A Manipulation Prevention Model for Blockchain-Based E-Voting Systems

Security and trust are seen as the most important issues in electronic voting systems. Therefore, it is necessary to use cryptographic procedures to ensure anonymity, security, privacy, and reliability in these systems. In recent years, blockchain has become one of the most commonly used methods for securing data storage and transmission through decentralized applications. E-voting is one of these application areas. However, data manipulation is still seen as a major potential problem in e-voting systems. In theproposed model, administrators or miners are prevented from previewing election results which are normally accessible data due to the blockchain structure. A double-layer encryption model is proposed and tested to prevent manipulations that may occur with the election results. It is ensured that the election results can be counted after the participation of all stakeholders at the end. In this way, potential manipulations may be prevented during the election period. As a result of the model, the privacy of voters is ensured, no central authority is needed, and the recorded votes are kept in a distributed structure

A Systematic Review of Challenges and Opportunities of Blockchain for E-Voting

A blockchain is a distributed, digitized and consensus-based secure information storage mechanism. The present article provides an overview of blockchain based e-voting systems. The primary purpose of this review is to study the up-to-date state of blockchain-based voting research along with associated possible challenges while aiming to forecast future directions. The methodology applied in the review is a systematic review approach. Following an introduction to the basic structure and features of the blockchain in relation to e-voting, we provide a conceptual description of the desired blockchain-based e-voting application. Symmetrical and asymmetrical cryptography improvements play a key role in developing blockchain systems. We have extracted and reviewed 63 research papers from scientific databases that have advised the adoption of the blockchain framework to voting systems. These articles indicate that blockchain-supported voting systems may provide different solutions than traditional e-voting. We classified the main prevailing issues into the five following categories: general, integrity, coin-based, privacy and consensus. As a result of this research, it was determined that blockchain systems can provide solutions to certain problems that prevail in current election systems. On the other hand, privacy protection and transaction speed are most frequently emphasized problems in blockchain applications. Security of remote participation and scalability should be improved for sustainable blockchain based e-voting. It was concluded that frameworks needed enhancements in order to be used in voting systems due to these reservations

Using a Subtractive Center Behavioral Model to Detect Malware

In recent years, malware has evolved by using different obfuscation techniques; due to this evolution, the detection of malware has become problematic. Signature-based and traditional behavior-based malware detectors cannot effectively detect this new generation of malware. This paper proposes a subtractive center behavior model (SCBM) to create a malware dataset that captures semantically related behaviors from sample programs. In the proposed model, system paths, where malware behaviors are performed, and malware behaviors themselves are taken into consideration. This way malicious behavior patterns are differentiated from benign behavior patterns. Features that could not exceed the specified score are removed from the dataset. The datasets created using the proposed model contain far fewer features than the datasets created by n-gram and other models that have been used in other studies. The proposed model can handle both known and unknown malware, and the obtained detection rate and accuracy of the proposed model are higher than those of the known models. To show the effectiveness of the proposed model, 2 datasets with score and without score are created by using SCBM. In total, 6700 malware samples and 3000 benign samples are tested. The results are compared with those derived from n-gram and models from other studies in the literature. The test results show that, by combining the proposed model with an appropriate machine learning algorithm, the detection rate, false positive rate, and accuracy are measured as 99.9%, 0.2%, and 99.8%, respectively