The management of data while maintaining its utility and preservation of security scheme is a matter of concern for the cloud owner. In order to minimize the overhead at cloud service provider of applying security over each document and then send it to the client, we propose a layered architecture. This approach maintains security of the sensitive document and privacy of its data sensitivity. To make a balance between data security and utility, the proposed approach categorizes the data according to its sensitivity. Perseverance of various categorization requires different algorithmic schemes. We set up a cloud distributed environment where data is categorized into four levels of sensitivity; public, confidential, secret, top secret and a different approach has been used to preserve the security at each level. At the most sensitive layers i.e. secret and top secret data, we made a provision to detect the faulty node that is responsible for data leakage. Finally, experimental analysis is carried out to analyze the performance of the layered approach. The experimental results show that time taken (in ms) in processing 200 documents of size 20 MB is 437, 2239, 3142, 3900 for public, confidential, secret and top secret data respectively when the documents are distributed among distinct users, which proves the practicality of the proposed approach.

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Layer-based privacy and security architecture for cloud data sharing

A large number of researchers, academia, government sectors, and business enterprises are adopting the cloud environment due to the least upfront capital investment, maximum scalability, and several other features of it. Despite the multiple features supported by the cloud environment, it also suffers several challenges. Data protection is the primary concern in the area of information security and cloud computing. Numerous solutions have been developed to address this challenge. However, there is a lack of comprehensive analysis among the existing solutions and a necessity emerges to explore, classify, and analyze the significant existing work for investigating the applicability of these solutions to meet the requirements. This article presents a comparative and systematic study, and in-depth analysis of leading techniques for secure sharing and protecting the data in the cloud environment. The discussion about each dedicated technique includes: functioning for protecting the data, potential and revolutionary solutions in the domain, the core and adequate information including workflow, achievements, scope, gaps, future directions, etc. about each solution. Furthermore, a comprehensive and comparative analysis of the discussed techniques is presented. Afterward, the applicability of the techniques is discussed as per the requirements and the research gaps along with future directions are reported in the field. The authors believe that this article’s contribution will operate as a catalyst for the potential researchers to carry out the research work in the area.

/pdf/secure-data-storage-and-sharing-techniques-for-data-3np3fd5n.pdf

Secure Data Storage and Sharing Techniques for Data Protection in Cloud Environments: A Systematic Review, Analysis, and Future Directions

Data sharing across multiple different entities is on-demand to upgrade an enterprise's performance. However, some malicious entity can reveal this data to an unauthorised third party that may result in heavy loss to the enterprises in terms of finance, reputation, and long-term stability. This study presents a novel model GUIM-SMD for the identification of the guilty entity which is responsible for the data leakage to the unauthorised party in the shared environment. An effective distribution strategy to allocate the data among the users based on the access control mechanism is proposed in this model. The approach introduces the summation matrix which is computed using D-score and U-score that are assigned to the classified data and user, correspondingly. Furthermore, D-score and U-score are based on the data sensitivity and user guilty record relatively; and their values vary between 0 and 1. The evaluated summation matrix is used for data distribution among various users. The results show improvement up to 98.74, 236.38, and 252.39% for average probability, average success rate, and detection rate, respectively, as compared to the prior work.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ifs.2019.0203

GUIM-SMD: guilty user identification model using summation matrix-based distribution

The data is an important asset of an organization and it is essential to keep this asset secure. It requires security in whatever state is it i.e. data at rest, data in use, and data in transit. There is a need to pay more attention to it when the third party is included i.e. when the data is stored in the cloud then it requires more security. Since confidential data can reside on a variety of computing devices (physical servers, virtual servers, databases, file servers, PCs, point-of-sale devices, flash drives, and mobile devices) and move through a variety of network access points (wireline, wireless, VPNs, etc.), there is a need of solutions or mechanism that can tackle the problem of data loss, data recovery and data leaks. In this context, the paper presents a holistic view of data protection for sharing and communicating environments for any type of organization. A taxonomy of data leakage protection systems and major challenges faced while protecting confidential data are discussed. Data protection solutions, Data Leakage Protection System's analysis techniques, and, a thorough analysis of existing state-of-the-art contributions empowering machine learning-based approaches are entailed. Finally, the paper explores and concludes various critical emerging challenges and future research directions concerning data protection.

