Rahul B Adhao

@mitaoe.ac.in

Assistant Professor at Computer Science and Engineering Data Science
MIT Academy of Engineering Pune

Rahul B Adhao


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https://researchid.co/rba.comp

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Computer Networks and Communications, Artificial Intelligence, Information Systems
14

Scopus Publications

254

Scholar Citations

7

Scholar h-index

7

Scholar i10-index

Scopus Publications

  • A MAGIQ-TOPSIS-Fuzzy-Logic Based Cloud Service Providers Ranking Model for Federated Cloud Computing Environment
    V. SARAF, R. ADHAO, V. PACHGHARE
    Advances in Electrical and Computer Engineering, 2026
    In the era of multi-cloud environments, the challenge for end users or organizations is to select the most appropriate cloud service provider (CSP) due to the complexity and variability of service-level offerings by various CSPs. In this paper, we propose a MAGIQ-TOPSIS-Fuzzy-logic based Cloud Service Provider Ranking (MTF-CSPR) model, which provides a more robust and intelligent decision-making system in CSP selection. The proposed model addresses both qualitative and quantitative evaluation criteria by capturing and processing the inherent vagueness of linguistic assessments through fuzzy logic. MAGIQ is employed to derive optimal weights for evaluation criteria that reflect their relative importance towards the alternatives. TOPSIS is applied to rank CSPs where the ideal and anti-ideal solutions are derived, and then based on CSP's proximity to the ideal solution, the ranks are derived. The proposed model ensures accuracy, scalability, and context-awareness for the CSP ranking by integrating subjective expert opinions with objective performance metrics. Experimental evaluation demonstrates that the proposed MTF-CSPR model provides more consistent and accurate rankings compared to conventional standalone approaches, as well as the other ranking models under consideration, providing better decision-support for enterprises to adopt the hybrid or multi-cloud strategies.
  • A Trust Computation Model for Collaborative Cloud Environment
    Nikhil Kamble, Vinod Saraf, Vinod Pachghare, Rahul Adhao
    Procedia Computer Science, 2025
    Cloud computing has revolutionized the IT industry, with businesses of all sizes, from major corporations to local pizza shops and retail stores, increasingly relying on cloud services. This technology provides on-demand access to various computing resources over the Internet, including databases, development tools, applications, and physical and virtual servers. However, ensuring service uptime and accurately predicting application performance on cloud infrastructure remain significant challenges. Securing applications and data on the cloud while maintaining user privacy adds to these concerns. From the perspective of cloud consumers, issues such as service unavailability when needed, slower-than-expected response times, and the inherent security and privacy risks associated with cloud services can lead to a lack of trust in cloud providers. This research project addresses these concerns by proposing various methods for computing trust in cloud service providers. The trustworthiness evaluation will consider multiple factors, including reputation, service level agreements (SLAs), self-assessments, and cloud audits. Agents responsible for monitoring trust will observe and collect data on the different services offered by cloud providers. This data will be analyzed, and a trust computation engine will assess the provider’s trustworthiness. Key quality of service (QoS) features such as availability, dependability, data integrity, and reliability are crucial for users, as they play a significant role in determining the trustworthiness of a cloud service. The research mainly focuses on calculating trust as a numerical value by considering availability, response time, throughput, and Reliability. Trust will be the percentage value for the cloud. The authors have taken only the above four factors for better trust value.
  • Statistical feature selection based intrusion detection system for internet of things environment
    Rahul B. Adhao, Mayur Tayde, Vinod Pachghare
    Aip Conference Proceedings, 2023
  • Feature Engineering for Flow-Based IDS
    Rahul B. Adhao, Vinod K. Pachghare
    Wireless Communication Security, 2023
