Muhammad Usman Sana
Designation : Assistant Professor
Specialization : Security Information system and Engineering
Office Exten : 109
Specialization : Security Information system and Engineering
Email : [email protected]
Office Number : +(92) 092533649975Office Exten : 109
Muhammad Usman Sana completed his Ph.D. degree from Xian University of Science and Technology, China in 2023 with major in Security Information System and engineering. He received his M.S. in Communication Engineering from Chalmers University of Technology, Sweden in 2010 and B.E. degree in Information Technology from the University of Engineering and Technology Taxilla, Pakistan in 2006. His research is situated in the field of Technology & Innovation, with a special focus in Cloud Computing, Networks Security, Wireless Sensor Networks, Social Network Analysis, Next Generation Networks, Socially-aware Communication, Routing and Switching. Muhammad Usman Sana teaches several courses such as Data Communications and Networks, Computer Network, Networks Security, System integration and architecture, Advance Cloud Computing, Routing and Switching to bachelors and masters students in IT department. He provides many services to the University as Convener of Departmental QEC, UOG, Member of BOS of IT Department, UOG, M.Phil. program coordinator of IT Department, UOG, Sports coordinator, UOG, Exam coordinator of IT department, UOG and Member of DRC committee of IT department, UOG. Usman is also a keen and responsible reviewer of top international journals indexed by the Web of Science Core Collection. He Worked as Technical Program Committee member of IEEE International Conference on Energy, Power, and Environment (ICEPE-2023).
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His research is situated in the field of Technology & Innovation, with a special focus in Cloud Computing, Networks Security, Wireless Sensor Networks, Social Network Analysis, Next Generation Networks, Socially-aware Communication, Routing and Switching.
- PhD, Other
- Mphil, Other
- BEIT,University of Engineering & Technalogy Texila
- fsc ,Gujranwala Board of Intermediate & Secondary Education
- matric,Gujranwala Board of Intermediate & Secondary Education
- Exam coordinator of IT department, UOG conduct exam and arrange committee meetings
- Sports coordinator, UOG arrange sports events and coordinate with students
- M.Phil. program coordinator of IT Department, UOG coordinate Mphil students about courses selection and manage the courses and deadlines. Also coordinate the meetings.
- Member of BOS of IT Department, UOG design curriculum of BS IT
- Convener of Departmental QEC, UOG update the faculty with QEC rules and requirements
- Member of DRC committee of IT department, UOG evaluation of Mphil students proposal and thesis.
| Student Name | Degree | Title | Status / Completed Year |
|---|---|---|---|
| TAHMINA EHSAN | MS | SECURING SMART CONTRACTS IN FOG COMPUTING MACHINE LEARNING-BASED ATTACK DETECTION FOR REGISTRATION AND RESOURCE ACCESS GRANTING Smart contracts are becoming increasingly popular for managing transactions or activities infog computing environments. However, the use of smart contracts for registration and resourceaccess granting is vulnerable to various types of attacks that can compromise their security.Detecting these attacks can be challenging, as attackers can use sophisticated techniques toevade detection. Machine learning techniques have shown promise in detecting attacks on smart contracts in other contexts, but their effectiveness in fog computing environments has not been fully explored. This research uses a machine learning-based approach for detecting different attacks on smart contracts used for registration and resource access granting in fog computing.This research uses the machine learning approach for attack detection on the smart contract.The solidity, bytecode, and opcode of smart contracts are collected from the online officialwebsite of Ethereum “etherscan.io”. After data collection, the data preprocessing and analysisstep is performed. There are different feature extraction techniques such as count vector, TermFrequency-Inverse Document frequency, Bag of words, n-gram, and hashing vector are usedfor feature extraction from the opcode of the smart contract. Different