A Holistic View on Data Protection for Sharing, Communicating, and Computing Environments: Taxonomy and Future Directions

Sensitive data leakage is the major growing problem being faced by enterprises in this technical era. Data leakage causes severe threats for organization of data safety which badly affects the reputation of organizations. Data leakage is the flow of sensitive data/information from any data holder to an unauthorized destination. Data leak prevention (DLP) is set of techniques that try to alleviate the threats which may hinder data security. DLP unveils guilty user responsible for data leakage and ensures that user without appropriate permission cannot access sensitive data and also provides protection to sensitive data if sensitive data is shared accidentally. In this paper, data leakage prevention (DLP) model is used to restrict/grant data access permission to user, based on the forecast of their access to data. This study provides a DLP solution using data statistical analysis to forecast the data access possibilities of any user in future based on the access to data in the past. The proposed approach makes use of renowned simple piecewise linear function for learning/training to model. The results show that the proposed DLP approach with high level of precision can correctly classify between users even in cases of extreme data access.

A Forecasting-Based DLP Approach for Data Security

In the field of wireless networks, Mobile Ad hoc Network (MANET) is one of the most unique applications due to mobility and scalability. As it is vulnerable to malicious attackers with the open medium and wide distribution, network performance is degraded with the behavior attacks in MANET’s, which makes protection most important. In any ad hoc networks, one of the situations is that, when a data distributor possesses sensitive data destined to a set of supposedly trusted parties, some data can be leaked at an authorized place. With the introduction of fake objects the data allocation strategies gives a path in identifying leakages ensuring more security with encryption. In this context, light weight system is proposed to characterize the data loss and to minimize the performance degradation in such networks. The light weight system includes the concept of cryptography and routing protocol implementation at different stages of data transfer. A social network is taken as a challenging issue to achieve the proposed scheme.

An Approach to Identify Data Leakage in Secure Communication

Data pass in any network system through packets to perform communication. On these packets, an intruder may try to perform eavesdropping that leads to information loss. Generally, Packet dropping and modification are common attacks that can be launched by an adversary to disrupt communication in multi hop networks. To compensate such data loss, many schemes have been proposed to identify such attacks. Out of which very few techniques were able to identify such type of intruders in an effective manner. A systematic approach is proposed to locate and identify packet droppers and modifiers. Hence it has been assumed that the nodes continuously monitor the forwarding behaviors of their neighbors to determine if their neighbors are misbehaving. To address this problem, an effective method is designed which can identify misbehaving forwarders that drop or modify packet with a hierarchical structure. The scheme is focused towards behavioral study of the nodes for the detection of attacks in the network. Extensive analysis and simulations have been conducted to study the performance of attacks with respect to efficiency of the scheme.

http://ieeexplore.ieee.org/iel7/7045158/7054731/07054822.pdf

A behavioral model to identify and analyze anomalous attacks on packet forwarding

Inside a network environment, packets is the most important in carrying data to perform communication. Such a circumstance is easy to be attacked by an intruder and perform eavesdropping which leads to data loss/duplication/redundancy. Comprehend speaking, packet dropping and modification are the two common attacks that can be easily launched by an adversary to disrupt communication in multi hop networks, specifically mobile ad hoc networks. Hence a remedial approach is proposed to compensate such attacks. A tree based approach is designed to designate the attack in order to identify packet droppers and modifiers. In this direction, it has been assumed that the mobile nodes continuously monitor the behaviors of the forwarding mobile nodes which may be neighbors to determine if their neighbors are misbehaving. To address this problem, a hierarchical method is proposed and detects malicious mobile nodes that drop or modify packets. Extensive analysis and simulations have been conducted to study the performance of attacks with respect to efficiency of

/pdf/behavioral-model-to-detect-anomalous-attacks-in-packet-15zjfw064t.pdf

Rajasekhara Rao Kurra

Papers

An Approach to Identify Data Leakage in Secure Communication

A behavioral model to identify and analyze anomalous attacks on packet forwarding

Behavioral Model to Detect Anomalous Attacks in Packet Transmission