Rabia Latif

Scopus Publications

BBAS: A blockchain-based authentication system for e-health with multi-factor authentication, access control, and post-quantum security
Rabia Latif, Bello Musa Yakubu, Nor Shahida Mohd Jamail, Amir Mohamed Talib, Fahad Omar Alomary
Scientific Reports, 2026
The rapid digitisation of healthcare services presents challenges in guaranteeing safe, scalable, and privacy-preserving access to sensitive medical information. This article presents BBAS, a blockchain-based authentication system for e-Health. BBAS incorporates a multi-factor authentication (MFA) framework that includes password hashing, one-time passwords (OTP), and biometric verification, with a hybrid access control model that combines role-based access control (RBAC) and attribute-based access control (ABAC). To guarantee enduring security, BBAS utilises post-quantum digital signatures (CRYSTALS-Dilithium) and exploits the InterPlanetary file system (IPFS) for off-chain data storage, assuring tamper-resistance and scalability. We implemented the system using solidity smart contracts on a permissioned Ethereum network and assessed via 500 authentication iterations. Results show BBAS outperforms benchmark models across all critical metrics: authentication success rate (ASR: 98.6%), latency (0.05 s), throughput (19,000 req/s), gas cost (35,000 gas/req), block confirmation time (10 s), and storage overhead (0.03 KB/record). Biometric error rates-false acceptance rate (FAR: 0.5%), false rejection rate (FRR: 1.2%), and equal error rate (EER: 0.85%)-are markedly decreased, therefore improving both security and usability. This research validates BBAS as a reliable, scalable, and quantum-resistant authentication framework for contemporary e-Health systems.
Blockchain based precision rice farming framework using deep learning techniques
Zauwali Sabitu Paki, Bello Musa Yakubu, Souley Boukari, Rabia Latif, Nor Shahida Mohd Jamail, Abdulsalam Yau Gital, Suliman Mohamed Fati
Discover Internet of Things, 2026
Food security is a critical issue, and rice is one of the world’s staple foods on which more than half of the world feeds. Its growing demand calls for sustainable agriculture methods that can meet present and future demands while tackling resource restrictions and climate change. Precision agriculture, or smart agriculture (SA), uses data analytics, sensors, drones, and machine learning (ML) algorithms to enhance agricultural techniques, minimise waste, and alleviate environmental effects. This study seeks to provide a safe framework (RiceBlock model) for precision rice farming integrating blockchain, Internet of Things (IoT), and deep learning technologies. The framework proposes an IoT-enabled sensor network for real-time agricultural data collection, a blockchain for securing the collected data, and a deep learning-driven analytics model for rice yield prediction and automated decision-making. The proposed model demonstrated superior rice yield prediction accuracy against the state-of-the-art models in terms of R2 (0.97), RMSE (1.38), MAE (1.16), and NRMSE (0.04). It ensures data security, integrity, and resilience against known attacks and data manipulation.
Secured-FL: Blockchain-Based Defense against Adversarial Attacks on Federated Learning Models
Bello Musa Yakubu, Nor Shahida Mohd Jamail, Rabia Latif, Seemab Latif
Computers Materials and Continua, 2026
Federated Learning (FL) enables joint training over distributed devices without data exchange but is highly vulnerable to attacks by adversaries in the form of model poisoning and malicious update injection. This work proposes Secured-FL, a blockchain-based defensive framework that combines smart contract–based authentication, clustering-driven outlier elimination, and dynamic threshold adjustment to defend against adversarial attacks. The framework was implemented on a private Ethereum network with a Proof-of-Authority consensus algorithm to ensure tamper-resistant and auditable model updates. Large-scale simulation on the Cyber Data dataset, under up to 50% malicious client settings, demonstrates Secured-FL achieves 6%–12% higher accuracy, 9%–15% lower latency, and approximately 14% less computational expense compared to the PPSS benchmark framework. Additional tests, including confusion matrices, ROC and Precision-Recall curves, and ablation tests, confirm the interpretability and robustness of the defense. Tests for scalability also show consistent performance up to 500 clients, affirming appropriateness to reasonably large deployments. These results make Secured-FL a feasible, adversarially resilient FL paradigm with promising potential for application in smart cities, medicine, and other mission-critical IoT deployments.