classifiers are used onextracted features such as Logistic Regression, Decision Tree, Random forest, KNN, BaggingClassifier, Extra Tree, XGBoost, Gradient Boost, Naïve Bayes, and Light Gradient Boostclassifier. Different evaluation matrices are used to evaluate the results of each model such asaccuracy, precision, recall, f1 score, cross-validation, and time cost.The results show that the Extra tree classifier provides high accuracy 86% with 8s in the countvector feature extraction technique. The Extra tree classifier provides the highest accuracy 84%with 6s in the TF-IDF technique. The XGBoost classifier provides the highest accuracy 86%with 105s in the Bag of Words technique. The Extra tree classifier provides the highest accuracy86% with 21s in the n-gram technique. The Extra tree classifier provides the highest accuracy86% with 9s in the hashing vector technique.The accuracy comparison of all models in each technique is equal except the TF-IDF technique,but it takes less time for prediction as compared to other techniques. | 2023 |
| MUSTABEEN AZIZ | MS | FOG-BASED DDOS ATTACK DETECTION IN BLOCKCHAIN-ENABLED IOT NETWROKING USING MACHINE LEARNING With the advancement of technology, IoT devices are increasing day by day. IoT is based oncentralized storage architecture. A centralized storage system has some challenges like failure ofnodes, data security, and privacy. Blockchain technology emerged with IOT applications andsolved IoT security issues. Blockchain provides a decentralized platform to store IOT data. ButBlockchain faces IOT attacks like DDOS attacks. IoT produces a large amount of data. IoT attackscan affect the performance of Blockchain. In this to detect DDOS attacks in distributed fog nodesagainst the consensus mechanism in Hyperledger Blockchain. Intrusion detection system isintegrated with distributed fog nodes for attacks detection. This model is evaluated by using anIOT-based dataset. The CICDDOS2019 dataset is based on DDOS attacks. We use this dataset forintrusion detection as reflection-based attacks and exploitation-based attacks. To use Pearson’sfeature selection technique to get most important features and PCA feature reduction technique to reduce dimensions on Reflection-based attacks and Exploitation-based attacks. And use ensemble-learning models namely, stacking, bagging, and boosting, and Machine Learning models namely Extra Tree, KNN, Decision Tree, and Random Forest for intrusion detection. We proposed thesemodels to evaluate the performance of multi-attacks on exploitation-based attacks and reflection-based attacks. | 2023 |
- 1. Noor Fatima, Muhammad Usman Sana , Naveed Anwer Butt , Nagwan Abdel Samee , Mona M. Jamjoom , Imran Ashraf “Combating Modal Aliasing in MPTCP Anomaly Detection with Ensemble Empirical Mode Decomposition Method” Journal of Cloud Computing-Advances Systems and Applications, May 2025 DOI:
- 2. Hina Rashid , Hannan Bin Liaqat , Muhammad Usman Sana , Tayybah Kiren, Hanen Karamti, Imran Ashraf “Framework for Detecting Phishing Crimes on Twitter using selective Features and Machine Learning” Computers & Electrical Engineering, April 2025 DOI:
- 3. Mustabeen Aziz, Muhammad Usman Sana, Tayybah Kiren, Tahmina Ehsan, Alvena Ehsan, Fateha Minahil “DETECTION OF APPLICATION-LAYER DoS ATTACKS IN IOT DEVICES USING FEATURE SELECTION AND MACHINE LEARNING MODELS” International Journal of Innovations in Science & Technology, March 2025 DOI:
- 4. Tahmina Ehsan, Muhammad Usman Sana, Tayybah Kiren, Alvena Ehsan, MustabeenAziz, Fateha Minahil “Machine Learning-Based Improvement of Smart ContractSecurity in Fog Computing Using Word2vec and Bert” International Journal of Innovations in Science & Technology, March 2025 DOI:
- 5. Fiaz Majeed , Syed Ali Ghalib , Umair Shafique , Muhammad Usman Sana , Nagwan Abdel Samee, Mona M. Jamjoom , Imran Ashraf “Improving Detection of Tomato Diseases Using Feature Fusion and long short-Term Memory- Based Hybrid Model” Bmc Plant Biology, February 2025 DOI:
- 6. Tahmina Eshan, Muhammad Usman Sana, Alvena Ehsan, Mustabeen Aziz , Tahir khurshaid, Imran Ashraf “Enhanced Security of Smart Contracts in Fog Computing: Hybrid Classifiers and Feature Selection for Improved Attack Detection for Registration and Resource Access Granting” Cluster Computing-The Journal Of Networks Software Tools And Applications, January 2025 DOI:
