Aaisha Makkar
| Name | Aaisha Makkar |
|---|---|
| Job title | Lecturer in Computer Science |
| Research institute | College of Science and Engineering |
| ORCID | https://orcid.org/0000-0001-7203-6553 |
Research outputs
A Fuzzy-based approach to Enhance Cyber Defence Security for Next-generation IoT
Makkar, A., Ghosh, U., Sharma, P.K. and Javed, A. 2023. A Fuzzy-based approach to Enhance Cyber Defence Security for Next-generation IoT. IEEE Internet of Things Journal. Vol 10 (Issue 3), pp. 2079-2086. https://doi.org/10.1109/jiot.2021.3053326SecureEngine: Spammer classification in cyber defence for leveraging green computing in Sustainable city
Aaisha Makkar 2022. SecureEngine: Spammer classification in cyber defence for leveraging green computing in Sustainable city. Sustainable Cities and Society. 79 (April 2022), p. 103658. https://doi.org/10.1016/j.scs.2021.103658SecureIIoT Environment: Federated Learning Empowered Approach for Securing IIoT From Data Breach
Aaisha Makkar, Tae Woo Kim, Ashutosh Kumar Singh, Jungho Kang and Jong Hyuk Park 2022. SecureIIoT Environment: Federated Learning Empowered Approach for Securing IIoT From Data Breach. IEEE Transactions on Industrial Informatics. 18 (9), pp. 6406 - 6414. https://doi.org/10.1109/tii.2022.3149902Visualization and deep-learning-based malware variant detection using OpCode-level features
Darem, A., Abawajy, J., Makkar, A., Alhashmi, A. and Alanazi, S. 2021. Visualization and deep-learning-based malware variant detection using OpCode-level features. Future Generation Computer Systems. Vol 125 (Dec 2021), pp. 314-323. https://doi.org/10.1016/j.future.2021.06.032An Efficient Spam Detection Technique for IoT Devices Using Machine Learning
Makkar, A., Garg, S., Kumar, N., Hossain, M.S., Ghoneim, A. and Alrashoud, M. 2021. An Efficient Spam Detection Technique for IoT Devices Using Machine Learning. IEEE Transactions on Industrial Informatics. Vol 17 (Issue 2), pp. 903-912. https://doi.org/10.1109/tii.2020.2968927PROTECTOR: An optimized deep learning-based framework for image spam detection and prevention
Makkar, A. and Kumar, N. 2021. PROTECTOR: An optimized deep learning-based framework for image spam detection and prevention. Future Generation Computer Systems. 125 (Dec 2021), pp. 41-58. https://doi.org/10.1016/j.future.2021.06.026Artificial Intelligence and Edge Computing-enabled Web Spam Detection for Next Generation IoT Applications
Makkar, A., Ghosh, U. and Sharma, P.K. 2021. Artificial Intelligence and Edge Computing-enabled Web Spam Detection for Next Generation IoT Applications. IEEE Sensors Journal. 21 (Issue: 22), pp. 25352 - 25361. https://doi.org/10.1109/jsen.2021.3066492FedLearnSP: Preserving Privacy and Security using Federated Learning and Edge Computing
Makkar, A., Ghosh, U., Rawat, D.B. and Abawajy, J. 2021. FedLearnSP: Preserving Privacy and Security using Federated Learning and Edge Computing. IEEE. https://doi.org/10.1109/mce.2020.3048926Ai based management of food wastage
Sama, L., Makkar, A., Prokshitha, P., Sharma, B.K. and Dhaloria, D. 2021. Ai based management of food wastage. 2021 International Semantic Intelligence Conference, ISIC 2021; New Delhi; India; 25 February 2021 through 27 February 2021; Code 167510. CEUR Workshops Proceedings.An Intelligent Phishing Detection Scheme Using Machine Learning
Makkar, A., Kumar, N., Sama, L., Mishra, S. and Samdani, Y. Giri D., Buyya R., Ponnusamy S., De D., Adamatzky A. and Abawajy J.H. (ed.) An Intelligent Phishing Detection Scheme Using Machine Learning. Springer.SPAMI: A cognitive spam protector for advertisement malicious images
Makkar, A., Kumar, N., Zomaya, A.Y. and Dhiman, S. 2020. SPAMI: A cognitive spam protector for advertisement malicious images. Information Sciences. 540 (Nov 2020), pp. 17-37. https://doi.org/10.1016/j.ins.2020.05.113An efficient deep learning-based scheme for web spam detection in IoT environment
Makkar, A. and Kumar, N. 2020. An efficient deep learning-based scheme for web spam detection in IoT environment. Future Generation Computer Systems. 108 (July 2020), pp. 467-487. https://doi.org/10.1016/j.future.2020.03.004DIADL: An Energy Efficient Framework for Detecting Intrusion Attack Using Deep LearnIing
Sama, L., Makkar, A., Mishra, S.K. and Samdani, Y. 2020. DIADL: An Energy Efficient Framework for Detecting Intrusion Attack Using Deep LearnIing. 12th International Conference on Computer Modeling and Simulation, ICCMS 2020 and the 9th International Conference on Intelligent Computing and Applications. ICICA 2020; Virtual, Online; Australia; 22 June 2020 through 24 June 2020; Code 162275. ACM. https://doi.org/10.1145/3408066.3408107The Power of AI in IoT : Cognitive IoT-based Scheme for Web Spam Detection
Makkar, A., Kumar, N. and Guizani, M. 2019. The Power of AI in IoT : Cognitive IoT-based Scheme for Web Spam Detection. 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019; Xiamen; China; 6 December 2019 through 9 December 2019; Category numberCFP19COI-ART; Code 157933. IEEE. https://doi.org/10.1109/ssci44817.2019.9002885FS2RNN: Feature Selection Scheme for Web Spam Detection Using Recurrent Neural Networks
