Ajay Kumar

Work place: JSS Academy of Technical Education, Noida, India

E-mail: er.ajay.itcs@gmail.com


Research Interests: Machine Learning, Network Security, IoT


Ajay Kumar, Assistant Professor in the Department of Computer Science & Engineering at JSS Academy of Technical Education, Noida. He has received his M. Tech (Computer Engineering) from YMCA University of Science & Technology, Faridabad, India and B. Tech (Computer Engineering) from University Institute of Engineering & Technology, M.D.U. Rohtak. He has more than 10 years of academic experience. He has contributed 09 Research papers in International Journal and 11 Research papers in International/National Conferences/proceedings and Edited Books. He has added 02 Patents with his name out of which 01 has granted. His areas of research are Machine Learning, IOT & Network Security. He has organized various workshops and FDPs in these areas. He is the life time member of International Association of Engineers (IAENG).

Author Articles
A Progressive Key Administration for Block-Chain Technology with Lagrange Interpolation

By Pradeep Kumar Ajay Kumar Mukesh Raj Priyank Sirohi

DOI: https://doi.org/10.5815/ijieeb.2024.03.05, Pub. Date: 8 Jun. 2024

Block chain is a computerized data set containing data (like records of monetary exchanges) that can be at the same time utilized and shared inside an enormous decentralized, openly open organization. Block chain development has been a prominent occurrence of changing the statutes of wellbeing in money related trades and information exchange. It offers an extraordinary development for data integration with security. Block chain relies upon the norms of understanding, decentralization, and cryptography for following the trust in trades. In any case, block chain security issues have continued to agitate various affiliations and early adopters. It is sure that, even the grounded block chain new organizations experience burdens in block chain security. Without a doubt, block chain innovation has seen a far-reaching adaption lately. Aside from beginning adaption into digital currencies, today it is being utilized in medical care, land, shrewd contacts, and so forth.  The ill-advised execution of innovation has been the reason for some block chain block protection concern, which can put the block chain vulnerable and can permit the aggressors to play out a few noxious exercises. To address the secrecy to the sensitive information in the Block chain organization, a proposed method namely progressive secure key administration for Block-Chain Technology with Lagrange Interpolation (PKABCLI) has been presented in this paper.

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An Enhanced Method Utilizing Hopfield Neural Model for Mobile Agent Protection

By Pradeep Kumar Niraj Singhal Ajay Kumar Kakoli Banerjee

DOI: https://doi.org/10.5815/ijwmt.2023.05.03, Pub. Date: 8 Oct. 2023

Mobile agent is a piece of computer code that organically goes from one host to the another in a consistent or inconsistent environment to distribute data among users. An autonomous mobile agent is an operational programme that may migrate from one computer to machine in different networks under its own direction. Numerous health care procedures use the mobile agent concept. An agent can choose to either follow a predetermined course on the network or determine its own path using information gathered from the network. Security concerns are the main issue with mobile agents. Agent servers that provide the agents with a setting for prosecution are vulnerable to attack by cunning agents. In the same way agent could be carrying sensitive information like credit card details, national level security message, passwords and attackers can access these files by acting as a middle man. In this paper, optimized approach is provided to encrypt the data carried by mobile agent with Advanced Encryption Standard (AES) algorithm and secure key to be utilized by the AES Encryption algorithm is generated with the help of Hopfield Neural Network (HNN). To validate our approach, the comparison is done and found that the time taken to generate the key using HNN is 1101ms for 1000 iterations which is lesser than the existing models that are Recurrent Neural Networks and Multilayer Perceptron Network models. To add an additional level of security, data is encoded using hash maps which make the data not easily readable even after decrypting the information. In this way it is ensured that, when the confidential data is transmitted between the sender and the receiver, no one can regenerate the message as there is no exchange of key involved in the process.

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