Visible to the public Biblio

Filters: Author is Gomathi, S.  [Clear All Filters]
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
D
Gomathi, S., Parmar, Nilesh, Devi, Jyoti, Patel, Namrata.  2020.  Detecting Malware Attack on Cloud using Deep Learning Vector Quantization. 2020 12th International Conference on Computational Intelligence and Communication Networks (CICN). :356—361.

In recent times cloud services are used widely and due to which there are so many attacks on the cloud devices. One of the major attacks is DDos (distributed denial-of-service) -attack which mainly targeted the Memcached which is a caching system developed for speeding the websites and the networks through Memcached's database. The DDoS attack tries to destroy the database by creating a flood of internet traffic at the targeted server end. Attackers send the spoofing applications to the vulnerable UDP Memcached server which even manipulate the legitimate identity of the sender. In this work, we have proposed a vector quantization approach based on a supervised deep learning approach to detect the Memcached attack performed by the use of malicious firmware on different types of Cloud attached devices. This vector quantization approach detects the DDoas attack performed by malicious firmware on the different types of cloud devices and this also classifies the applications which are vulnerable to attack based on cloud-The Hackbeased services. The result computed during the testing shows the 98.2 % as legally positive and 0.034% as falsely negative.