Cross-domain recommendation framework for enhanced personalization through contrastive learning
Rabia Khan, Naima Iltaf, Rabia Latif, Nor Shahida Mohd Jamail
Journal of Supercomputing, 2026
INVESTIGATION OF RADIOISOTOPES POPULATION DYNAMICS BY THE HELP OF MODELING AND ARTIFICIAL INTELLIGENCE
HASIB KHAN, HADEEL BIN AMER, RABIA LATIF, WAFA F. ALFWZAN, RAJERMANI THINAKARAN
Fractals, 2026
This paper investigates the alpha-decay chain reaction of Uranium-238 ([Formula: see text]) into Thorium-234 ([Formula: see text]) and Radium-226 ([Formula: see text]) through a combination of computational simulations and theoretical analysis. The study focuses on the role of varying Uranium-238 and decay constants on the final quantity. Results indicate that higher initial Uranium-238 quantities yield increased final quantities of both Thorium-234 and Radium-226, with decay constants significantly influencing the system’s behavior. A fixed-point technique is employed to ensure the existence of solutions, confirming the system’s stability and convergence to steady-state solutions. Additionally, the potential of artificial intelligence (AI) techniques is discussed, highlighting their utility in optimizing decay models and improving computational efficiency. The integration of AI offers deeper insights into parameter optimization and outcome prediction for complex decay systems. These findings contribute to the understanding of radioactive decay dynamics and provide a framework for more advanced computational models in nuclear science, as a process innovation.
HIGHLIGHTING COMPLEX UNCERTAINTIES IN AN SIRS-INFECTION SPREADING SYSTEM
ABDULWASEA ALKHAZZAN, HADEEL BIN AMER, RABIA LATIF, WAFA F. ALFWZAN, HASIB KHAN
Fractals, 2026
This paper introduces a novel stochastic susceptible-infected-recovered-susceptible (SIRS) epidemic model tailored to study transportation-related infections. The model is analyzed both theoretically and computationally, offering new insights into the dynamics of disease spread in interconnected urban environments. Theoretically, we employ a Lyapunov function to establish the existence of a global positive solution and demonstrate that the solution is stochastically ultimately bounded (SUB) and stochastically permanent. Additionally, we derive a crucial sufficient condition to determine when the infectious disease may die out in the two cities under study. In the numerical analysis, we implement two distinct computational methods: the stochastic Euler–Maruyama (SEM) method and the stochastic nonstandard finite difference (SNSFD) method, to validate the theoretical findings of the studied model. When comparing the two numerical methods, our results show that the SNSFD method outperforms the SEM method in preserving key dynamic characteristics, such as positivity, boundedness, and stability, especially for larger temporal step sizes. This comparative analysis highlights the robustness and efficiency of the SNSFD scheme in handling complex stochastic epidemic models. The findings are illustrated with clear and detailed graphs, providing an accessible understanding of the model’s behavior under various parameter configurations. This work contributes to the field by combining theoretical rigor with innovative computational techniques, offering a comprehensive framework for studying transportation-related infectious diseases.
Whisper in Focus: Parameter-Efficient Stuttering Disfluency Classification
Huma Ameer, Mehwish Fatima, Esam Mohammed Asem Othman, Sana Mukhtar, Nor Shahida Mohd Jamail, Rabia Latif, Seemab Latif
IEEE Access, 2026
Stuttering disfluency classification remains a complex challenge in speech processing due to the high variability of disfluency patterns. Transformer-based models such as Wav2Vec2.0 have demonstrated promising results but struggle with generalization across diverse speakers and incur substantial computational costs. In this study, we present a parameter-efficient adaptation of Whisper, an encoder-decoder transformer trained on 680,000 hours of labeled speech-text pairs, for automatic classification of stuttering disfluencies. We introduce three key contributions: (1) a refined SEP-28k dataset with improved quality and speaker diversity, (2) novel layer-freezing strategies within Whisper’s encoder to balance task-specific learning and computational efficiency, and (3) extensive evaluation on the FluencyBank dataset to assess model generalizability. Our experimental results show that Whisper achieves a weighted F1-score of 0.812, outperforming Wav2Vec2.0 and HuBERT while reducing trainable parameters by 46% and training time by 37%. Analysis reveals that deeper encoder layers encode critical task-specific representations necessary for robust disfluency detection.
