Summary
Task clustering process in distributed systems is used to improve the performance of executing workflow applications. In cloud‐assisted Internet of things (CIoT) framework, performing efficient task clustering is highly challenging because of the dynamic nature of cloud, availability of various billing models, and billing granularity options. This work aims at reducing the security overhead among the dependent tasks by using data transfer aware grouping technique (DTAGT), which takes into consideration the amount of data transferred among dependent tasks to obtain a cluster that reduces the security overhead in the network by optimizing the data transfer rate. Collecting the data in a CIoT environment is difficult, and the redundancy among the data from the sensors tends to decrease the lifetime of the network and also increases the latency has been resolved using proposed DTAGT. The overall objective of the proposed grouping algorithm is to reduce the total completion time and cost of executing workflow application with high reliability in CIoT platform. The proposed system is implemented, and the result analysis shows DTWCT (Dual‐tree complex wavelet transform) outperforms the other related works.
Internet of Things (IoT) played a crucial role in various sectors such as automobiles and the logistic tracking medical field because it consists of distributed nodes, servers, and software for effective communication. Although, this IoT paradigm suffered from intrusion threats and attacks that cause security and privacy issues. Existing intrusion detection techniques fail to maintain reliability against the attacks. Therefore, in this work, IoT intrusion threat has been analyzed by using the sparse convolute network to contest the threats and attacks. The network is trained using sets of intrusion data, characteristics, and suspicious activities, which helps identify and track the attacks, mainly Distributed Denial of Service (DDoS) attacks. Along with this, the network is optimized using evolutionary techniques that identify and detect the regular, error, and intrusion attempts under different conditions. The sparse network forms the complex hypotheses evaluated using neurons, and the obtained event stream outputs are propagated to further hidden layer processes. This process minimizes the intrusion involvement in IoT data transmission. The effective utilization of training patterns in the network classifies the standard and threat patterns successfully. Then the effectiveness of the system is evaluated using experimental results and discussion.
With the development of technologies, most of the users utilizing the Internet for transmitting information from one place to another place. The transmitted data may be affected because of the intermediate user. Therefore, the steganography approach is applied for managing the secret information. Here audio steganography is utilized to maintain the secret information by hiding the image into the audio files. In this work, discrete cosine transforms, and discrete wavelet transform is applied to perform the Steganalysis process. The optimal hiding location has been identified by using the optimization technique called a genetic algorithm. The method utilizes the selection, crossover and mutation operators for selecting the best location. The chosen locations are difficult to predict by unauthorized users because the embedded location is varied from information to information. Then the efficiency of the system ensures the high PSNR, structural similarity index (SSIM), minimum mean square error value and Jaccard, which is evaluated on the audio Steganalysis dataset.
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