Detecting DDoS Attacks in Cloud Computing Environment

Nowadays, Internet of Things (IoT) adoptions are burgeoning and deemed the lynchpin towards achieving ubiquitous connectivity. In this context, defining and leveraging robust IoT security risk management strategies are paramount for secure IoT adoptions. Thus, this study aims to support IoT adopters from any sector to formulate or reframe their IoT security risk management strategies to achieve robust strategies that effectively address IoT security issues. In a nutshell, this article relies on a mixed methods research methodology and proposes a reference model for IoT security risk management strategy. The proposed IoT security risk management strategy reference model (IoTSRM2) relies on the 25 selected IoT security best practices which are outlined using a proposed taxonomic hierarchy, and on the proposed three-phased methodology that consists of nine steps and outputs. The main contribution of this work is the proposed IoTSRM2 which consists of six domains, 16 objectives, and 30 prioritized controls. Furthermore, prior to providing the related work, this article provides a critical evaluation of selected informative references of IoTSRM2 based on their percentage-wise linkage to the IoTSRM2 domains and to the entire IoTSRM2. The findings of the critical evaluation illustrate, inter alia, the selected informative references that are the top three most and least linked to the entire IoTSRM2.

The Hadoop framework has been developed to effectively process data-intensive MapReduce applications. Hadoop users specify the application computation logic in terms of a map and a reduce function, which are often termed MapReduce applications. The Hadoop distributed file system is used to store the MapReduce application data on the Hadoop cluster nodes called Data nodes, whereas Name node is a control point for all Data nodes. While its resilience is increased, its current data-distribution methodologies are not necessarily efficient for heterogeneous distributed environments such as public clouds. This work contends that existing data distribution techniques are not necessarily suitable, since the performance of Hadoop typically degrades in heterogeneous environments whenever data-distribution is not determined as per the computing capability of the nodes. The concept of data-locality and its impact on the performance of Hadoop are key factors, since they affect the performance in the Map phase when scheduling tasks. The task scheduling techniques in Hadoop should arguably consider data locality to enhance performance. Various task scheduling techniques have been analysed to understand their data-locality awareness while scheduling applications. Other system factors also play a major role while achieving high performance in Hadoop data processing. The main contribution of this work is a novel methodology for data placement for Hadoop Data nodes based on their computing ratio. Two standard MapReduce applications, Word Count and Grep, have been executed and a significant performance improvement has been observed based on our proposed data distribution technique.