Aaisha Makkar


NameAaisha Makkar
Job titleLecturer in Computer Science
Research instituteCollege of Science and Engineering
ORCIDhttps://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.2021.3053326

SecureEngine: 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.2021.103658

SecureIIoT 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.2022.3149902

Visualization 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.2021.06.032

An 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.2020.2968927

PROTECTOR: 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.2021.06.026

Artificial 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.2021.3066492

FedLearnSP: 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.2020.3048926

Ai 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.2020.05.113

An 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.2020.03.004

DIADL: 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/10.1145/3408066.3408107

The 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/ssci44817.2019.9002885

FS2RNN: 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.2018.8647294

User 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.2018.02.003

Cognitive 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.2018.07.046

QAIR: 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.2018.8647180

Spammer 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.

Advancements 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.2024066

SecureFed: 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/s13042-023-01789-7

Quantum 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.2022.3200724
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