Makkar, A., Obaidat, M.S. and Kumar, N. 2018. FS2RNN: Feature Selection Scheme for Web Spam Detection Using Recurrent Neural Networks. 2018 IEEE Global Communications Conference, GLOBECOM 2018; Abu Dhabi National Exhibition Centre (ADNEC) Abu Dhabi; United Arab Emirates; 9 December 2018 through 13 December 2018; Category number CFP18GLO-ART; Code 145422. IEEE. https://doi.org/10.1109/glocom.2018.8647294User behavior analysis-based smart energy management for webpage ranking: Learning automata-based solution
Makkar, A. and Kumar, N. 2018. User behavior analysis-based smart energy management for webpage ranking: Learning automata-based solution. Sustainable Computing: Informatics and Systems. Vol 20 (Dec 2018), pp. 174-191. https://doi.org/10.1016/j.suscom.2018.02.003Cognitive spammer: A Framework for PageRank analysis with Split by Over-sampling and Train by Under-fitting
Makkar, A. and Kumar, N. 2018. Cognitive spammer: A Framework for PageRank analysis with Split by Over-sampling and Train by Under-fitting. Future Generation Computer Systems. 90 (January 2019), pp. 381-404. https://doi.org/10.1016/j.future.2018.07.046QAIR: Quality Assessment Scheme for Information Retrieval in IoT Infrastructures
Makkar, A., Kumar, N., Obaidat, M.S. and Hsiao, K.-F. 2018. QAIR: Quality Assessment Scheme for Information Retrieval in IoT Infrastructures. 2018 IEEE Global Communications Conference. Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/glocom.2018.8647180Spammer classification using ensemble methods over content-based features
Makkar, A. and Goel, S. Kusum Deep, Jagdish Chand Bansal, Kedar Nath Das, Arvind Kumar Lal, Harish Garg, Atulya K. Nagar and Millie Pant (ed.) 2017. Spammer classification using ensemble methods over content-based features. Springer Verlag.Enhancing robotics learning using imitation learning through visual-based behaviour cloning
Jadeja, Y., Shafik, M., Wood, P. and Makkar, A. 2024. Enhancing robotics learning using imitation learning through visual-based behaviour cloning. 21st International Conference on Manufacturing Research. EDP Sciences. https://doi.org/10.1051/matecconf/202440112006Various Active Learning Strategies Analysis in Image Labeling: Maximizing Performance with Minimum Labeled Data
Tyagi, A., Aditya, H., Shelke, N., Khandelwal, R., Singh, J., Jadeja, Y. and Turukmane, A. 2024. Various Active Learning Strategies Analysis in Image Labeling: Maximizing Performance with Minimum Labeled Data. in: Santosh, K. C., Makkar, A., Conway, M., Singh, A. K., Vacavant, A., Abou el Kalam, A., Bouguelia, M. and Hegadi, R. (ed.) Recent Trends in Image Processing and Pattern Recognition Switzerland Springer. pp. 178-188Preserving Accuracy in Federated Learning via Equitable Model and Efficient Aggregation
Mehdi, M., Makkar, A., Conway, M. and Sama. L 2024. Preserving Accuracy in Federated Learning via Equitable Model and Efficient Aggregation. International Conference on Recent Trends in Image Processing and Pattern Recognition. Springer Nature. https://doi.org/10.1007/978-3-031-53082-1_7Exploring Imaging Biomarkers for Early Detection of Alzheimer’s Disease Using Deep Learning: A Comprehensive Analysis
Sami, N., Makkar, A., Meziane, F. and Conway, M. 2024. Exploring Imaging Biomarkers for Early Detection of Alzheimer’s Disease Using Deep Learning: A Comprehensive Analysis. International Conference on Recent Trends in Image Processing and Pattern Recognition. Springer. https://doi.org/10.1007/978-3-031-53085-2_17Advancements in enhancing cyber-physical system security: Practical deep learning solutions for network traffic classification and integration with security technologies
Gaba, S., Budhiraja, S., Kumar, V. and Makkar, A. Advancements in enhancing cyber-physical system security: Practical deep learning solutions for network traffic classification and integration with security technologies. Communications in Analysis and Mechanics. 21 (1), pp. 1527-155. https://doi.org/10.3934/mbe.2024066SecureFed: federated learning empowered medical imaging technique to analyze lung abnormalities in chest X‑rays
Makkar, A. and Santosh, K. C. 2023. SecureFed: federated learning empowered medical imaging technique to analyze lung abnormalities in chest X‑rays. International Journal of Machine Learning and Cybernetics. 14, p. 2659–2670. https://doi.org/10.1007/s13042-023-01789-7Quantum Machine Learning Driven Malicious User Prediction for Cloud Network Communications
Gupta, R., Saxena. R., Gupta, I., Makkar, A. and Sing, A. K. 2022. Quantum Machine Learning Driven Malicious User Prediction for Cloud Network Communications. IEEE Networking Letters . 4 (4), pp. 174-178. https://doi.org/https://doi.org/10.1109/LNET.2022.3200724996
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