Spectrum energy detection in cognitive radio networks based on a novel adaptive threshold energy detection method

With the rapid development of the Internet, the security of network multimedia data has attracted increasingly more attention. The moving target defense (MTD) and cyber mimic defense (CMD) approaches provide a new way to solve this problem. To enhance the security of network multimedia data, this paper proposes a mimic encryption box for network multimedia data security. The mimic encryption box can directly access the network where the multimedia device is located, automatically complete the negotiation, provide safe and convenient encryption services, and effectively prevent network attacks. According to the principles of dynamization, diversification, and randomization, the mimic encryption box uses a reconfigurable encryption algorithm to encrypt network data and uses IP address hopping, port number hopping, protocol camouflage, and network channel change to increase the attack threshold. Second, the mimic encryption box has a built-in pseudorandom number generator and key management system, which can generate an initial random key and update the key with the hash value of the data packet to achieve “one packet, one key.” Finally, through the cooperation of the ARM and the FPGA, an access control list can be used to filter illegal data and monitor the working status of the system in real time. If an abnormality is found, the feedback reconstruction mechanism is used to “clean” the FPGA to make it work normally again. The experimental results and analysis show that the mimic encryption box designed in this paper has high network encryption performance and can effectively prevent data leakage. At the same time, it provides a mimic security defense mechanism at multiple levels, which can effectively resist a variety of network attacks and has high security.

/pdf/mimic-encryption-box-for-network-multimedia-data-security-4ky18uymx7.pdf

Mimic Encryption Box for Network Multimedia Data Security

The purpose of this work is to solve the problems facing toll plazas, and the system may also allow authorities to solve car theft cases very effectively. In the proposed scheme, to capture information on passing vehicles and make hassle-free automated transactions supporting the unique identity pin (UIP), the toll will be equipped with optical character recognition (OCR) and RFID sensing. The UIP is passed to the central server unit (CSU) where the amount of the car from the account is deducted. The balance is deducted by using RFID from the car owner’s account. The deduction number is tracked with the help of the RTC, i.e. the number will not be deducted if within a given time the same car crosses the same tollgate. It will signal TCS to unlock the barricade until the balance is deducted at CSU, and the vehicle is not permitted to move; if the vehicle is found to be robbed at CSU, it will mean that the TCS will not unlock the barricade. For further intervention, these information can be included.

OCR-Based Automatic Toll Collection and Theft Vehicle Detection Using IoT

This study proposes a new approach to cloud data security, with two distinct processes: proposed data sanitization and restoration. By hiding it from unwanted users and access, the data sanitization procedure ensures the protection of sensitive data stored in massive databases. In this case, the Modified Apriori approach is used to clean the data. In this scenario, the central goal is to generate a key, which is done using an optimization algorithm. Recovery or retrieval of cleaned data is a reversible process in data recovery. The optimization key for both sanitization and restoration will be discovered using the proposed MFUWOA. The optimal key for both restorations as well as sanitization will be determined using the proposed MFUWOA. While resolving the optimization challenge, the targets stated include the data retention ratio, hidden ratio, and level of alteration. Moreover, the adopted MFUWOA model for dataset 1 attains the best fitness value (∼0.2) to extant schemes including WOA (∼0.3), MFO (∼0.7), PRO (∼0.34), SSO (∼0.8), CMBO (∼0.4), and ALO (∼1.0), correspondingly. 

Modified Apriori Based Data Sanitization for Cloud Data Security: An Optimization Assisted Model

A new secure data retrieval system based on ECDH and hierarchical clustering with Pearson correlation

Cloud service provide the information security to protect the data using broad set of privacy policies and controls. Various security mechanisms concentrates the reality of key leakage issues to deal the security policy. The problem arise due to the leakage of data from attackers which leads the time factor of creating vulnerabilities and information loss. So information security in the cloud needs higher security crypto policy standards to make effective privacy protection. The cloud security service provider’s intent the proactive security standard to make the higher security in cryptographic encryption techniques. To propose a standard two-phase implementation of Round-key and random key based crypto security encryption standard (R2R-CSES) for improving security system in a cloud environment. R2R provides the high-level implementation of security protection using key verification policy. This service makes the random encryption using proactive security encryption with round random crypto policy holds key applicable standards with cloud authenticator policy. The projection of our results estimates the advanced encryption standard with key substitution make effective security to prevent vulnerabilities. The optimization produces least time complexity with service access to provide the security mechanism in high standard.

R2R-CSES: proactive security data process using random round crypto security encryption standard in cloud environment

/pdf/cloud-secure-distributed-storage-deduplication-scheme-for-2aiccl6n1p.pdf

Cloud Secure Distributed Storage Deduplication Scheme for Encrypted Data

/pdf/retraction-note-to-r2r-cses-proactive-security-data-process-3kev4sk7.pdf

Retraction Note to: R2R-CSES: proactive security data process using random round crypto security encryption standard in cloud environment

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R2R-CSES: proactive security data process using random round crypto security encryption standard in cloud environment

Cloud Secure Distributed Storage Deduplication Scheme for Encrypted Data

Retraction Note to: R2R-CSES: proactive security data process using random round crypto security encryption standard in cloud environment