No abstract

No abstract

Due to the advanced growth in multimedia data and Cloud Computing (CC), Secure Image Archival and Retrieval System (SIARS) on cloud has gained more interest in recent times. Content based image retrieval (CBIR) systems generally retrieve the images relevant to the query image (QI) from massive databases. However, the secure image retrieval process is needed to ensure data confidentiality and secure data transmission between cloud storage and users. Existing secure image retrieval models faces difficulties like minimum retrieval performance, which fails to adapt with the large-scale IR in cloud platform. To resolve this issue, this article presents a SIARS using deep learning (DL) and multiple share creation schemes. The proposed SIARS model involves Adagrad based convolutional neural network (AG-CNN) based feature extractor to extract the useful set of features from the input images. At the same time, secure multiple share creation (SMSC) schemes are executed to generate multiple shares of the input images. The resultant shares and the feature vectors are stored in the cloud database with the respective image identification number. Upon retrieval, the user provides a query image and reconstructs the received shared image to attain the related images from the database. An elaborate experimentation analysis is carried out on benchmark Corel10K dataset and the results are examined in terms of retrieval efficiency and image quality. The attained results ensured the superior performance of the SIARS model on all the applied test images.

Presently, a green Internet of Things (IoT) based energy aware network plays a significant part in the sensing technology. The development of IoT has a major impact on several application areas such as healthcare, smart city, transportation, etc. The exponential rise in the sensor nodes might result in enhanced energy dissipation. So, the minimization of environmental impact in green media networks is a challenging issue for both researchers and business people. Energy efficiency and security remain crucial in the design of IoT applications. This paper presents a new green energy-efficient routing with DL based anomaly detection (GEER-DLAD) technique for IoT applications. The presented model enables IoT devices to utilize energy effectively in such a way as to increase the network span. The GEER-DLAD technique performs error lossy compression (ELC) technique to lessen the quantity of data communication over the network. In addition, the moth flame swarm optimization (MSO) algorithm is applied for the optimal selection of routes in the network. Besides, DLAD process takes place via the recurrent neural network-long short term memory (RNN-LSTM) model to detect anomalies in the IoT communication networks. A detailed experimental validation process is carried out and the results ensured the betterment of the GEER-DLAD model in terms of energy efficiency and detection performance.

Wormhole attack is one of the serious routing attacks amongst all the network layer attacks launched on MANET. Wormhole attack is launched by creation of tunnels and it leads to total disruption of the routing paths on MANET. In this paper, MLDW-a multilayered Intrusion Detection Prevention System approach is proposed to detect and isolate wormhole attack on MANET. The routing protocol used is Adhoc On Demand Distance Vector (AODV). MLDW has a layered framework consisting of link latency estimator, intermediate neighbor node discovery mechanism, packet drop calculator, node energy degrade estimator followed by isolation technique. MLDW effectiveness is evaluated using ns2 network simulator.