Biblio

Verifiability is one of the parameters in e-voting that can increase confidence in voting technology with several parties ensuring that voters do not change their votes. Voting has become an important part of the democratization system, both to make choices regarding policies, to elect representatives to sit in the representative assembly, and to elect leaders. the more voters and the wider the distribution, the more complex the social life, and the need to manage the voting process efficiently and determine the results more quickly, electronic-based voting (e-Voting) is becoming a more promising option. The level of confidence in voting depends on the capabilities of the system. E-voting must have parameters that can be used as guidelines, which include the following: Accuracy, Invulnerability, Privacy and Verifiability. The implementation of the simple verifiability metric to measure the degree of verifiability in the e-voting protocol, the researchers can calculate the degree of verifiability in the e-voting protocol and the researchers have been able to assess the proposed e-voting protocol with the standard of the best degree of verifiability is 1, where the value of 1 is is absolutely verified protocol.

Malware is one of the threats to information security that continues to increase. In 2014 nearly six million new malware was recorded. The highest number of malware is in Trojan Horse malware while in Adware malware is the most significantly increased malware. Security system devices such as antivirus, firewall, and IDS signature-based are considered to fail to detect malware. This happens because of the very fast spread of computer malware and the increasing number of signatures. Besides signature-based security systems it is difficult to identify new methods, viruses or worms used by attackers. One other alternative in detecting malware is to use honeypot with machine learning. Honeypot can be used as a trap for packages that are suspected while machine learning can detect malware by classifying classes. Decision Tree and Support Vector Machine (SVM) are used as classification algorithms. In this paper, we propose architectural design as a solution to detect malware. We presented the architectural proposal and explained the experimental method to be used.