Mhamad Bakro

I received a B.Sc. degree in Informatics Engineering from the Faculty of Informatics Engineering at Aleppo University in Aleppo, Syria, in 2018, with a final grade of 82.50%. Subsequently, I earned an M.Tech degree in Computer Science and Engineering from C. V. Raman Global University in Bhubaneswar, India, in 2020, graduating with distinction and a final grade of 85.10%. I am currently pursuing a Ph.D. in Computer Science and Engineering from C.V. Raman Global University in Bhubaneswar, Odisha, India. Over the course of my academic career, I have published at three conferences, contributed to a book chapter, and authored two journal articles. In addition, I serve as a reviewer for several academic journals and conferences. My current research interests include AI for Cybersecurity, Cloud Computing, Intrusion Detection Systems, Machine Learning, Deep Learning, Optimization Algorithms, IOT, Cryptography, and Blockchain.

EDUCATION

PhD Scholar in Computer Science and Engineering
C. V. Raman Global University, India [ 11/11/2020 – Expected to be completed by the end of 2023 ]
Dissertation: Enhancing Cloud Security: Designing and Developing Advanced Intrusion Detection Systems through Feature Selection and AI Approaches (ML & DL)

Master of Technology in Computer Science and Engineering
C. V. Raman Global University, India [ 10/10/2018 – 06/07/2020 ]
Final grade: 85.10%
Thesis: Hybrid Blockchain-Enabled Security in Cloud Storage Infrastructure with using ECC and AES Algorithms

Bachelor of Science in Informatics Engineering
Faculty of Informatics Engineering-Aleppo University, Syria [ 14/09/2012 – 09/05/2018 ]
Final grade: 82.50%
Thesis: Robot to Help People with Cerebral Paralysis

General Certificate of Secondary Education (Scientific Branch)
Homat Al Diyar school, Education Dept. in Aleppo, Ministry of Education in Syria [ 2012 ]
Final grade: 92.41%

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Networks and Communications, Artificial Intelligence, Computer Science, Computer Engineering

