Narrow Results

In recent years, blockchain (BC) technologies have been increasing for data secrecy, system reliability and safety. BC is vulnerable to cyberattacks despite its utility. According to the statistics, attacks are rare, which differs greatly from the average. The goal of BC attack detection is to discover insights, patterns and anomalies within massive data repositories, it may benefit from deep learning. In this paper, the Prevention of Insider Attacks using Blockchain with Hierarchical Auto-associative Polynomial Convolutional Neural Network in Cloud Platform (PIS-BCNN-CP) is proposed. Here, the node authentication is handled by the smart contract. The aim of authorizing a node is to confirm that only a particular node has the possibility to submit and recover the information. Then Hierarchical Auto-associative Polynomial Convolutional Neural Network (HAAPCNN) is proposed to detect the Insider Attacks as Malicious and Normal. Generally, HAAPCNN does not agree with any optimization strategies to determine the optimal parameters for guaranteeing the exact detection of insider attacks. Hence, the Bear Smell Search Algorithm (BSSA) is exploited to optimize the weight parameters of a HAAPCNN. The BC Enabled Secure Data Storage depends on Proof of Continuous Work (PoCW) consensus BC algorithm is used. The proposed system is implemented and evaluated using performance metrics. The results provide higher accuracy, and lower False Negative Rate when compared with existing state-of-the-art methods.

Storage of data security has emerged as a basic necessity for both large- and small-scale industries. Cloud computing is internet-based computing technology in which people can work with their application with a high level of security in Internet of Things (IoT) enhanced devices. Cloud computing also provides privilege to store data that are collected from the devices in server side. Confidentiality and integrity of data play a major issue in cloud computing while preserving the IoT data. In this paper, the security of data is achieved by performing clustering and further applying cryptographic technique to the clustered data gathered from the devices that connected to the internet. Security of data can be improved by using a prominent cryptographic technique Attribute Based Encryption (ABE). Initially, the documents are clustered using the EM algorithm and clustered results are stored in various parts of the cloud. These documents are secured and can be accessed by the user who satisfies the attributes. In some cases, if the users’ attributes are not satisfied with the documents, it cannot be accessed by the corresponding user. Thus, the data from various devices are encrypted and maintained by the owner securely.

In embedded multicore shared memory systems, processing elements (PEs) are mutually untrusted since they carry different computing tasks independently. Therefore, the sharing of secret constants (SCs) between PEs, which is applied in the existing confidentiality protection schemes, will lead to the leakage of nonshared data. Besides, for integrity protection, tree construction checking over the whole counter space leads to the increase of both memory occupation and the average delay of verification. In this paper, we propose a ciphertext sharing confidentiality protection scheme based on certificateless proxy re-encryption and an integrity protection scheme based on a multigranularity scalable hash tree for secure data sharing between untrusted processing elements (SDSUP). With our schemes, the SC does not need to be shared and the scale of the checking tree is reduced, thus solving the leakage of nonshared data and reducing the high cost in integrity check. The results from the Rice Simulator for ILP Multiprocessors (RSIM) multicore simulator show that compared with the unprotected system, the performance degradation from applying the confidentiality protection scheme is 17.3% on average. Moreover, the performance degradation of the integrity protection scheme is 12.89%, which is superior to 35.36% for the bonsai Merkle tree (BMT), 29.49% for the multigrained hash tree (MGT) and 21.82% for the multigranularity incremental hash tree (MIT).

One of the main features of information flow control is to ensure the enforcement of privacy and regulated accessibility. However, most information flow models that have been proposed do not provide substantial assurance to enforce end-to-end confidentiality policies or they are too restrictive, overprotected, and inflexible. This paper presents an approach to control flow information in object-oriented systems using versions, thus allowing considerable flexibility without compromising system security by leaking sensitive information. Models based on message filtering intercept every message exchanged among objects to control the flow of information. Versions are proposed to provide flexibility and avoid unnecessary and undesirable blocking of messages during the filtering process. Two options of operations are supported by versions — cloning reply and non-cloning reply. Furthermore, we present an algorithm which enforces message filtering through these operations.