- 7. Muhammad Wahab Hanif, Zhenhua Yu, Rehmat Bashir, Zhanli Li, Sardar Annes Farooq, Muhammad Usman Sana “A new network model for multiple object detection for autonomous vehicle detection in mining environment” Iet Image Processing, October 2024 DOI:
- 8. Maryam Aslam, Muhammad Usman Sana, Tayybah Kiren, Muhammad Jehanzeb Irshad “Classification of Apple Plant Leaf Diseases Using Deep Convolutional Neural Network” Data Science and Engineering, June 2024 DOI:
- 9. Tahmina Ehsan, Muhammad Usman Sana, Muhammad Usman Ali, Elizabeth Caro Montero, Eduardo Silva Alvarado, Sirojiddin Djuraev, Imran Ashraf “Securing Smart Contracts in Fog Computing: Machine Learning-Based Attack Detection for Registration and Resource Access Granting” Ieee Access, March 2024 DOI:
- 10. Ayesha Jabbar, Hannan Bin Liaqat, Aftab Akram, Muhammad Usman Sana, Irma Domínguez Azpíroz, Isabel De La Torre Diez, Imran Ashraf “A Lesion-Based Diabetic Retinopathy Detection Through Hybrid Deep Learning Model” Ieee Access, March 2024 DOI:
- 11. Mustabeen Aziz , Muhammad Usman Sana , Muhammad Usman Ali, Manuel Masias Vergara , jon Arambarri , Imran Ashraf. “Fog-Based DDoS Attack Detection in Blockchain-enabled Internet of Things” Multimedia Tools And Applications, November 2023 DOI:
- 12. Turrnum Shahzadi, Muhammad Usman Ali, Fiaz Majeed, Muhammad Usman Sana, Raquel Martínez Diaz, Md Abdus Samad, Imran Ashraf “Nerve Root Compression Analysis to Find Lumbar Spine Stenosis on MRI Using CNN” Diagnostics, September 2023 DOI:
- 13. Samra Shahzadi, Naveed Anwer Butt, Muhammad Usman Sana, Iñaki Elío Pascual, Mercedes Briones Urbano, Isabel de la Torre Díez, Imran Ashraf “Voxel Extraction and Multiclass Classification of Identified Brain Regions across Various Stages of Alzheimer’s Disease Using Machine Learning Approaches” Diagnostics, September 2023 DOI:
- 14. Muhammad Usman Sana, Zhanli Li, Tayybah Kiren, Hannan Bin Liaqat, Shahid Naseem, Atif Saeed “A Secure Method for Data Storage and Transmission in Sustainable Cloud Computing” Cmc-Computers Materials & Continua, March 2023 DOI:
- 15. Naveed Anwer Butt, Zafar Mahmood, Muhammad Usman Sana, Isabel de la Torre Díez, Juan Castanedo Galán, Santiago Brie, Imran Ashraf “Behavioral and Performance Analysis of a Real-Time Case Study Event Log: A Process Mining Approach” Applied Sciences-Basel, March 2023 DOI:
- 16. Muhammad Umer, Saima Sadiq, Michele Nappi, Muhammad Usman Sana, Imran Ashraf “ETCNN: Extra Tree and Convolutional Neural Network-based Ensemble Model for COVID-19 Tweets Sentiment Classification.” Pattern Recognition Letters, November 2022 DOI:
- 17. Tahira Sarwar Mir, Hannan Bin Liaqat, Tayybah Kiren, Muhammad Usman Sana, Roberto Marcelo Alvarez, Yini Miró, Alina Eugenia Pascual Barrera, Imran Ashraf “Antifragile and Resilient Geographical Information System Service Delivery in Fog Computing. ” Sensors, November 2022 DOI:
- 18. Fawad Javaid, Anyi Wang, Muhammad Usman Sana, Hafiz Zameer ul Hassan, Imran Ashraf “A dual channel and node mobility based cognitive approach to optimize wireless networks in coal mines” Journal of King Saud University-Computer and Information Sciences, April 2022 DOI:
- 19. Muhammad Usman Sana, Zhanli Li, Fawad Javaid, Hannan Bin Liaqat, Muhammad Usman Ali “Enhanced Security in Cloud Computing Using Neural Network and Encryption” Ieee Access, October 2021 DOI:
- 20. Fawad Javaid, Anyi Wang, Muhammad Usman Sana, Asif Husain, Imran Ashraf “An optimized approach to channel modeling and impact of deteriorating factors on wireless communication in underground mines” Sensors, September 2021 DOI:
- 21. Muhammad Usman Sana, Zhanli Li, Fawad Javaid, Muhammad Wahab Hanif, Imran Ashraf “Improved particle swarm optimization based on blockchain mechanism for flexible job shop problem.” Cluster Computing-The Journal Of Networks Software Tools And Applications, July 2021 DOI:
- 22. Muhammad Usman Sana, Zhanli Li “Efficiency aware scheduling techniques in cloud computing: a descriptive literature review” Peerj, May 2021 DOI:
- 23. Fawad Javaid, Anyi Wang, Muhammad Usman Sana, Asif Husain, Imran Ashraf “Characteristic study of visible light communication and influence of coal dust particles in underground coal mines” Electronics, April 2021 DOI:
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