Cloud computing has very attractive features like elastic, on demand and fully managed computer system resources and services. However, due to its distributed and dynamic nature as well as vulnerabilities in virtualization implementation, the cloud environment is prone to various cyber-attacks and security issues related to cloud model. Some of them are inability to access data coming to and from cloud service, theft and misuse of data hosted, no control over sensitive data access, advance threats like malware injection attack, wrapping attacks, virtual machine escape, distributed denial of service attack (DDoS) etc. DDoS is one of the notorious attack. Despite a number of available potential solutions for the detection of DDoS attacks, the increasing frequency and potency of recent attacks and the constantly evolving attack vectors, necessitate the development of improved detection approaches. This paper proposes a novel architecture that combines a well posed stacked sparse AutoEncoder (AE) for feature learning with a Deep Neural Network (DNN) for classification of network traffic into benign traffic and DDoS attack traffic. AE and DNN are optimized for detection of DDoS attacks by tuning the parameters using appropriately designed techniques. The improvements suggested in this paper lead to low reconstruction error, prevent exploding and vanishing gradients, and lead to smaller network which avoids overfitting. A comparative analysis of the proposed approach with ten state-of-the-art approaches using performance metrics-detection accuracy, precision, recall and F1-Score, has been conducted. Experiments have been performed on CICIDS2017 and NSL-KDD standard datasets for validation. Proposed approach outperforms existing approaches over the NSL-KDD dataset and yields competitive results over the CICIDS2017 dataset.
World Wide Web (WWW) is now a famous medium by which people all around the world can spread and gather information of all kind. However, there is large amount of irrelevant redundant and information on web pages also. Such information makes various web mining tasks web page crawling, web page classification, link based ranking and topic distillation complex. Previously, the relevant content was extracted only from textual part of web pages. But now-a-days the content on web page is not only in the text form but also as an image, video or audio. This paper proposes an improved algorithm for extracting informative content from web pages i.e. it extracts the relevant content not only as text but also as images, videos, audios, adobe flash files and online games. Experiments were conducted on different real websites show that precision and recall values of our approach is superior to the previous Word to Leaf Ratio approach.
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