Scopus Publications

• Designing a System Using Features Selection Based on Filter Methods and XGBoost Classifier for Detecting Intrusion in Cloud
  Mhamad Bakro, Rakesh Ranjan Kumar, Sukant Kishoro Bisoy, Mohammad Osama Addas, Dania Khamis
  Lecture Notes in Networks and Systems, 2024
• Building a Cloud-IDS by Hybrid Bio-Inspired Feature Selection Algorithms Along With Random Forest Model
  Mhamad Bakro, Rakesh Ranjan Kumar, Mohammad Husain, Zubair Ashraf, Arshad Ali, et al.
  IEEE Access, 2024
  The adoption of cloud computing has become increasingly widespread across various domains. However, the inherent security vulnerabilities of cloud computing pose significant risks to its overall safety. Consequently, intrusion detection systems (IDS) play a pivotal role in identifying malicious activities within a cloud system. The considerable volume of network traffic data may contain redundant and irrelevant features that can impact the classification performance of the classifier. In addition, the complexity and time consumption increase while processing such a substantial volume of data in the cloud intrusion detection process. To enhance the performance of the IDS, this study proposes a hybrid feature selection approach, combining two bio-inspired algorithms, namely the grasshopper optimization algorithm (GOA) and the genetic algorithm (GA). The combination of these two algorithms ensures a more efficient search for optimal solutions. A random forest (RF) classifier is trained using those optimal features. Moreover, the proposal addresses the challenge of imbalanced data by employing a hybrid approach: over-sampling the minority classes using an adaptive synthetic (ADASYN) algorithm, while implementing random under-sampling (RUS) for the majority class as needed. This integrated strategy significantly influences each category, enhancing the true positive rate (TPR) while minimizing the false positive rate (FPR), thus improving the overall system performance. The proposed approach was evaluated using three datasets: UNSW-NB15, CIC-DDoS2019, and CIC Bell DNS EXF 2021. The recorded accuracies for these datasets were 98%, 99%, and 92%, respectively. The hybrid feature selection-based IDS demonstrated superior performance in multi-class classification, along with exemplary results for individual classes within the datasets. The proposed strategy exhibited a marked superiority with the random forest classifier, especially when compared to other classifiers including SVM, LR, FLN, LSTM, AlexNet, DNN, DBN, DT, and XGBoost. Moreover, this performance remained consistent and commendable even when benchmarked against contemporary state-of-the-art methodologies across multiple evaluation metrics.
• Efficient Intrusion Detection System in the Cloud Using Fusion Feature Selection Approaches and an Ensemble Classifier
  Mhamad Bakro, Rakesh Ranjan Kumar, Amerah A. Alabrah, Zubair Ashraf, Sukant K. Bisoy, et al.
  Electronics Switzerland, 2023
  The application of cloud computing has increased tremendously in both public and private organizations. However, attacks on cloud computing pose a serious threat to confidentiality and data integrity. Therefore, there is a need for a proper mechanism for detecting cloud intrusions. In this paper, we have proposed a cloud intrusion detection system (IDS) that is focused on boosting the classification accuracy by improving feature selection and weighing the ensemble model with the crow search algorithm (CSA). The feature selection is handled by combining both filter and automated models to obtain improved feature sets. The ensemble classifier is made up of machine and deep learning models such as long short-term memory (LSTM), support vector machine (SVM), XGBoost, and a fast learning network (FLN). The proposed ensemble model’s weights are generated with the CSA to obtain better prediction results. Experiments are executed on the NSL-KDD, Kyoto, and CSE-CIC-IDS-2018 datasets. The simulation shows that the suggested system attained more satisfactory results in terms of accuracy, recall, precision, and F-measure than conventional approaches. The detection rate and false alarm rate (FAR) of different attack types was more efficient for each dataset. The classifiers’ performances were also compared individually to the ensemble model in terms of the false positive rate (FPR) and false negative rate (FNR) to demonstrate the ensemble model’s robustness.
• An Improved Design for a Cloud Intrusion Detection System Using Hybrid Features Selection Approach With ML Classifier
  Mhamad Bakro, Rakesh Ranjan Kumar, Amerah Alabrah, Zubair Ashraf, Md Nadeem Ahmed, et al.
  IEEE Access, 2023
  The focus of cloud computing nowadays has been reshaping the digital epoch, in which clients now face serious concerns about the security and privacy of their data hosted in the cloud, as well as increasingly sophisticated and frequent cyberattacks. Therefore, it has become imperative for both individuals and organizations to implement a robust intrusion detection system (IDS) capable of monitoring packets in the network, distinguishing between benign and malicious behavior, and detecting the type of attacks. IDS based on ML are efficient and precise in spotting network threats. Yet, for large dimensional data sizes, the performance of these systems decreases. Thus, it is critical to building a suitable feature selection approach that selects necessary features without having an impact on the classification process or causing information loss. Furthermore, training ML models on unbalanced datasets show a rising false positive rate (FPR) and a lowering detection rate (DR). In this paper, we present an improved cloud IDS designed by incorporating the synthetic minority over-sampling technique (SMOTE) to address the imbalanced data issue, and for feature selection, we propose to use a hybrid approach that includes three techniques: information gain (IG), chi-square (CS), and particle swarm optimization (PSO). Finally, the random forest (RF) model is utilized for detecting and classifying various types of attacks. The suggested system has been verified by the UNSW-NB15 and Kyoto datasets, achieving accuracies of over 98% and 99% in the multi-class classification scenario, respectively. It was noticed that an intrusion detection system with fewer informative features would operate more effectively. The simulation results significantly outperform other methodologies proposed in the related work in terms of different evaluation metrics.
• Hybrid Blockchain-Enabled Security in Cloud Storage Infrastructure Using ECC and AES Algorithms
  Mhamad Bakro, Sukant K. Bisoy, Ashok K. Patel, M. Adib Naal
  Lecture Notes on Data Engineering and Communications Technologies, 2022
• Developing a Cloud Intrusion Detection System with Filter-Based Features Selection Techniques and SVM Classifier
  Mhamad Bakro, Rakesh Ranjan Kumar, Sukant K. Bisoy, Mohammad Osama Addas, Dania Khamis
  Communications in Computer and Information Science, 2022
• Condition monitoring and fault diagnosis of induction motor - An experimental analysis
  Abdelelah Almounajjed, Ashwin Kumar Sahoo, Mani Kant Kumar, Mhamad Waleed Bakro
  Proceedings of the 7th International Conference on Electrical Energy Systems Icees 2021, 2021
  Continuous work of Induction Motor (IM) is an exigency for the recent industries which without a doubt influences the reliability and stability of the production process. Detecting the faults at an early stage can reduce the loss in time and expenses related to sudden stop of the motor. Consequently, condition monitoring is considered the first proposition in this domain to detect the initial faults in the machine. In this paper, we detail the most frequent failures in the IM and the condition monitoring techniques used to detect these faults. Furthermore, experimental tests are done on many common external faults of IM, and a comprehensive comparative study among more than 10 faults is done for a better comprehension of the machine behavior during the faults.
• Performance Analysis of Cloud Computing Encryption Algorithms
  Mhamad Bakro, Sukant K. Bisoy, Ashok K. Patel, M. Adib Naal
  Lecture Notes in Networks and Systems, 2021

