Rahul Ghosh

Bio: Rahul Ghosh is an academic researcher from VIT University. The author has contributed to research in topics: Intrusion detection system & Python (programming language). The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

Data Intrusion Detection with basic Python coding and prevention of other intrusive manifestation by the use of intrusion application

Abstract: A model of an ongoing interruption location master framework fit for recognizing break-ins, entrances and different types of PC mishandle is portrayed. The model depends on the speculation that security infringement can be identified by checking a framework's review records for unusual examples of framework utilization. The program on interruption discovery will have the capacity to distinguish whether a site, which for instance requires our client ID and secret word, is dependable or not. The model is autonomous of a specific framework, application condition. Framework defenselessness, or sort of interruption, in this manner giving a structure to a broadly useful interruption recognition master framework.

Implementation of Vehicles Classification using Extreme Gradient Boost Algorithm

Abstract: This paper presents the implementation of vehicles classification using Extreme Gradient Boost (XG Boost) Algorithm to improve the accuracy in the vehicle classification with respect to the shapes and features. Generally, Classification based on different parameter such as hefty, classes, structures, extracting features, segmenting the images and semantic classification are being challenge to incorporate in Machine Learning. In order to overcome this barrier, XG boost algorithm has been implemented to achieve the high performance vehicle classification from the large scale surveillance dataset. The experimental results shows that the accuracy is improved in the vehicle classification with standard resolution image.

Joint Honeypot Networks and Hybrid Intrusion Detection System for Mobile Cloud Computing

Abstract: The growing smartphone technology and emerging mobile cloud technology are the latest wireless technology. Mobile cloud computing has many of the advantages that look forward to the future and it's also simple for hackers to take full control of many other users Privacy of Data. While data security is expected to be secured, the main drawback for users when the computer is connected to the internet it's not that difficult for an intruder to engage in a data theft on the required target. So, for providing better security the combination of Hybrid Intrusion Detection System (HyInt) and Honeypot networks is thus implemented into Mobile Cloud Environment with the significant purpose of mitigating unidentified and known attacks in order to provide security. Execution of the research work provides a pure perspective of the security and quality products of the algorithm that was not included in the previous research work. As part of the research work, intensive statistical analysis was performed to prove the consistency of the proposed algorithm. The implementation and evaluation outcome offers clear potential for any further research work in the cloud-based Intrusion Detection System. The implemented algorithm can be used for high-security cloud environment that is developed for army and banking purposes to monitor the network's activities effectively.

Implementation of Vehicles Classification using Extreme Gradient Boost Algorithm

Abstract: This paper presents the implementation of vehicles classification using Extreme Gradient Boost (XG Boost) Algorithm to improve the accuracy in the vehicle classification with respect to the shapes and features. Generally, Classification based on different parameter such as hefty, classes, structures, extracting features, segmenting the images and semantic classification are being challenge to incorporate in Machine Learning. In order to overcome this barrier, XG boost algorithm has been implemented to achieve the high performance vehicle classification from the large scale surveillance dataset. The experimental results shows that the accuracy is improved in the vehicle classification with standard resolution image.