    During the last decennium, computer network security has undergone an incredible revolution with the rapid development of high-speed networking technologies. A good example is NetFlow, which has experienced a drastic advance since the arrival of flow-enabled networking devices. According to a study, 70% of the network operators have devices with flow-exporting capabilities. Netflow export technology aggregates network packets into the flow. This NetFlow format advancement in the number of IP packet features has a huge advantage. In other words, if the latest version of NetFlow is enabled on your network device, a lot of network information becomes available to you; for example, Netflow v9 traffic has 280 features. Serving many network issues, these entire features may be necessary. However, in the case of network Intrusion Detection System (IDS) not all these features may be needed. Some may be redundant and not relevant. Such features can affect the performance of the IDS. Simultaneously, the time required for identifying the attack and resource consumption for IDS is increasing. An ID detects malicious traffic based on the extracted features from network flow. This article reviews the use of feature selection for the flow-based network IDS.
  • Blockchain Based Record Management System in Hospitals
    Adarsh Vernekar, Akash Sakhare, Prashant Bhapkar, Saurabh Jadhav, Rahul B. Adhao
    2023 International Conference on Innovative Trends in Information Technology Icitiit 2023, 2023
    Every industry is expanding too quickly and adjusting to this new technology as it develops. Since it has the potential to deliver more precise and economical patient care, healthcare data management has recently attracted a lot of attention. Even today, many hospitals hold their own autonomous record management system, which causes security issues. In centralized record management systems, data privacy, centralized data stewardship, and system vulnerability problems affect traditional client-server-based and cloud-based health data management systems. Blockchain technology has a promising future in the healthcare industry because of its immutability, transparency, privacy, and security properties, which can address certain critical problems with the health management system. A more patient-oriented approach in healthcare systems is required to improve the accuracy and transparency of medical data. In healthcare systems, health records are the most sensitive asset that must be unique and protected across the system. Our objective is to showcase the potential use of blockchain technology in health record management systems in hospitals. In this paper, we demonstrate a health record management system that uses blockchain technology to store the medical records of a patient across multiple hospitals. The proposed system will mainly help in maintaining consistency issues related to data along with improved security in the system.
  • NER in Hindi Language Using Transformer Model:XLM-Roberta
    Aditya A Choure, Rahul B Adhao, Vinod K Pachghare
    2022 IEEE International Conference on Blockchain and Distributed Systems Security Icbds 2022, 2022
    Natural language processing (NLP) is a computer program that trains computers to read and understand the text and spoken words in the same way that people do. In Natural Language Processing, Named Entity Recognition (NER) is a crucial field. It extracts information from given texts and is used to translate machines, text to speech synthesis, to understand natural language, etc. Its main goal is to categorize words in a text that represent names into specified tags like location, organization, person-name, date, time, and measures. In this paper, the proposed method extracts entities on Hindi Fraud Call (publicly not available) annotated Corpus using XLM-Roberta (base-sized model). By pre-training model to build the accurate NER system for datasets, the Authors are using XLM-Roberta as a multi-layer bidirectional transformer encoder for learning deep bidirectional Hindi word representations. The fine-tuning concept is used in this proposed method. XLM-Roberta Model has been fine-tuned to extract nine entities from sentences based on context of sentences to achieve better performance. An Annotated corpus for Hindi with a tag set of Nine different Named Entity (NE) classes, defined as part of the NER Shared Task for South and Southeast Asian Languages (SSEAL) at IJCNLP. Nine entities have been recognized from sentences. The Obtained F1-score(micro) and F1-score(macro) are 0.96 and 0.80, respectively.
  • Ensemble Based Feature Selection Technique For Flow Based Intrusion Detection System
    Mayur V Tayde, Rahul B Adhao, Vinod Pachghare
    2022 IEEE 7th International Conference for Convergence in Technology I2ct 2022, 2022
