse Optimization in Dense WLANs
Stephen Azeez and Shagufta Henna
Springer Nature Singapore

Graph Modelling and Graph-Attention Neural Network for Immune Response Prediction
Mallikharjuna Rao Sakhamuri, Shagufta Henna, Leo Creedon, and Kevin Meehan
IEEE
The recent Covid-19 pandemic has attracted significant attention toward understanding the innate immune response of humans. To develop new drugs and antibiotics that activate T-cells to combat malicious pathogens, it is necessary to comprehend the immune system, including T-cell response, peptides, and Human Leukocyte Antigens (HLA) interactions. Traditional machine learning models, such as Convolutional Neural Networks (CNNs) and Feed Forward Networks (FNNs) are limited to feature extraction of peptides to predict immunogenicity values. CNNs and FNNs cannot capture the underlying structure and relationships between HLA and peptides, and therefore, do not assist with the immune response predictions. To address these issues, firstly this paper models the Immune Epitope dataset as a graph to capture dependencies and interactions among HLAs and peptides. Secondly, to assess the performance of the graph model, the results of the Graph Neural Network (GNN) are validated against the results of FNN. The results show that the GNN has better performance efficiency over conventional models in terms of accuracy and other performance metrics, thereby recommending graph-based deep learning as an efficient tool for drug discovery, diagnosis, and other immunology.

Multi-armed Bandit-based Channel Bonding for Off-body Communication in IEEE 802.15.6 Cognitive Radio Wireless Body Area Networks
Shagufta Henna, Kevin Meehan, and Mallikharjuna Rao Sakhamuri
IEEE
Off-body communication in Cognitive Radio Wireless Body Area Networks (CRWBANs) must cope with different types of interference and dynamic traffic loads. Existing static channel assignment schemes cannot handle dynamic traffic loads with higher data rate requirements. One of the major problems experienced by CRWBANs is contention across wireless body area networks, thereby affecting the network throughput. Channel bonding has been successfully applied to different networks, such as Wireless Local Area Networks (WLANs), cognitive radio sensor networks, and wireless sensor networks. However, its use for the off-body communication in IEEE 802.15.6-based CRWBANs has not been investigated. This article proposes a multi-armed bandit-based bonded channel algorithm (MAB-BCA) with Upper Confidence Bound (UCB) for channel bonding to improve the off-body network capacity. The MAB-BCA based on UCB algorithm maximizes the capacity of off-body communication in CRWBANs. It demonstrates significant performance improvement over Static Channel Assignment (SCA) and Reinforcement Learning—Channel Assignment Algorithm (RL-CAA) in terms of average throughput and comparable bit error rate and dissatisfaction probability.

Multi-Graph Convolutional Neural Network for Breast Cancer Multi-task Classification
Mohamed Ibrahim, Shagufta Henna, and Gary Cullen
Springer Nature Switzerland
AbstractMammography is a popular diagnostic imaging procedure for detecting breast cancer at an early stage. Various deep-learning approaches to breast cancer detection incur high costs and are erroneous. Therefore, they are not reliable to be used by medical practitioners. Specifically, these approaches do not exploit complex texture patterns and interactions. These approaches warrant the need for labelled data to enable learning, limiting the scalability of these methods with insufficient labelled datasets. Further, these models lack generalisation capability to new-synthesised patterns/textures. To address these problems, in the first instance, we design a graph model to transform the mammogram images into a highly correlated multigraph that encodes rich structural relations and high-level texture features. Next, we integrate a pre-training self-supervised learning multigraph encoder (SSL-MG) to improve feature presentations, especially under limited labelled data constraints. Then, we design a semi-supervised mammogram multigraph convolution neural network downstream model (MMGCN) to perform multi-classifications of mammogram segments encoded in the multigraph nodes. Our proposed frameworks, SSL-MGCN and MMGCN, reduce the need for annotated data to 40% and 60%, respectively, in contrast to the conventional methods that require more than 80% of data to be labelled. Finally, we evaluate the classification performance of MMGCN independently and with integration with SSL-MG in a model called SSL-MMGCN over multi-training settings. Our evaluation results on DSSM, one of the recent public datasets, demonstrate the efficient learning performance of SSL-MNGCN and MMGCN with 0.97 and 0.98 AUC classification accuracy in contrast to the multitask deep graph (GCN) method Hao Du et al. (2021) with 0.81 AUC accuracy.

Erratum: Performance Evaluation of LoRaWAN for Green Internet of Things (IEEE Access (2019) 7 (164102-164112) DOI: 10.1109/ACCESS.2019.2943720)
Zulfiqar Ali, Shagufta Henna, Adnan Akhunzada, Mohsin Raza, and Sung Won Kim
Institute of Electrical and Electronics Engineers (IEEE)

Shagufta Henna

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Scopus Publications