Sidra Aslam

dblp:35/9283 · DBLP profile ↗
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8ranked-venue papers
4as first author
6since 2021 · last 2026
—ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 5 · 3 first-author · 4 since 2021Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-author · 1 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021
YearPublicationVenuePosition
2026 Security and deployment challenges in software-defined vehicular networks: A systematic review
Sidra Aslam, Alireza Esfahani, Shidrokh Goudarzi, Antonino Masaracchia, Shahid Mumtaz
Comput. Networks1
2026 Deep learning-driven prediction of chemotherapy response in breast cancer: a pathway toward precision medicine
Fizza Rimal Butt, Tanveer Mustafa, Muhammad Mushtaq Ahmad, Zaighum Abbas, Sidra Aslam, Daniela Calina, Javad Sharifi-Rad, Muhammad J. Iqbal
Soft Comput.6
2025 A Novel and Secure Machine Learning-Based Hyperledger Blockchain for IoT Healthcare
abstract
Data privacy protection and secure sharing are the main issues faced by smart healthcare IoT systems. In medical uses, patient health information is frequently kept in the cloud, which limits the user’s ability to entirely control their data. Additionally, standard encryption keys do not sufficiently mitigate the risks posed by malicious entities like compromised cloud service providers. To address these issues, blockchain technology, combined with Internet of Medical Things (IoMT) can securely safeguard patient medical records through a peer-to-peer, secure, and collective ledger. Therefore, we propose a novel IoT-driven architecture that leverages blockchain technology to protect patient medical files from tampering and unauthorized access. This architecture integrates patient medical files with blockchain and is enhanced by a combination of Bidirectional Long Short-Term Memory (BiLSTM) networks and Convolutional Neural Networks (CNN). Utilizing blockchain for the transmission of encrypted data significantly strengthens data security and minimizes the risk of data breaches. The process of generating encryption and decryption keys through a coupled CNN and BiLSTM ensures the robustness and uniqueness of these keys. Additionally, the selection of the best key is performed using the Gradient Descent Optimization Algorithm (GDOA), which demonstrates the effectiveness and efficiency of the encryption and decryption process. We also compare the implementation of our model with existing technologies, assessing its performance based on various metrics, including restoration efficiency, response time, record time, key generation time, encryption time, decryption time, turnaround time, and overall running time. Our proposed method is confirmed to be more effective than current techniques in terms of these performance metrics.
Sidra Aslam, Saba Aslam, Taotao Wang, Daquan Feng, Shengli Zhang 0001
IEEE Internet Things J.1
2025 On detecting stock price manipulation attacks: a comprehensive systematic literature review
Amal Alfajeer, Ala Altaweel, Ahmed Bouridane, Djedjiga Mouheb, Sidra Aslam
Multim. Tools Appl.5
2024 Security attacks in Opportunistic Mobile Networks: A systematic literature review
Ala Altaweel, Sidra Aslam, Ibrahim Kamel
J. Netw. Comput. Appl.2
2024 JamholeHunter: On detecting new wormhole attack in Opportunistic Mobile Networks
Ala Altaweel, Sidra Aslam, Ibrahim Kamel
J. Netw. Comput. Appl.2
2020 OBAC: towards agent-based identification and classification of roles, objects, permissions (ROP) in distributed environment
Sidra Aslam, Mansoor Ahmed, Imran Ahmed 0002, Abid Khan, Awais Ahmad 0001, Muhammad Imran 0007, Adeel Anjum, Shahid Hussain 0001
Multim. Tools Appl.1
2017 Information collection centric techniques for cloud resource management: Taxonomy, analysis and challenges
Sidra Aslam, Saif ul Islam, Abid Khan, Mansoor Ahmed, Adnan Akhunzada, Muhammad Khurram Khan
J. Netw. Comput. Appl.1