    The use of the internet has been drastically increasing in the last decades; hence, network security has become a big issue. To resolve these issues Intrusion detection system (IDS) plays a very important role. IDS detects malicious activity in the network by inspecting all network traffic and sending an alert message or signal to the administrator. So that the person can take the most reliable action to stop or block an attack, by using a higher number of features, we can easily develop an IDS, but it will take more computational time. Hence, it is necessary to choose a relevant set of features from the dataset, which will give us high accuracy and low computational time. In the proposed system CICIDS-2017 dataset is used, and for feature selection, the arithmetic operation is performed on Gain ratio, and Pearson’s correlation. Based on the arithmetic operation result, the top 30 features are selected out of 78 features, and these reduced sets of features are fed to the machine learning classifier random forest, and results are calculated. The result shows that a reduced set of features provides better accuracy, 99.8302%, than the total 78 features.
  • An Intrusion Detection System for Zero-Day Attacks to Reduce False Positive Rates
    Priya Pitre, Arya Gandhi, Vaishnavi Konde, Rahul Adhao, Vinod Pachghare
    2022 International Conference for Advancement in Technology Iconat 2022, 2022
    The Intrusion Detection System (IDS) - is one that monitors network traffic to issue alerts about any suspicious activity on the network. Conventionally, there are two types of IDSs - Signature-Based, which efficiently detect already known attacks, and Anomaly-Based, where models are trained to detect unknown attacks. The latter type of IDS plays a crucial role in detecting zero-day attacks- a type of attack where the vulnerability of the software is exploited before a developer can take action on it. However, it comes with a few problems, like its high false-positive rates that cause the network to slow down and require constant human intervention and its inability to detect attacks in real-time. This paper analyzes state-of-the-art models that deal with this problem, analyzing their benefits and shortcomings. Further, we propose a framework for addressing these zero-day attacks and reducing their false positive rate of detection using a combination of feature selection methods and fine-tuning of the dataset specifically for false-positive detection. These methods will be tried with various optimizers and models several times, and their results will be compared. We attach results from preliminary testing on the novel idea of a subset of the dataset, with promising results to be applied to find the model that works better than most existing.
  • Feature Selection Based on Hall of Fame Strategy of Genetic Algorithm for Flow-Based IDS
    Rahul Adhao, Vinod Pachghare
    Lecture Notes in Networks and Systems, 2021
  • Ensemble-Based Filter Feature Selection Technique for Building Flow-Based IDS
    Ishita Karna, Aniket Madam, Chinmay Deokule, Rahul Adhao, Vinod Pachghare
    Access 2021 Proceedings of 2021 2nd International Conference on Advances in Computing Communication Embedded and Secure Systems, 2021
    Intrusion Detection systems play a crucial role in maintaining network security. It keeps track of network traffic for anomalous activities and detects any vulnerabilities in the network. It is not a trivial task to build one due to the high number of features in the dataset, which increases the computational overhead on the system. It is necessary that we select only the relevant features from the dataset to ensure that the model thus built provides high accuracy in low computational time. This paper works on different filter-based feature selection techniques to lower the complexity of intrusion detection systems while preserving the performance of the system. The use of feature selection techniques followed by ensemble learning provides an optimal subset of features. The proposed method attempts to handle the imbalance of classes in CIC-IDS2017 and NSL-KDD datasets by separately classifying the minority and majority classes. The model's performance is explored in terms of precision, accuracy, and F1 score, that has been observed to be superior to existing works in the field of intrusion detection.
  • Feature selection using principal component analysis and genetic algorithm
    Rahul Adhao, Vinod Pachghare
    Journal of Discrete Mathematical Sciences and Cryptography, 2020
  • Performance-Based Feature Selection Using Decision Tree
    Rahul B Adhao, Vinod K Pachghare
    Proceeding 1st International Conference on Innovative Trends and Advances in Engineering and Technology Icitaet 2019, 2019
  • Scalable tracking system for public buses using IoT technologies
    Jay Lohokare, Reshul Dani, Sumedh Sontakke, Rahul Adhao
    2017 International Conference on Emerging Trends and Innovation in ICT Icei 2017, 2017
  • Road traffic prediction and congestion control using Artificial Neural Networks
    Rohan More, Abhishek Mugal, Sheetal Rajgure, Rahul B. Adhao, V K. Pachghare
    International Conference on Computing Analytics and Security Trends Cast 2016, 2017

