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The voting process is an essential part of the democratic system. As the size and breadth of voter distribution grow, the social aspects become more complex and the need to manage the voting process more efficiently and more quickly increases. This makes electronic voting (e-voting) a more interesting alternative voting technology. The voting confidence level highly depends on the ability of the system to protect the votes until the end of the process. Parameters in e-voting consist of accuracy, invulnerability, privacy, and verifiability, where verifiability is currently the most important factor in attempts to further improve the quality of e-voting technology. The availability of verifiability properties gives the voters confidence that the voting system used provides protection, both to the votes cast and to the identity of the voters themselves. Individual verifiability refers to the verifiability properties of the voting system that should be able to accommodate the needs of the voters. In this paper, an e-voting system that accommodates the individual verifiability requirements and a method for measuring the degree of individual verifiability are proposed. [ABSTRACT FROM AUTHOR]

An Intrusion Detection System (IDS) which implement a group-based classification algorithm, theoretically has the benefit of higher accuracy. Unfortunately, higher accuracy only achieved if the observed group is homogeneous from a certain distribution. Recently, a distributed denial of service (DDoS) attack consists of multiple botnets which produce multi types of traffic in one attack session. It makes the IDS suffers from decreasing accuracy as the increasing heterogeneity within the observed group. To address the problem, we propose homogeneous grouping algorithm based on triangle area Mahalanobis distance to support IDS which implement group-based data analysis. First, the Mahalanobis distance measurement was used to construct homogeneous groups. Then, the covariance matrix of each homogeneous group was classified using a decision tree classifier. Classification performance was evaluated using known KDDCup 99 dataset. The results pointed out that the used of homogeneous grouping algorithm improve the classification performance for natural and mixed random DDoS traffic. [ABSTRACT FROM AUTHOR]