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.

Detection of DoS/DDoS Attack against HTTP Servers Using Naive Bayesian

Abstract: With a growth of E-commerce and availability of resources over internet number of attacks on servers providing these services, resources are also increased. Denial of service and Distributed Denial of Service are most widely launched attacks against these servers for preventing legitimate users from accessing these services. This paper presents architecture of offline Signature based Network Intrusion Detection System for detection of Denial/Distributed Denial of Service attacks against HTTP servers using distributed processing and Naive Bayesian classifier. Experimental results are provided to prove the efficiency of proposed architecture.

Assessment of Object Detection Using Deep Convolutional Neural Networks

Abstract: Detecting the objects from images and videos has always been the point of active research area for the applications of computer vision and artificial intelligence namely robotics, self-driving cars, automated video surveillance, crowd management, home automation and manufacturing industries, activity recognition systems, medical imaging, and biometrics. The recent years witnessed the boom of deep learning technology for its effective performance on image classification and detection challenges in visual recognition competitions like PASCAL VOC, Microsoft COCO, and ImageNet. Deep convolutional neural networks have provided promising results for object detection by alleviating the need for human expertise for manually handcrafting the features for extraction. It allows the model to learn automatically by letting the neural network to be trained on large-scale image data using powerful and robust GPUs in a parallel way, thus, reducing training time. This paper aims to highlight the state-of-the-art approaches based on the deep convolutional neural networks especially designed for object detection from images.

Methods for detection of cyberbullying: A survey

Abstract: The advent of the digital age has paved the way for a new form of bullying which often leads to social stigma. With an increase in the use of social media platforms by adolescents, cyber bullying has become quite rampant and in some extreme cases it has also resulted in suicides by the victims. Very little efforts have been taken to curb this social menace and hence, this paper tries to address this issue by reviewing the steps that can be undertaken to detect cyber bullying on online social networks. This paper aims to review the different methods and algorithms used for detection in cyber bullying and provide a comparative study amongst them so as to decide which method is the most effective approach and provides the best accuracy.