ZenGuard a machine learning based zero trust framework for context aware threat mitigation using SIEM SOAR and UEBA
Aamina Hassan, Abdul Rauf, Narmeen Shafqat, Rabia Latif, Hasib Khan
Scientific Reports, 2025
Perimeter-based security models, which rely on predefined network boundaries, are increasingly ineffective against modern threats such as insider misuse, supply chain attacks, and Advanced Persistent Threats (APTs). Zero Trust Architecture (ZTA) offers a more resilient approach by enforcing continuous verification of users, devices, and activity. While SIEM (Security Information and Event Management) and SOAR (Security Orchestration, Automation, and Response) platforms are widely adopted and play a critical role in monitoring and response, they often operate with static rules and limited behavioral context, making it challenging to fully implement ZTA principles. ZenGuard addresses these operational gaps by introducing context-aware, real-time, and adaptive enforcement capabilities. This paper introduces ZenGuard, an open-source framework that integrates ZTA, SIEM, SOAR, and User and Entity Behavior Analytics (UEBA) into a unified, vendor-independent platform. ZenGuard employs Python-based automation and interpretable machine learning models to detect behavioral anomalies and trigger adaptive responses across identity, device, and network layers. We evaluate ZenGuard using real-world Security Operation Center (SOC) telemetry from enterprise environments to validate overall threat detection and response, demonstrating a Mean Time to Respond (MTTR) under 10 seconds in cases such as privilege escalation, lateral movement and data exfiltration. Furthermore, UEBA accuracy was assessed on synthetic behavioral datasets that emulate diverse threats that are not consistently observable in live environments. In essence, ZenGuard supports Zero Trust principles as defined by NIST SP 800-207 and ISO/IEC 27001 controls, offering a practical, explainable, and scalable approach to modern cybersecurity automation.
AI-Based Deep Learning of the Water Cycle System and Its Effects on Climate Change
Hasib Khan, Wafa F. Alfwzan, Rabia Latif, Jehad Alzabut, Rajermani Thinakaran
Fractal and Fractional, 2025
This study combines artificial intelligence (AI) with mathematical modeling to improve the forecasting of the water cycle mechanism. The proposed model simulates the development of global temperature, precipitation, and water availability, integrating key climate parameters that control these dynamics. Using a system of fractional-order differential equations in the fractal–fractional sense of derivatives, the model captures interactions between solar radiation, the greenhouse effect, evaporation, and runoff. The deep learning framework is trained on extensive climate datasets, allowing it to refine predictions and identify complex patterns within the water cycle. By applying AI techniques alongside mathematical modeling, this procedure provides valuable insights into climate change and water resource administration. The model’s predictions can contribute to assessing future climate states, optimizing environmental policies, and designing sustainable water management strategies. Furthermore, the hybrid methodology improves decision-making by offering data-driven solutions for climate adaptation. The findings illustrate the effectiveness of AI-driven models in addressing global climate challenges with improved precision.
From Packets to Semantics: Transformer-Driven Privacy-Preserving ETA for OTT App Classification
Aamina Hassan, Abdul Rauf, Rabia Latif, Ahmed Iftikhar Baig, Hélène Kanso
IEEE Access, 2025
The rapid adoption of TLS 1.3, QUIC, and Encrypted Client Hello (ECH) has rendered traditional Deep Packet Inspection ineffective, motivating a new generation of encrypted traffic analysis (ETA) methods that operate without decryption. This paper advances the state of the art in Over-the-Top (OTT) application classification by introducing a unified framework that models packet bytes and Inter-arrival timing as byte–time sequences. Within this framework, we conduct a controlled and reproducible comparison between a Bi Directional Transformer-based encoder (BERT) and a recurrent Long Short-Term Memory (LSTM) model eliminating variations in preprocessing, training, and evaluation to ensure methodological parity. Comprehensive class-wise metrics and learning curves demonstrate the superiority of self-attention architectures in learning encrypted flow dynamics. On ISCXVPN2016, the Transformer achieves 99.65% accuracy and 99.28% recall, outperforming published baselines by up to 2.15 percentage points. On CSTNET (50 traffic types), it improves macro-F1 by 15.1 points over the LSTM, with notable gains for challenging classes (e.g., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">gitlab.com</i>: 0.979 vs. 0.6075 F1). These results establish Transformer encoders as a robust and generalizable foundation for privacy-preserving traffic analytics, network policy enforcement, and Security Operations Center (SOC) automation in fully encrypted environments. Unlike earlier Transformer-based ETA studies, our findings stem from the first rigorously controlled, like-for-like comparison against an LSTM model, highlighting the true architectural gains of self-attention.
