Secured Data Storage Using Deduplication in Cloud Computing Based on Elliptic Curve Cryptography

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 billion-dollar 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.     

Simultaneous fingerprint and high-wavenumber confocal Raman spectroscopy enhances early detection of cervical precancer in vivo

Raman spectroscopy is a vibrational spectroscopic technique capable of nondestructively probing endogenous biomolecules and their changes associated with dysplastic transformation in the tissue. The main objectives of this study are (i) to develop a simultaneous fingerprint (FP) and high-wavenumber (HW) confocal Raman spectroscopy and (ii) to investigate its diagnostic utility for improving in vivo diagnosis of cervical precancer (dysplasia). We have successfully developed an integrated FP/HW confocal Raman diagnostic system with a ball-lens Raman probe for simultaneous acquistion of FP/HW Raman signals of the cervix in vivo within 1 s. A total of 476 in vivo FP/HW Raman spectra (356 normal and 120 precancer) are acquired from 44 patients at clinical colposcopy. The distinctive Raman spectral differences between normal and dysplastic cervical tissue are observed at ~854, 937, 1001, 1095, 1253, 1313, 1445, 1654, 2946, and 3400 cm(-1) mainly related to proteins, lipids, glycogen, nucleic acids and water content in tissue. Multivariate diagnostic algorithms developed based on partial least-squares-discriminant analysis (PLS-DA) together with the leave-one-patient-out, cross-validation yield the diagnostic sensitivities of 84.2%, 76.7%, and 85.0%, respectively; specificities of 78.9%, 73.3%, and 81.7%, respectively; and overall diagnostic accuracies of 80.3%, 74.2%, and 82.6%, respectively, using FP, HW, and integrated FP/HW Raman spectroscopic techniques for in vivo diagnosis of cervical precancer. Receiver operating characteristic (ROC) analysis further confirms the best performance of the integrated FP/HW confocal Raman technique, compared to FP or HW Raman spectroscopy alone. This work demonstrates, for the first time, that the simultaneous FP/HW confocal Raman spectroscopy has the potential to be a clinically powerful tool for improving early diagnosis and detection of cervical precancer in vivo during clinical colposcopic examination.     

Malicious Traffic classification Using Long Short-Term Memory (LSTM) Model

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.

     

ELPC-Trust Framework for Wireless Sensor Networks

Wireless Personal Communications - Wireless Sensor Network (WSN) and its security concern play a vital part in its effecting functioning. WSN routing layer attacks pose a great threat to...     

An Efficient Clustering and Deep Learning Based Resource Scheduling for Edge Computing to Integrate Cloud-IoT

Wireless Personal Communications -