The tremendous development of cloud computing with related technologies is an unexpected one. However, centralized cloud storage faces few challenges such as latency, storage, and packet drop in the network. Cloud storage gets more attention due to its huge data storage and ensures the security of secret information. Most of the developments in cloud storage have been positive except better cost model and effectiveness, but still data leakage in security are billiondollar questions to consumers. Traditional data security techniques are usually based on cryptographic methods, but these approaches may not be able to withstand an attack from the cloud server's interior. So, we suggest a model called multi-layer storage (MLS) based on security using elliptical curve cryptography (ECC). The suggested model focuses on the significance of cloud storage along with data protection and removing duplicates at the initial level. Based on divide and combine methodologies, the data are divided into three parts. Here, the first two portions of data are stored in the local system and fog nodes to secure the data using the encoding and decoding technique. The other part of the encrypted data is saved in the cloud. The viability of our model has been tested by research in terms of safety measures and test evaluation, and it is truly a powerful complement to existing methods in cloud storage.
The current research paper discusses the implementation of higher order-matched filter design using odd and even phase processes for efficient area and time delay reduction. Matched filters are widely used tools in the recognition of specified task. When higher order taps are implemented upon the transposed form of matched filters, it can enhance the image recognition application and its performance in terms of identification and accuracy. The proposed method i.e., odd and even phases' process of FIR filter can reduce the number of multipliers and adders, used in existing system. The main advantage of using higher order tap-matched filter is that it can reduce the area required, owing to its odd and even processes. Further, it also successfully reduces the time delay, especially in case of high order demands. The performance of higher order matched filter design, using odd and even phase process, was analyzed using Xilinx 9.1 ISE Simulator. The study results accomplished reduction in area, 70% increase in throughput compared to traditional implementation and reduced time delay. In addition to these, Vedic multiplier-based FIR is modified with a tree-based MAM that reduces the number of shifter and adder to replace the multiplier.
Mashup of health care data from different medical sources must be privacy preserved since the data recipient and/or the data provider may not always be a trusted party. Raw medical data contains person specific sensitive information like ailment, surgery etc. and hence it is susceptible to certain privacy attacks such as attribute linkage and record linkage. There are different privacy models to thwart the privacy attacks. This paper illustrates how to vertically integrate the data from mental health clinic and National AIDS Control Organization (NACO) and preserve privacy using the LKC privacy model.
The preliminary research in the area of applications of neural networks and pattern matching algorithms in species classification is presented. Artificial neural networks for classification and different pattern matching algorithms for matching the given DNA patterns or strings with the existing DNA sequences available in the databases are specifically studied. A set of local searching algorithms were experimented for different test string lengths and their time complexity is tabulated. Conclusions and future directions are also presented.
This paper proposes Lion Optimized Cognitive Acoustic Network (LOCAN) to reduce packet delay and packet loss during packet transmission in Underwater Acoustic Sensor Network (UWASN). Packet delay and packet loss in UWASN are because of water column variations such as Doppler effect and geometric spreading (GS). Doppler effect forms due to sensor node’s motion and sea surface variations such as salinity and temperature. Geometric spreading (GS) occurs due to sediment drift wave fronts and frequent changes in node’s location and depth. Water column variations change the amplitude of sound propagation, causing channel coherence and multipath interference, which affect packet transmission. The existing UWASN algorithms focus only on temperature and salinity variations. In LOCAN, channel selection through Lion Optimization Algorithm solves the problems of water column variation and improves the battery life, network lifetime, and throughput. The proposed algorithms show a better result in terms of efficiency, when compared to existing UWASN algorithms.
Malicious traffic classification is the initial and primary step for any network-based security systems. This traffic classification systems include behavior-based anomaly detection system and Intrusion Detection System. Existing methods always relies on the conventional techniques and process the data in the fixed sequence, which may leads to performance issues. Furthermore, conventional techniques require proper annotation to process the volumetric data. Relying on the data annotation for efficient traffic classification may leads to network loops and bandwidth issues within the network. To address the above-mentioned issues, this paper presents a novel solution based on artificial intelligence perspective. The key idea of this paper is to propose a novel malicious classification system using Long Short-Term Memory (LSTM) model. To validate the efficiency of the proposed model, an experimental setup along with experimental validation is carried out. From the experimental results, it is proven that the proposed model is better in terms of accuracy, throughput when compared to the state-of-the-art models. Further, the accuracy of the proposed model outperforms the existing state of the art models with increase in 5% and overall 99.5% in accuracy.
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