DRIFTNET-EnVACK: Adaptive Drift Detection in Cloud Data Streams With Ensemble Variational Auto-Encoder Featuring Contextual Network
Tajwar Mehmood, Seemab Latif, Rabia Latif, Hammad Majeed, Asad Waqar Malik
IEEE Access, 2024
LSTMDD: an optimized LSTM-based drift detector for concept drift in dynamic cloud computing
Tajwar Mehmood, Seemab Latif, Nor Shahida Mohd Jamail, Asad Malik, Rabia Latif
Peerj Computer Science, 2024
Dual Modality Reverse Reranking (DM-RR) Based Image Retrieval Framework
IKHLAQ AHMED, NAIMA ILTAF, RABIA LATIF, NOR SHAHIDA MOHD JAMAIL, ZAFRAN KHAN
IEEE Open Journal of the Industrial Electronics Society, 2024
Efficient Text Style Transfer Through Robust Masked Language Model and Iterative Inference
Osama Subhani Khan, Naima Iltaf, Usman Zia, Rabia Latif, Nor Shahida Mohd Jamail
IEEE Access, 2024
MarketTrust: blockchain-based trust evaluation model for SIoT-based smart marketplaces
Rabia Latif, Bello Musa Yakubu, Tanzila Saba
Scientific Reports, 2023
Assessing Urdu Language Processing Tools via Statistical and Outlier Detection Methods on Urdu Tweets
Zoya, Seemab Latif, Rabia Latif, Hammad Majeed, Nor Shahida Mohd Jamail
ACM Transactions on Asian and Low Resource Language Information Processing, 2023
A Survey of Blockchain Technology: Architecture, Applied Domains, Platforms, and Security Threats
Ayesha Altaf, Faiza Iqbal, Rabia Latif, Bello Musa Yakubu, Seemab Latif, Hamza Samiullah
Social Science Computer Review, 2023
ADAL-NN: Anomaly Detection and Localization Using Deep Relational Learning in Distributed Systems
Kashan Ahmed, Ayesha Altaf, Nor Shahida Mohd Jamail, Faiza Iqbal, Rabia Latif
Applied Sciences Switzerland, 2023
Policy-Based Spam Detection of Tweets Dataset
Momna Dar, Faiza Iqbal, Rabia Latif, Ayesha Altaf, Nor Shahida Mohd Jamail
Electronics Switzerland, 2023
Image Encryption Scheme Based on Orbital Shift Pixels Shuffling with ILM Chaotic System
Wajid Ali, Congxu Zhu, Rabia Latif, Muhammad Asim, Muhammad Usman Tariq
Entropy, 2023
Multi Factor Authentication as a Service (MFAaaS) for Federated Cloud Environments
Sara Ahmed AlAnsary, Rabia Latif, Tanzila Saba
Lecture Notes in Networks and Systems, 2023
Trust Management Frameworks in Multi-Cloud Environment: A Review
Arwa Alajroush, Rabia Latif, Tanzila Saba
Proceedings 2023 6th International Conference of Women in Data Science at Prince Sultan University Wids Psu 2023, 2023
CrossDomain Recommendation Based on MetaData Using Graph Convolution Networks
Rabia Khan, Naima Iltaf, Rabia Latif, Nor Shahida Mohd Jamail
IEEE Access, 2023
Web Scraping for Data Analytics: A BeautifulSoup Implementation
Ayat Abodayeh, Reem Hejazi, Ward Najjar, Leena Shihadeh, Rabia Latif
Proceedings 2023 6th International Conference of Women in Data Science at Prince Sultan University Wids Psu 2023, 2023
A novel trust management model for edge computing
Rabia Latif, Malik Uzair Ahmed, Shahzaib Tahir, Seemab Latif, Waseem Iqbal, Awais Ahmad
Complex and Intelligent Systems, 2022
A novel searchable encryption scheme to reduce the access pattern leakage
Muhammad Awais, Shahzaib Tahir, Fawad Khan, Hasan Tahir, Ruhma Tahir, Rabia Latif, Mir Yasir Umair
Future Generation Computer Systems, 2022
Machine learning for post-traumatic stress disorder identification utilizing resting-state functional magnetic resonance imaging
Tanzila Saba, Amjad Rehman, Mirza Naveed Shahzad, Rabia Latif, Saeed Ali Bahaj, Jaber Alyami
Microscopy Research and Technique, 2022
ConTrust: A Novel Context-Dependent Trust Management Model in Social Internet of Things
Rabia Latif
IEEE Access, 2022
Privacy Concerned on Contact-Tracing Application during COVID-19
Afnan AlFaadhel, Rabia Latif
Proceedings 2022 5th International Conference of Women in Data Science at Prince Sultan University Wids Psu 2022, 2022
Location Privacy Issues in Location-Based Services
