College of Engineering, Pune

About: College of Engineering, Pune is a based out in . It is known for research contribution in the topics: Sliding mode control & Control theory. The organization has 4264 authors who have published 3492 publications receiving 19371 citations. The organization is also known as: COEP.

Identification of green building attributes for the development of an assessment tool: a case study in India

Abstract: Green building rating systems have been developed to measure the level of sustainability of buildings. Existing methods can be applied to different regions by addressing additional aspects such as varied climatic conditions and regional variations. This paper investigated the most widely used environmental building assessment methods, namely BREEAM, LEED, SB-Tool, CASBEE, LEED-India, GRIHA and Eco-housing. Comparative studies revealed that the existing assessment schemes had some limitations when applied to an Indian built environment. This necessitates the development of a new building environmental assessment scheme. An attempt is made to develop a framework to evaluate sustainability of buildings in India by applying principal component analysis (PCA). The PCA of 82 valid responses on the attributes measuring sustainability of buildings has extracted nine components: (1) site selection; (2) environment; (3) building resources and re-use; (4) building services and management; (5) innovative cons...

DoS Attack Detection Using Machine Learning and Neural Network

Abstract: The current digital world is using the internet almost everywhere. The usage of internet has been increasing, however, threats are also increasing in numbers. One such threat is DoS attack which uses reasonable service requests to gain excessive computing and network resources and results in an inability to access them by legitimate users. The DoS attack can happen at different layers of OSI model such as network, transport and application layers. The aim of this paper is to detect DoS attack effectively using Machine learning (ML) and Neural Network (NN) algorithms. The detection is specifically focused on application layer DoS attack detection rather than at transport and network DoS attack detection. The latest DoS attack dataset CIC IDS 2017 dataset is used in the experiment. The experimentation has divided the dataset into different splits and the best split is found for each algorithm i.e. RF and MLP. Results of RF and MLP are compared and it is shown that RF provides better results than MLP.