Abhishek Kumar
@cuchd.in
Assistant Director
Chandigarh University
Dr. Abhishek Kumar is currently working as an Assistant director /Associate professor in Computer science & Engineering Department in Chandigarh University, Punjab, India .He is Doctorate in computer science from University of Madras and is doing Post-Doctoral Fellow in Ingenium Research Group Ingenium Research Group Lab, Universidad De Castilla-La Mancha, Ciudad Real, and Ciudad Real Spain. He has done M.Tech in Computer Sci. & Engineering and B.Tech in I.T. from, Rajasthan Technical University, Kota India. He has total Academic teaching experience of more than 11 years along with 2 years teaching assistantship. He is having more than 100 publications in reputed, peer reviewed National and International Journals, books & Conferences He has guided more than 30 M.Tech Projects at national and International level and guiding 6 PhD Scholar. His research area includes- Artificial intelligence, Renewable Energy Image processing, Computer Vision, Data Mining, Machine Learning. He has been Se
RESEARCH, TEACHING, or OTHER INTERESTS
Artificial Intelligence, Engineering, Health Information Management, Energy
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Benkhaddra Ilyas, Abhishek Kumar, Setitra Mohamed Ali, and Hang Lei
Springer Science and Business Media LLC
Mayank Gautam, Sachin Ahuja, and Abhishek Kumar
IEEE
With many gaps and shortcomings in the field of IoT security, no matter how simple you think it is on your side what goes inside an intrusion detection system technique to detect attacks. This study explores the current state of IDS in IoT. The most important points of vulnerability are identified, and we examine ways to address them immediately. One key finding of the research is that interoperability problems between differing IoT devices and platforms loom as two big roadblocks. Besides, the lack of standardized evaluation standards or sets for intrusion detection models in IoT environments turns out to be an important gap between what is being researched currently and reality. The ambiguity of how to upscale in large IoT networks is cited as an issue left unresolved by the study. In addition, it points toward the crucial role intrusion detection plays in safeguarding IoT systems and also reveals existing areas of weakness with regard to adaptability, scalability and standardization..
Shakshi Kattna, Abhishek Kumar, and Sachin Ahuja
IEEE
Cloud computing has become a revolution of modern computer paradigms, offering adaptable resources to the utmost. But thread operations inside of cloud environments still fall under the critical challenge for efficient processing and resource utilization. Integration of blockchain technology would be a new, transformative opportunity for optimizing threads. Straddling the realms of blockchain and cloud infrastructure, this paper discusses optimizing threads and examines how distributing a program can lead to better computational efficiency using blockchain decentralized ledger system. In optimizing threads, the study is concerned with scalability of cloud environments, consensus overhead and interoperability. In addition, it introduces various new technologies for overcoming these problems-including parallel processing and enhancements of consensus protocols as well load balancing techniques.
S. Pradeep, Yogesh Kumar Sharma, Umesh Kumar Lilhore, Sarita Simaiya, Abhishek Kumar, Sachin Ahuja, Martin Margala, Prasun Chakrabarti, and Tulika Chakrabarti
Springer Science and Business Media LLC
AbstractSoftware-defined networking (SDN) has significantly transformed the field of network management through the consolidation of control and provision of enhanced adaptability. However, this paradigm shift has concurrently presented novel security concerns. The preservation of service path integrity holds significant importance within SDN environments due to the potential for malevolent entities to exploit network flows, resulting in a range of security breaches. This research paper introduces a model called "EnsureS", which aims to enhance the security of SDN by proposing an efficient and secure service path validation approach. The proposed approach utilizes a Lightweight Service Path Validation using Batch Hashing and Tag Verification, focusing on improving service path validation's efficiency and security in SDN environments. The proposed EnsureS system utilizes two primary techniques in order to validate service pathways efficiently. Firstly, the method utilizes batch hashing in order to minimize computational overhead. The proposed EnsureS algorithm enhances performance by aggregating packets through batches rather than independently; the hashing process takes place on each one in the service pathway. Additionally, the implementation of tag verification enables network devices to efficiently verify the authenticity of packets by leveraging pre-established trust relationships. EnsureS provides a streamlined and effective approach for validating service paths in SDN environments by integrating these methodologies. In order to assess the efficacy of the Proposed EnsureS, a comprehensive series of investigations were conducted within a simulated SDN circumstance. The efficacy of Proposed EnsureS was then compared to that of established methods. The findings of our study indicate that the proposed EnsureS solution effectively minimizes computational overhead without compromising on the established security standards. The implementation successfully reduces the impact of different types of attacks, such as route alteration and packet spoofing, increasing SDN networks' general integrity.
Rahul Kumar, Shweta Singh, Shweta Chauhan, Abhineet Anand, and Abhishek Kumar
Elsevier BV
Osama Bassam J. Rabie, Shitharth Selvarajan, Daniyal Alghazzawi, Abhishek Kumar, Syed Hasan, and Muhammad Zubair Asghar
Institution of Engineering and Technology (IET)
AbstractDetection of cyber‐threats in the smart grid Supervisory Control and Data Acquisition (SCADA) is still remains one of the complex and essential processes need to be highly concentrated in present times. Typically, the SCADA is more prone to the security issues due to their environmental problems and vulnerabilities. Therefore, the proposed work intends to design a new detection approach by integrating the optimization and classification models for smart grid SCADA security. In this framework, the min‐max normalization is performed at first for noise removal and attributes arrangement. Here, the correlation estimation mechanism is mainly deployed to reduce the dimensionality of features by choosing the relevant features used for attack prediction. Moreover, the optimal features are selected by using the optimal solution provided by the Holistic Harris Hawks Optimization (H3O). Finally, the Perceptron Stochastic Neural Network (PSNN) is utilized to categorize the normal and attacking data flow in the network with minimal processing time and complexity. By using the combination of proposed H3O‐PSNN technique, the detection accuracy is improved up to 99% for all datasets used in this study, and also other measures such as precision to 99.2%, recall to 99%, f1‐score to 99.2% increased, when compared to the standard techniques.