RECENT SCHOLAR PUBLICATIONS

• Real-Time Intrusion Detection in IIoT/OT Networks Via Traffic-Feature Forecasting: A Testbed Study
  M Bakro, A Marotta, W Tiberti, O Odoardi, I Salvatore, P Di Marco
  2026 IEEE 22nd International Conference on Factory Communication Systems … , 2026
  2026
• Building a cloud-IDS by hybrid bio-inspired feature selection algorithms along with random forest model
  M Bakro, RR Kumar, M Husain, Z Ashraf, A Ali, SI Yaqoob, MN Ahmed, ...
  Ieee Access 12, 8846-8874 , 2024
  2024
  Citations: 107
• An Improved Design for a Cloud Intrusion Detection System Using Hybrid Features Selection Approach With ML Classifier.
  M Bakro, RR Kumar, AA Alabrah, Z Ashraf, MN Ahmed, M Shameem, ...
  IEEe Access 11 (2023), 64228-64247 , 2023
  2023
  Citations: 102
• Efficient intrusion detection system in the cloud using fusion feature selection approaches and an ensemble classifier
  M Bakro, RR Kumar, AA Alabrah, Z Ashraf, SK Bisoy, N Parveen, ...
  Electronics 12 (11), 2427 , 2023
  2023
  Citations: 64
• Developing a Cloud Intrusion Detection System with Filter-Based Features Selection Techniques and SVM Classifier
  M Bakro, RR Kumar, SK Bisoy, MO Addas, D Khamis
  International Conference on Computing, Communication and Learning, 15-26 , 2023
  2023
  Citations: 9
• Designing a System Using Features Selection Based on Filter Methods and XGBoost Classifier for Detecting Intrusion in Cloud
  M Bakro, RR Kumar, SK Bisoy, MO Addas, D Khamis
  International Conference on Advanced Computing and Intelligent Engineering … , 2022
  2022
• Hybrid Blockchain-Enabled Security in Cloud Storage Infrastructure Using ECC and AES Algorithms
  Mhamad Bakro, Sukant K. Bisoy, Ashok K. Patel, M. Adib Naal
  Blockchain based Internet of Things 112, pp 139-170 , 2022
  2022
  Citations: 16
• Performance analysis of cloud computing encryption algorithms
  M Bakro, SK Bisoy, AK Patel, MA Naal
  Advances in Intelligent Computing and Communication: Proceedings of ICAC … , 2021
  2021
  Citations: 17
• Condition monitoring and fault diagnosis of induction motor-an experimental analysis
  A Almounajjed, AK Sahoo, MK Kumar, MW Bakro
  2021 7th International Conference on Electrical Energy Systems (ICEES), 433-438 , 2021
  2021
  Citations: 16

MOST CITED SCHOLAR PUBLICATIONS

• Building a cloud-IDS by hybrid bio-inspired feature selection algorithms along with random forest model
  M Bakro, RR Kumar, M Husain, Z Ashraf, A Ali, SI Yaqoob, MN Ahmed, ...
  Ieee Access 12, 8846-8874 , 2024
  2024
  Citations: 107
• An Improved Design for a Cloud Intrusion Detection System Using Hybrid Features Selection Approach With ML Classifier.
  M Bakro, RR Kumar, AA Alabrah, Z Ashraf, MN Ahmed, M Shameem, ...
  IEEe Access 11 (2023), 64228-64247 , 2023
  2023
  Citations: 102
• Efficient intrusion detection system in the cloud using fusion feature selection approaches and an ensemble classifier
  M Bakro, RR Kumar, AA Alabrah, Z Ashraf, SK Bisoy, N Parveen, ...
  Electronics 12 (11), 2427 , 2023
  2023
  Citations: 64
• Performance analysis of cloud computing encryption algorithms
  M Bakro, SK Bisoy, AK Patel, MA Naal
  Advances in Intelligent Computing and Communication: Proceedings of ICAC … , 2021
  2021
  Citations: 17
• Hybrid Blockchain-Enabled Security in Cloud Storage Infrastructure Using ECC and AES Algorithms
  Mhamad Bakro, Sukant K. Bisoy, Ashok K. Patel, M. Adib Naal
  Blockchain based Internet of Things 112, pp 139-170 , 2022
  2022
  Citations: 16
• Condition monitoring and fault diagnosis of induction motor-an experimental analysis
  A Almounajjed, AK Sahoo, MK Kumar, MW Bakro
  2021 7th International Conference on Electrical Energy Systems (ICEES), 433-438 , 2021
  2021
  Citations: 16
• Developing a Cloud Intrusion Detection System with Filter-Based Features Selection Techniques and SVM Classifier
  M Bakro, RR Kumar, SK Bisoy, MO Addas, D Khamis
  International Conference on Computing, Communication and Learning, 15-26 , 2023
  2023
  Citations: 9
• Real-Time Intrusion Detection in IIoT/OT Networks Via Traffic-Feature Forecasting: A Testbed Study
  M Bakro, A Marotta, W Tiberti, O Odoardi, I Salvatore, P Di Marco
  2026 IEEE 22nd International Conference on Factory Communication Systems … , 2026
  2026
• Designing a System Using Features Selection Based on Filter Methods and XGBoost Classifier for Detecting Intrusion in Cloud
  M Bakro, RR Kumar, SK Bisoy, MO Addas, D Khamis
  International Conference on Advanced Computing and Intelligent Engineering … , 2022
  2022