Biblio

Encryption defined as change information process (which called plaintext) into an unreadable secret format (which called ciphertext). This ciphertext could not be easily understood by somebody except authorized parson. Decryption is the process to converting ciphertext back into plaintext. Deoxyribonucleic Acid (DNA) based information ciphering techniques recently used in large number of encryption algorithms. DNA used as data carrier and the modern biological technology is used as implementation tool. New encryption algorithm based on DNA is proposed in this paper. The suggested approach consists of three steps (conventional, stream cipher and DNA) to get high security levels. The character was replaced by shifting depend character location in conventional step, convert to ASCII and AddRoundKey was used in stream cipher step. The result from second step converted to DNA then applying AddRoundKey with DNA key. The evaluation performance results proved that the proposed algorithm cipher the important data with high security levels.

Recent developments in deepfake technology are increasing new security threats. To solve these issues, various detection methods have been proposed including the methods utilizing biological signals captured by R-PPG. However, existing methods have limitations in terms of detection accuracy and generalized performance. In this paper, we present our approach for R-PPG-based BPM (Beats Per Minute) analysis for effective deepfake detection. With the selected deepfake datasets, we performed (a) comparison and analysis of conditions for BPM processing, and (b) BPM extraction by dividing the face into 16 regions and comparison of BPM in each region. The results showed that our proposed BPM-related properties are effective in deepfake detection.

The COVID-19 pandemic has impacted the world economy and mainly all activities where social distancing cannot be respected. In order to control this pandemic, screening tests such as PCR have become essential. For example, in the case of a trip, the traveler must carry out a PCR test within 72 hours before his departure and if he is not a carrier of the COVID-19, he can therefore travel by presenting, during check-in and boarding, the negative result sheet to the agent. The latter will then verify the presented sheet by trusting: (a) the medical biology laboratory, (b) the credibility of the traveler for not having changed the PCR result from “positive to negative”. Therefore, this confidence and this verification are made without being based on any mechanism of security and integrity, despite the great importance of the PCR test results to control the COVID-19 pandemic. Consequently, we propose in this paper a blockchain-based decentralized trust architecture that aims to guarantee the integrity, immutability and traceability of COVID-19 test results. Our proposal also aims to ensure the interconnection between several organizations (airports, medical laboratories, cinemas, etc.) in order to access COVID-19 test results in a secure and decentralized manner.

Trust among humans affects the way we interact with each other. In autonomous systems, this trust is often predefined and hard-coded before the systems are deployed. However, when systems encounter unfolding situations, requiring them to interact with others, a notion of trust will be inevitable. In this paper, we discuss trust as a fundamental measure to enable an autonomous system to decide whether or not to interact with another system, whether biological or artificial. These decisions become increasingly important when continuously integrating with others during runtime.

This paper introduces a biologically inspired associative data model and structure for finding nearest neighbors and similar patterns. The method can be used as an alternative to the classical approaches to accelerate the search for such patterns using various priorities for attributes according to the Sebestyen measure. The presented structure, together with algorithms developed in this paper can be useful in various computational intelligence tasks like pattern matching, recognition, clustering, classification, multi-criterion search etc. This approach is particularly useful for the on-line operation of associative neural network graphs. Graphs that dynamically develop their structure during learning on training data. The results of experiments show that the associative approach can substantially accelerate the nearest neighbor search and that associative structures can also be used as a model for KNN tasks. Finally, this paper presents how the associative structures can be used to self-organize data and represent knowledge about them in the associative way, which yields new search approaches described in this paper.

Large graphs are increasingly prevalent in mobile networks, social networks, traffic networks and biological networks. These graphs are often uncertain, where edges are augmented with probabilities that indicates the chance to exist. Recently k-nearest neighbor search has been studied within the field of uncertain graphs, but the scalability and efficiency issues are not well solved. Moreover, solutions are implemented on a single machine and thus cannot fit large uncertain graphs. In this paper, we develop a framework, called DURS, to distribute k-nearest neighbor search into several machines and re-partition the uncertain graphs to balance the work loads and reduce the communication costs. Evaluation results show that DURS is essential to make the system scalable when answering k-nearest neighbor queries on uncertain graphs.

Nowadays the massive amount of data is produced from different sources and lots of applications are processing these data to discover insights. Sometimes we may get unexpected results from these applications and it is not feasible to trace back to the data origin manually to find the source of errors. To avoid this problem, data must be accompanied by the context of how they are processed and analyzed. Especially, data-intensive applications like e-Science always require transparency and therefore, we need to understand how data has been processed and transformed. In this paper, we propose mathematical formulation and implementation of query inversion techniques to trace the provenance of data in a relational database management system (RDBMS). We build mathematical formulations of inverse queries for most of the relational algebra operations and show the formula for join operations in this paper. We, then, implement these formulas of inversion techniques and the experiment shows that our proposed inverse queries can successfully trace back to original data i.e. finding data provenance.

Sybil security threat in vehicular ad hoc networks (VANETs) has attracted much attention in recent times. The attacker introduces malicious nodes with multiple identities. As the roadside unit fails to synchronize its clock with legitimate vehicles, unintended vehicles are identified, and therefore erroneous messages will be sent to them. This paper proposes a novel biologically inspired spider-monkey time synchronization technique for large-scale VANETs to boost packet delivery time synchronization at minimized energy consumption. The proposed technique is based on the metaheuristic stimulated framework approach by the natural spider-monkey behavior. An artificial spider-monkey technique is used to examine the Sybil attacking strategies on VANETs to predict the number of vehicular collisions in a densely deployed challenge zone. Furthermore, this paper proposes the pseudocode algorithm randomly distributed for energy-efficient time synchronization in two-way packet delivery scenarios to evaluate the clock offset and the propagation delay in transmitting the packet beacon message to destination vehicles correctly. The performances of the proposed technique are compared with existing protocols. It performs better over long transmission distances for the detection of Sybil in dynamic VANETs' system in terms of measurement precision, intrusion detection rate, and energy efficiency.