Manal AlShalaan, Reem AlSubaie, Rabia Latif
Proceedings 2022 5th International Conference of Women in Data Science at Prince Sultan University Wids Psu 2022, 2022
Social Media Privacy Issues, Threats, and Risks
Gahadh Faisal AlMudahi, Lama Khalid AlSwayeh, Sara Ahmed AlAnsary, Rabia Latif
Proceedings 2022 5th International Conference of Women in Data Science at Prince Sultan University Wids Psu 2022, 2022
Students Personality Assessment using Deep Learning from University Admission Statement of Purpose
Salma Kulsoom, Seemab Latif, Tanzila Saba, Rabia Latif
Proceedings 2022 7th International Conference on Data Science and Machine Learning Applications Cdma 2022, 2022
RiceChain: secure and traceable rice supply chain framework using blockchain technology
Bello Musa Yakubu, Rabia Latif, Aisha Yakubu, Majid Iqbal Khan, Auwal Ibrahim Magashi
Peerj Computer Science, 2022
A novel cloud management framework for trust establishment and evaluation in a federated cloud environment
Rabia Latif, Syeda Hadia Afzaal, Seemab Latif
Journal of Supercomputing, 2021
T-smart: Trust model for blockchain based smart marketplace
Muhammad Waleed, Rabia Latif, Bello Musa Yakubu, Majid Iqbal Khan, Seemab Latif
Journal of Theoretical and Applied Electronic Commerce Research, 2021
From Transformers to Reformers
Nauman Riaz, Seemab Latif, Rabia Latif
2021 International Conference on Digital Futures and Transformative Technologies Icodt2 2021, 2021
Fine Tuning BERT for Unethical Behavior Classification
Syeda Faizan Fatima, Seemab Latif, Rabia Latif
2021 International Conference on Digital Futures and Transformative Technologies Icodt2 2021, 2021
Transfer Learning Grammar for Multilingual Surface Realisation
Atif Khurshid, Seemab Latif, Rabia Latif
2021 International Conference on Digital Futures and Transformative Technologies Icodt2 2021, 2021
Enterprise Architecture Frameworks Assessment: Capabilities, Cyber Security and Resiliency Review
Hilalah F. Al-Turkistani, Samar Aldobaian, Rabia Latif
2021 1st International Conference on Artificial Intelligence and Data Analytics Caida 2021, 2021
Wheat Plant Counting Using UAV Images Based on Semi-supervised Semantic Segmentation
Hamza Mukhtar, Muhammad Zeeshan Khan, Muhammad Usman Ghani Khan, Tanzila Saba, Rabia Latif
2021 1st International Conference on Artificial Intelligence and Data Analytics Caida 2021, 2021
Question Classification Using Universal Sentence Encoder and Deep Contextualized Transformer
Najam Arif, Seemab Latif, Rabia Latif
Proceedings International Conference on Developments in Esystems Engineering Dese, 2021
Analyzing LDA and NMF Topic Models for Urdu Tweets via Automatic Labeling
Zoya, Seemab Latif, Faisal Shafait, Rabia Latif
IEEE Access, 2021
Behavioral Based Insider Threat Detection Using Deep Learning
Rida Nasir, Mehreen Afzal, Rabia Latif, Waseem Iqbal
IEEE Access, 2021
Detection and Prevention of a Malicious Activity in Industrial Federated Cloud Computing Paradigm
Akash Gerard, Rabia Latif, Waseem Iqbal, Naqash Gerard, Ahmed Husnain Johar, Umer Asghar
Advances in Intelligent Systems and Computing, 2021
Suspicious Activity Recognition Using Proposed Deep L4-Branched-Actionnet with Entropy Coded Ant Colony System Optimization
Tanzila Saba, Amjad Rehman, Rabia Latif, Suliman Mohamed Fati, Mudassar Raza, Muhammad Sharif
IEEE Access, 2021
Crowdsourcing Cybercrimes through Online Resources
Naila Amir, Rabia Latif, Narmeen Shafqat, Seemab Latif
Proceedings International Conference on Developments in Esystems Engineering Dese, 2020
MAD-Malicious Activity Detection Framework in Federated Cloud Computing
Akash Gerard, Rabia Latif, Seemab Latif, Waseem Iqbal, Tanzila Saba, Naqash Gerard
Proceedings International Conference on Developments in Esystems Engineering Dese, 2020
