IoT data encryption algorithm for security

This research project is about encryption simulation for IoT data. It is important to enhance the security system when sending and receiving the IoT data. Some of these data, especially the health information for a particular person is very sensitive. Therefore, there is a need to encrypt and protect the data from malicious attack. The technique proposed in this research is using Hash function and encryption method to protect the data. To show the working of the encryption, a simulation is performed. The simulation used is a MATLAB coding. By inserting the number of bit and size of the data with random plain text, the system is able to encrypt the data. The simulation results showing that the encrypted data is completely different from the original data or the data haven't encrypted. Upon encrypted, the data being protected and will be unknown to the malicious. At the end of this research project, the result concluded that the waveforms will show the encryption process

Digital Signature Security in Data Communication

Authenticity of access in very information are very important in the current era of Internet-based technology, there are many ways to secure information from irresponsible parties with various security attacks, some of technique can use for defend attack from irresponsible parties are using steganography, cryptography or also use digital signatures. Digital signatures could be one of solution where the authenticity of the message will be verified to prove that the received message is the original message without any change, Ong-Schnorr-Shamir is the algorithm are used in this research and the experiment are perform on the digital signature scheme and the hidden channel scheme.Comment: 6 pages, Paper presented at the International Conference on Education and Technology (ICEduTech2017), Novotel Hotel, Balikpapan, Indonesi

Strengthening Data Security: an Holistic Approach

In the light of heightened concern around data security, this paper highlights some of the measures that can be used to develop and strengthen security in data archiving. The paper includes discussion of the different approaches that can be taken towards the construction of firm and resilient data and information security policies within the social science data archiving communities. While international standards can provide theoretical guidelines for the construction of such a policy, procedures need to be informed by more practical considerations. Attention is drawn to the necessity of following a holistic approach to data security, which includes the education of data creators in the reduction of disclosure risk, the integration of robust and appropriate data processing, handling and management procedures, the value of emerging technological solutions, the training of data users in data security, and the importance of management control, as well as the need to be informed by emerging government security and digital preservation standards

Secret Sharing for Cloud Data Security

Cloud computing helps reduce costs, increase business agility and deploy solutions with a high return on investment for many types of applications. However, data security is of premium importance to many users and often restrains their adoption of cloud technologies. Various approaches, i.e., data encryption, anonymization, replication and verification, help enforce different facets of data security. Secret sharing is a particularly interesting cryptographic technique. Its most advanced variants indeed simultaneously enforce data privacy, availability and integrity, while allowing computation on encrypted data. The aim of this paper is thus to wholly survey secret sharing schemes with respect to data security, data access and costs in the pay-as-you-go paradigm

Detecting periodic subsequences in cyber security data

Statistical approaches to cyber-security involve building realistic probability models of computer network data. In a data pre-processing phase, separating automated events from those caused by human activity should improve statistical model building and enhance anomaly detection capabilities. This article presents a changepoint detection framework for identifying periodic subsequences of event times. The opening event of each subsequence can be interpreted as a human action which then generates an automated, periodic process. Difficulties arising from the presence of duplicate and missing data are addressed. The methodology is demonstrated using authentication data from the computer network of Los Alamos National Laboratory.Comment: 31 pages, 10 Figure