No abstract

No abstract

<abstract> <p>Cybersecurity experts estimate that cyber-attack damage cost will rise tremendously. The massive utilization of the web raises stress over how to pass on electronic information safely. Usually, intruders try different attacks for getting sensitive information. An Intrusion Detection System (IDS) plays a crucial role in identifying the data and user deviations in an organization. In this paper, stream data mining is incorporated with an IDS to do a specific task. The task is to distinguish the important, covered up information successfully in less amount of time. The experiment focuses on improving the effectiveness of an IDS using the proposed Stacked Autoencoder Hoeffding Tree approach (SAE-HT) using Darwinian Particle Swarm Optimization (DPSO) for feature selection. The experiment is performed in NSL_KDD dataset the important features are obtained using DPSO and the classification is performed using proposed SAE-HT technique. The proposed technique achieves a higher accuracy of 97.7% when compared with all the other state-of-art techniques. It is observed that the proposed technique increases the accuracy and detection rate thus reducing the false alarm rate.</p> </abstract>

No abstract

The expanding amounts of information created by Internet of Things (IoT) devices places a strain on cloud computing, which is often used for data analysis and storage. This paper investigates a different approach based on edge cloud applications, which involves data filtering and processing before being delivered to a backup cloud environment. This Paper suggest designing and implementing a low cost, low power cluster of Single Board Computers (SBC) for this purpose, reducing the amount of data that must be transmitted elsewhere, using Big Data ideas and technology. An Apache Hadoop and Spark Cluster that was used to run a test application was containerized and deployed using a Raspberry Pi cluster and Docker.To obtain system data and analyze the setup's performance a Prometheusbased stack monitoring and alerting solution in the cloud based market is employed. This Paper assesses the system's complexity and demonstrates how containerization can improve fault tolerance and maintenance ease, allowing the suggested solution to be used in industry. An evaluation of the overall performance is presented to highlight the capabilities and limitations of the suggested architecture, taking into consideration the suggested solution's resource use in respect to device restrictions.