We propose an inference prevention agent as a tool that enables each of the databases in a distributed system to keep track of probabilistic dependencies with other databases and then use that information to help preserve the confidentiality of sensitive data. This is accomplished with minimal sacrifice of the performance and survivability gains that are associated with distributed database systems.

Top 10 Most Common Intellectual Property Rights Mistakes During Venture Capital Due Diligence.

Currently, the size of multimedia data is rising gradually from gigabytes to petabytes, due to the progression of a larger quantity of realistic data. The majority of big data is conveyed via the internet and they were accumulated on cloud servers. Since cloud computing offers internet-oriented services, there were a lot of attackers and malevolent users. They always attempt to deploy the private data of users without any right access. At certain times, they substitute the real data by any counterfeit data. As a result, data protection has turned out to be a noteworthy concern in recent times. This paper aims to establish an optimization-based privacy preservation model for preserving multimedia data by selecting the optimal secret key. Here, the encryption and decryption process is carried out by Improved Blowfish cryptographic technique, where the sensitive data in cloud server is preserved using the optimal key. Optimal key generation is the significant procedure to ensure the objectives of integrity and confidentiality. Likewise, data restoration is the inverse process of sanitization (decryption). In both the cases, key generation remains a major aspect, which is optimally chosen by a novel hybrid algorithm termed as “Clan based Crow Search with Adaptive Awareness probability (CCS-AAP). Finally, an analysis is carried out to validate the improvement of the proposed method.

While B2C via cloud computing has grown rapidly, information security and trustworthy computation are still major issues in today’s industrial realm. As a result, the routine workflow process varies dynamically over the course of data transactions, resulting in uncertainty. Consumers’ private information becomes easily vulnerable due to such data uncertainty. Intruders/fake marketers may permeate deeply into their current profiles due to the Internet, which leads to the creation of fictitious online markets that, in turn, drive more demand. This evolving business interaction leverages new interlopes to target customers’ data more precisely. However, it is still lacking in terms of complete techniques for dealing with such issues. These days, uncertain B2C workflows are greatly aided by Blockchain technology, especially to most of its security-enhancing characteristics. Despite numerous existing studies that have recommended employing Blockchain to handle B2C records, it is necessary to employ the technology in such business sectors to offer secured record management, workflow and keep data with high confidentiality and integrity intact. In this study, we suggest a Distributed Decentralized Security Scheme (DDSS) that manages and secures uncertain datasets through Blockchain technology in the B2C sector. To improve the safety of data workflows, the proposed approach enables the user to verify the authenticity of the records they require. A smart contract or special chain-code is used to dictate the parameters of the suggested system, ensuring security. The findings and discussions explain that the suggested scheme is far more effective than the previous strategies and has a greater level of confidentiality and integrity strength.

Security has become a major concern of software systems, especially distributed systems. The existence of various attacks should be considered in designing and developing those systems such that appropriate countermeasures could be applied. This chapter provides an overview of different types of possible attacks and countermeasures in the software security area. In addition, it discusses the security problems in mobile agent systems and introduces several related research works. This chapter also presents our research works on mobile agent system security based on Extended Elementary Object System (EEOS).

Recently, several video encryption algorithms have been proposed, which are applied to H.264 compressed bitstreams. Although these algorithms offer many desired advantages compared to conventional ones, they do not consider regions of interest. These regions may need better protection, may be the only regions that need protection, depending on the specific application. In this work an effective face protection scheme including state-of-the-art face detection methods and novel simultaneous CABAC and residual coefficients (RCs) encryption is presented, with an emphasis on encryption aspect. The goal is to identify and encrypt regions corresponding to human faces in a video sequence. Simulation results demonstrate the effectiveness of the proposed scheme.

Location-based services have already been widely used in many different areas. With the popularization of intelligent terminals, providing mobile internet services on the cloud have enormous commercial prospects. However, the high adhesion degree of mobile terminals to users not only brings facility but also results in the risk of privacy leak. The paper emphasized the necessity and advantages to provide mobile internet services based on cloud computing technology, analyzed the security issues of location privacy to LBS system brought by mobile cloud computing, and proposed the framework and implement method of LBS system under mobile cloud computing environment.