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Attacks in wireless sensor networks (WSNs) aim to prevent or eradicate the network's ability to perform its anticipated functions. Intrusion detection is a defense used in wireless sensor networks that can detect unknown attacks. Due to the incredible development in computer-related applications and massive Internet usage, it is indispensable to provide host and network security. The development of hacking technology tries to compromise computer security through intrusion. Intrusion detection system (IDS) was employed with the help of machine learning (ML) Algorithms to detect intrusions in the network. Classic ML algorithms like support vector machine (SVM), Knearest neighbour (KNN), and filter-based feature selection often led to poor accuracy and misclassification of intrusions. This article proposes a novel framework for IDS that can be enabled by Boruta feature selection with grid search random forest (BFS-GSRF) algorithm to overcome these issues. The performance of BFS-GSRF is compared with ML algorithms like linear discriminant analysis (LDA) and classification and regression tree (CART) etc. The proposed work was implemented and tested on network security laboratory -knowledge on discovery dataset (NSL-KDD). The experimental results show that the proposed model BFS-GSRF yields higher accuracy (i.e., 99%) in detecting attacks, and it is superior to LDA, CART, and other existing algorithms.

Nowadays, cloud based analytics platforms are replacing traditional physical clusters due to the high efficiency it provides. Such cloud platforms runs Hadoop on virtual clusters with remotely attached storage. In cloud architecture with multiple geographically separated regions, virtual machines (VMs) belonging to a virtual cluster are placed randomly. In order to run MapReduce jobs, data have to be moved to the regions where the VMs reside to achieve data locality. In this paper, we propose a data-location aware virtual cluster provisioning strategy to identify the data location and provision the cluster near to the storage. The use of bio-inspired optimization algorithms are considered for optimizing the placements of VMs. Data location aware cluster provisioning reduces the network distance between storage and the virtual cluster, resulting in faster job completion times.

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