Biblio

Recently, as a result of the COVID-19 pandemic, the internet service has seen an upsurge in use. As a result, the usage of cloud computing apps, which offer services to end users on a subscription basis, rises in this situation. However, the availability and efficiency of cloud computing resources are impacted by DDoS attacks, which are designed to disrupt the availability and processing power of cloud computing services. Because there is no effective way for detecting or filtering DDoS attacks, they are a dependable weapon for cyber-attackers. Recently, researchers have been experimenting with machine learning (ML) methods in order to create efficient machine learning-based strategies for detecting DDoS assaults. In this context, we propose a technique for detecting DDoS attacks in a cloud computing environment using big data and deep learning algorithms. The proposed technique utilises big data spark technology to analyse a large number of incoming packets and a deep learning machine learning algorithm to filter malicious packets. The KDDCUP99 dataset was used for training and testing, and an accuracy of 99.73% was achieved.

For sharing resources using ad hoc communication MANET are quite effective and scalable medium. MANET is a distributed, decentralized, dynamic network with no fixed infrastructure, which are self- organized and self-managed. Achieving high security level is a major challenge in case of MANET. Layered architecture is one of the ways for handling security challenges, which enables collection and analysis of data from different security dimensions. This work proposes a novel multi-layered outlier detection algorithm using hierarchical similarity metric with hierarchical categorized data. Network performance with and without the presence of outlier is evaluated for different quality-of-service parameters like percentage of APDR and AT for small (100 to 200 nodes), medium (200 to 1000 nodes) and large (1000 to 3000 nodes) scale networks. For a network with and without outliers minimum improvements observed are 9.1 % and 0.61 % for APDR and AT respectively while the maximum improvements of 22.1 % and 104.1 %.