Abhishek Kumar, Pramod Sing Rathore, Ashutosh Kumar Dubey, Rashmi Agrawal, and Kanta Prasad Sharma
Springer Science and Business Media LLC
Abhishek Kumar, Pramod Sing Rathore, Ashutosh Kumar Dubey, Rashmi Agrawal, and Kanta Prasad Sharma
Springer Science and Business Media LLC
Abhishek Kumar, Swarn Avinash Kumar, Vishal Dutt, S. Shitharth, and Esha Tripathi
Wiley
Ilyas Benkhaddra, Abhishek Kumar, Mohamed Ali Setitra, and Lei Hang
Springer Science and Business Media LLC
Arun Lal Srivastav, Markandeya, Naveen Patel, Mayank Pandey, Ashutosh Kumar Pandey, Ashutosh Kumar Dubey, Abhishek Kumar, Abhishek Kumar Bhardwaj, and Vinod Kumar Chaudhary
Springer Science and Business Media LLC
Benkhaddra Ilyas, Abhishek Kumar, Mohamed Ali Setitra, ZineEl Abidine Bensalem, and Hang Lei
Wiley
The attack named Distributed Denial of Service (DDoS) that takes place in the large blockchain network requires an efficient and robust attack detection and prevention mechanism for authenticated access. Blockchain is a distributed network in which the attacker tries to hack the network by utilizing all the resources with the application of enormous requests. Several methods like Rival Technique, filter modular approach and so on, were developed to detect and prevent the DDoS attack in the blockchain; still, detection accuracy is a challenging task. Hence, this research introduces an efficient technique using optimization‐based deep learning by considering the blockchain network and smart contract for the detection and prevention of DDoS attacks. Based on the user request, the traffic is analyzed, and the verification using the smart contract is made to find the authenticated user. After the verification, the response is provided for the authenticated user, and the suspicious traffic is utilized for the detection of DDoS attacks using the Poaching Raptor Optimization‐based deep neural network (Poaching Raptor‐based DNN), in which the classifier is tuned using the proposed optimization algorithm to reduce the training loss. The proposed algorithm is designed by hybridizing the habitual practice of the raptor by considering the concurring behavior, hunting style along with poaching behavior of the Lobo to enhance the detection accuracy. After the attack detection, the nonattacker is responded, and the attacker is prevented by entering the IP/MAC address in the logfile. The performance of the proposed method is evaluated in terms of recall, precision, FPR, and accuracy and obtained the values of 96.3%, 98.22%, 3.33%, and 95.12%, respectively.
Abhiudey Kabotra, Anupam Baliyan, and Abhishek Kumar
IEEE
Parkinson's disease (PD) is a neurological condition marked by a gradual decline in mental and motor skills. For prompt intervention and better patient care, a rapid and correct diagnosis of PD is essential. Machine learning (ML) has become a potent technique for the early identification and diagnosis of Parkinson's disease in recent years. The state-of-theart ML methods utilized for PD detection are thoroughly reviewed in this review paper, with an emphasis on their advantages, disadvantages, and possible uses.
Mamta, Ajish Mangot, Abhishek Kumar, and Anupam Baliyan
IEEE
Accurate solar power forecasting is critical for efficient energy management and grid integration. This study looks into how to forecast solar power using weather forecast data and machine learning methods. Several machine learning models have been proposed to forecast solar power, including Artificial Neural Networks, Random Forests, Support Vector Regressions, Gradient Boosting, and Hybrid AI models. However, the proposed Deep Belief Networks (DBNs) model has several advantages over conventional machine learning models, including its capacity to learn intricate patterns and relationships in the data, lowering the requirement for manual feature engineering, and scalability for both short- and long-term forecasting. The comparison table and accuracy metrics show that the suggested model performs better in terms of accuracy than conventional models and takes less time to run. The proposed model provides valuable insights into the optimal design and implementation of solar power forecasting systems, supporting the transition to a sustainable energy future.
Venkata Raghuveer Burugadda, Prashant S. Pawar, Abhishek Kumar, and Neha Bhati
IEEE
Heart failure is a frequent cause of hospitalization and readmission because of the severity of the disease. Researchers explored using Machine Learning (ML) algorithms to forecast whether heart failure patients must be readmitted to the hospital. This study examines ML algorithms that use data from electronic health records to forecast hospital readmissions for patients with heart failure. We will assess the accuracy, precision, recall, and F1-score of logistic regression, decision trees, random forests, Support Vector Machines (SVM), and artificial neural networks. The study's results will show how well ML algorithms predict heart failure patients' hospital readmission risk, which could lead to personalized therapies that improve patient outcomes and save healthcare costs. Comparing studies in this field shows gaps in model interpretability, generalizability, and socioeconomic determinants of health in prediction models.
RECENT SCHOLAR PUBLICATIONS
MOST CITED SCHOLAR PUBLICATIONS
RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)
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