A computational forensic framework for detection of hidden applications on android
Tahira Rasul, Rabia Latif, Nor Shahida Mohd Jamail
Indonesian Journal of Electrical Engineering and Computer Science, 2020
Analytical study of hardware-rooted security standards and their implementation techniques in mobile
Naveeda Ashraf, Ashraf Masood, Haider Abbas, Rabia Latif, Narmeen Shafqat
Telecommunication Systems, 2020
Backward-Forward Sequence Generative Network for Multiple Lexical Constraints
Seemab Latif, Sarmad Bashir, Mir Muntasar Ali Agha, Rabia Latif
IFIP Advances in Information and Communication Technology, 2020
Malicious Insider Attack Detection in IoTs Using Data Analytics
Ahmed Yar Khan, Rabia Latif, Seemab Latif, Shahzaib Tahir, Gohar Batool, Tanzila Saba
IEEE Access, 2020
Kleptographic Attack on Elliptic Curve Based Cryptographic Protocols
Anum Sajjad, Mehreen Afzal, Mian Muhammad Waseem Iqbal, Haider Abbas, Rabia Latif, Rana Aamir Raza
IEEE Access, 2020
Malicious insiders attack in IoT based Multi-Cloud e-Healthcare environment: A Systematic Literature Review
Afsheen Ahmed, Rabia Latif, Seemab Latif, Haider Abbas, Farrukh Aslam Khan
Multimedia Tools and Applications, 2018
Enhancing E-Healthcare Privacy Preservation Framework through L-Diversity
Adeel Shah, Haider Abbas, Waseem Iqbal, Rabia Latif
2018 14th International Wireless Communications and Mobile Computing Conference Iwcmc 2018, 2018
An adaptive rule-based approach to resolving real-time VoIP wholesale billing disputes
Nazish Yaqoob, Seemab Latif, R. Latif, Haider Abbas, Asif Yaseen
Journal of Information Science and Engineering, 2017
Performance evaluation of Enhanced Very Fast Decision Tree (EVFDT) mechanism for distributed denial-of-service attack detection in health care systems
Haider Abbas, Rabia Latif, Seemab Latif, Ashraf Masood
Annales Des Telecommunications Annals of Telecommunications, 2016
Distributed Denial of Service Attack Source Detection Using Efficient Traceback Technique (ETT) in Cloud-Assisted Healthcare Environment
Rabia Latif, Haider Abbas, Seemab Latif, Ashraf Masood
Journal of Medical Systems, 2016
Distributed denial of service (DDoS) attack detection using data mining approach in cloud-assisted wireless body area networks
Rabia Latif, Haider Abbas, Seemab Latif
International Journal of Ad Hoc and Ubiquitous Computing, 2016
EVFDT: An Enhanced Very Fast Decision Tree Algorithm for Detecting Distributed Denial of Service Attack in Cloud-Assisted Wireless Body Area Network
Rabia Latif, Haider Abbas, Seemab Latif, Ashraf Masood
Mobile Information Systems, 2015
Designing an assistive learning aid for writing acquisition: A challenge for children with dyslexia
S. Latif, Rabbia Tariq, Shehla Tariq, R. Latif
Studies in Health Technology and Informatics, 2015
Distributed Denial of Service (DDoS) Attack in Cloud- Assisted Wireless Body Area Networks: A Systematic Literature Review
Rabia Latif, Haider Abbas, Saïd Assar
Journal of Medical Systems, 2014
Analyzing feasibility for deploying very fast decision tree for DDoS attack detection in cloud-assisted WBAN
Rabia Latif, Haider Abbas, Saïd Assar, Seemab Latif
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2014
Cloud computing risk assessment: A systematic literature review
Rabia Latif, Haider Abbas, Saïd Assar, Qasim Ali
Lecture Notes in Electrical Engineering, 2014
A boundary node detection based solution for minimizing the effects of nodes distribution over key exchange in group based WSN
Rabia Latif, Asad Raza
Proceedings of the 2012 15th International Conference on Network Based Information Systems Nbis 2012, 2012
Hardware-based random number generation in wireless sensor networks(WSNs)
Rabia Latif, Mukhtar Hussain
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2009

Rabia Latif

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