RECENT SCHOLAR PUBLICATIONS

  • A MAGIQ-TOPSIS-Fuzzy-Logic Based Cloud Service Providers Ranking Model for Federated Cloud Computing Environment
    V Saraf, R Adhao, V Pachghare
    Advances in Electrical and Computer Engineering 26 (1), 83-92 , 2026
    2026
  • A Trust Computation Model for Collaborative Cloud Environment
    N Kamble, V Saraf, V Pachghare, R Adhao
    Procedia Computer Science 258, 4169-4178 , 2025
    2025
  • Darknet traffic-based intrusion detection system
    C Mehere, I Rathi, K Abhyankar, R Adhao, VK Pachghare
    Advances in Networks, Intelligence and Computing, 597-607 , 2024
    2024
  • Ensemble of Statistically Based Methods for Identifying Features in Intrusion Detection System.
    R Adhao, N Kamble, V Pachghare
    Degrés 9 (4) , 2024
    2024
  • Exploring the efficiency: A comprehensive analysis of machine learning algorithms in WEKA software
    N Kamble, A Phatak, A Joshi, R Adhao, V PACHGHARE
    JOURNAL OF STATISTICS AND MANAGEMENT SYSTEMS 27 (5), 1009-1019 , 2024
    2024
  • P2P Energy Market: System Architecture
    P Desai, S Chaudhari, R Adhao, J Lohokare
    Proceedings of the KILBY 100 7th International Conference on Computing Sciences , 2023
    2023
  • Statistical feature selection based intrusion detection system for internet of things environment
    RB Adhao, M Tayde, V Pachghare
    COMPUTATIONAL INTELLIGENCE AND NETWORK SECURITY 2724 (1), 020003 , 2023
    2023
    Citations: 2
  • Feature Engineering for Flow‐Based IDS
    RB Adhao, VK Pachghare
    Wireless Communication Security, 69-90 , 2022
    2022
  • Ensemble of Bio-inspired Algorithm with Statistical Measures for Feature Selection to Design a Flow-Based Intrusion Detection System
    RAV Pachghare
    The International Journal of Next-Generation Computing (IJNGC) 13 (4), 901-912 , 2022
    2022
  • NER in Hindi language using transformer model: XLM-RoBERTa
    AA Choure, RB Adhao, VK Pachghare
    2022 IEEE International Conference on Blockchain and Distributed Systems … , 2022
    2022
    Citations: 17
  • Ensemble based feature selection technique for flow based intrusion detection system
    MV Tayde, RB Adhao, V Pachghare
    2022 IEEE 7th International conference for Convergence in Technology (I2CT), 1-4 , 2022
    2022
    Citations: 3
  • An intrusion detection system for zero-day attacks to reduce false positive rates
    P Pitre, A Gandhi, V Konde, R Adhao, V Pachghare
    2022 International Conference for Advancement in Technology (ICONAT), 1-6 , 2022
    2022
    Citations: 27
  • Zero-Day Attack Detection using Ensemble Technique.
    FI Wangde, SP Mulay, RB Adhao, VK Pachghare
    International Journal of Next-Generation Computing 12 (5) , 2021
    2021
    Citations: 1
  • Network Traffic Classification Using Feature Selections and two-tier stacked classifier.
    RB Adhao, VK Pachghare
    International Journal of Next-Generation Computing 12 (5) , 2021
    2021
    Citations: 5
  • Hybrid Intrusion Detection System.
    RB Adhao, S Mahefuj, VK Pachghare, VM Khadse
    International Journal of Next-Generation Computing 12 (5) , 2021
    2021
  • Support based graph framework for effective intrusion detection and classification
    RB Adhao, VK Pachghare
    2021
    Citations: 2
  • Ensemble-based filter feature selection technique for building flow-based IDS
    I Karna, A Madam, C Deokule, R Adhao, V Pachghare
    2021 2nd International Conference on Advances in Computing, Communication … , 2021
    2021
    Citations: 14
  • Feature selection based on hall of fame strategy of genetic algorithm for flow-based ids
    R Adhao, V Pachghare
    Data Science and Security: Proceedings of IDSCS 2021, 310-316 , 2021
    2021
    Citations: 4
  • Strategy of Genetic Algorithm
    R Adhao, V Pachghare
    Data Science and Security: Proceedings of IDSCS 2021, 310 , 2021
    2021
  • Feature selection using principal component analysis and genetic algorithm
    R Adhao, V Pachghare
    Journal of Discrete Mathematical Sciences and Cryptography 23 (2), 595-602 , 2020
    2020
    Citations: 51

MOST CITED SCHOLAR PUBLICATIONS

  • Road traffic prediction and congestion control using artificial neural networks
    R More, A Mugal, S Rajgure, RB Adhao, VK Pachghare
    2016 international conference on computing, analytics and security trends … , 2016
    2016
    Citations: 68
  • Feature selection using principal component analysis and genetic algorithm
    R Adhao, V Pachghare
    Journal of Discrete Mathematical Sciences and Cryptography 23 (2), 595-602 , 2020
    2020
    Citations: 51
  • Scalable tracking system for public buses using IoT technologies
    J Lohokare, R Dani, S Sontakke, R Adhao
    Emerging Trends & Innovation in ICT (ICEI), 2017 International Conference on … , 2017
    2017
    Citations: 30
  • An intrusion detection system for zero-day attacks to reduce false positive rates
    P Pitre, A Gandhi, V Konde, R Adhao, V Pachghare
    2022 International Conference for Advancement in Technology (ICONAT), 1-6 , 2022
    2022
    Citations: 27
  • Performance-based feature selection using decision tree
    RB Adhao, VK Pachghare
    2019 international conference on innovative trends and advances in … , 2019
    2019
    Citations: 23
  • NER in Hindi language using transformer model: XLM-RoBERTa
    AA Choure, RB Adhao, VK Pachghare
    2022 IEEE International Conference on Blockchain and Distributed Systems … , 2022
    2022
    Citations: 17
  • Ensemble-based filter feature selection technique for building flow-based IDS
    I Karna, A Madam, C Deokule, R Adhao, V Pachghare
    2021 2nd International Conference on Advances in Computing, Communication … , 2021
    2021
    Citations: 14
  • Network Traffic Classification Using Feature Selections and two-tier stacked classifier.
    RB Adhao, VK Pachghare
    International Journal of Next-Generation Computing 12 (5) , 2021
    2021
    Citations: 5
  • Feature selection based on hall of fame strategy of genetic algorithm for flow-based ids
    R Adhao, V Pachghare
    Data Science and Security: Proceedings of IDSCS 2021, 310-316 , 2021
    2021
    Citations: 4
  • Nids designed using two stages monitoring
    RB Adhao, AR Kshirsagar, VK Pachghare
    International Journal of Computer Science and Information Technologies 5 (1 … , 2014
    2014
    Citations: 4
  • Ensemble based feature selection technique for flow based intrusion detection system
    MV Tayde, RB Adhao, V Pachghare
    2022 IEEE 7th International conference for Convergence in Technology (I2CT), 1-4 , 2022
    2022
    Citations: 3
  • Statistical feature selection based intrusion detection system for internet of things environment
    RB Adhao, M Tayde, V Pachghare
    COMPUTATIONAL INTELLIGENCE AND NETWORK SECURITY 2724 (1), 020003 , 2023
    2023
    Citations: 2
  • Support based graph framework for effective intrusion detection and classification
    RB Adhao, VK Pachghare
    2021
    Citations: 2
  • WIDS Using Flow Based Approach
    RB Adhao
    College of Engineering Pung , 2014
    2014
    Citations: 2
  • Zero-Day Attack Detection using Ensemble Technique.
    FI Wangde, SP Mulay, RB Adhao, VK Pachghare
    International Journal of Next-Generation Computing 12 (5) , 2021
    2021
    Citations: 1
  • Wireless Intrusion Detection System using Reputation
    SC Gavande, DVK Pachghare, R Adhao
    International Journal of Advanced Research in Computer Science and Software , 2015
    2015
    Citations: 1
  • A MAGIQ-TOPSIS-Fuzzy-Logic Based Cloud Service Providers Ranking Model for Federated Cloud Computing Environment
    V Saraf, R Adhao, V Pachghare
    Advances in Electrical and Computer Engineering 26 (1), 83-92 , 2026
    2026
  • A Trust Computation Model for Collaborative Cloud Environment
    N Kamble, V Saraf, V Pachghare, R Adhao
    Procedia Computer Science 258, 4169-4178 , 2025
    2025
  • Darknet traffic-based intrusion detection system
    C Mehere, I Rathi, K Abhyankar, R Adhao, VK Pachghare
    Advances in Networks, Intelligence and Computing, 597-607 , 2024
    2024
  • Ensemble of Statistically Based Methods for Identifying Features in Intrusion Detection System.
    R Adhao, N Kamble, V Pachghare
    Degrés 9 (4) , 2024
    2024