In today's world, Botnet has become one of the greatest threats to network security. Network attackers, or Botmasters, use Botnet to launch the Distributed Denial of Service (DDoS) to paralyze large-scale websites or steal confidential data from infected computers. They also employ "phishing" attacks to steal sensitive information (such as users' accounts and passwords), send bulk email advertising, and/or conduct click fraud. Even though detection technology has been much improved and some solutions to Internet security have been proposed and improved, the threat of Botnet still exists. Most of the past studies dealing with this issue used either packet contents or traffic flow characteristics to identify the invasion of Botnet. However, there still exist many problems in the areas of packet encryption and data privacy, simply because Botnet can easily change the packet contents and flow characteristics to circumvent the Intrusion Detection System (IDS). This study combines Particle Swarm Optimization (PSO) and K-means algorithms to provide a solution to remedy those problems and develop, step by step, a mechanism for Botnet detection. First, three important network behaviors are identified: long active communication behavior (ActBehavior), connection failure behavior (FailBehavior), and network scanning behavior (ScanBehavior). These behaviors are defined according to the relevant prior studies and used to analyze the communication activities among the infected computers. Second, the features of network behaviors are extracted from the flow traces in the network layer and transport layer of the network equipment. Third, PSO and K-means techniques are used to uncover the host members of Botnet in the organizational network. This study mainly utilizes the flow traces of a campus network as an experiment. The experimental findings show that this proposed approach can be employed to detect the suspicious Botnet members earlier than the detection application systems. In addition, this proposed approach is easy to implement and can be further used and extended in the campus dormitory network, home networks, and the mobile 3G network. . 2015. A network behavior-based botnet detection mechanism using PSO and K-means.

In addition to the rapid development of global information and communications technology (ICT) and the Internet, recent rapid growth in cloud computing technology represents another important trend. Individual continuance intention towards information technology is a critical area in which information systems research can be performed. This study aims to develop an integrated model designed to explain and predict an individual’s continuance intention towards personal cloud services based on the concepts of technology readiness (TR) and the unified theory of acceptance and use of technology 2 (UTAUT2), moderated by gender, age, and experience of personal cloud services. The key results of the partial least square test largely support the proposed model’s validity and the significant impact of effort expectancy, social influence, hedonic motivation, price value, habit, and technology readiness on continuance intention towards personal cloud services. In addition to providing symmetric theoretical support with the proposed model and transforming the individual characteristics of TR into UTAUT2, this study could be used to enhance and analyze users’ adoption of personal cloud services and also increase the symmetry of the model’s explanation and prediction. The findings from this research contribute to providing practical implications and academic resources as well as improving our understanding of personal cloud service applications.

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Log management and log auditing have become increasingly crucial for enterprises in this era of information and technology explosion. The log analysis technique is useful for discovering possible problems in business processes and preventing illegal-intrusion attempts and data-tampering attacks. Because of the complexity of the dynamically changing environment, auditing a tremendous number of data is a challenging issue. We provide a real-time audit mechanism to improve the aforementioned problems in log auditing. This mechanism was developed based on the Lempel-Ziv-Welch (LZW) compression technique to facilitate effective compression and provide reliable auditing log entries. The mechanism can be used to predict unusual activities when compressing the log data according to pre-defined auditing rules. Auditors using real-time and continuous monitoring can perceive instantly the most likely anomalies or exceptions that could cause problems. We also designed a user interface that allows auditors to define the various compression and audit parameters, using real log cases in the experiment to verify the feasibility and effectiveness of this proposed audit mechanism. In summary, this mechanism changes the log access method and improves the efficiency of log analysis. This mechanism greatly simplifies auditing so that auditors must only trace the sources and causes of the problems related to the detected anomalies. This greatly reduces the processing time of analytical audit procedures and the manual checking time, and improves the log audit efficiency.

With the open domestic financial market, the targets of investment and money management are toward diversity. The competition from internationalization makes the stock market no more flourishing as usual. The risk of margin trading becomes important information that securities firms try to analyze and get controlled. According to current regulations and working process, this study constructs an executive information system with the application of data warehouse and online analytical processing (OLAP) to help securities brokers make decisions in the operation of risk management for margin purchase and short sale of securities. The result solves the problem that managers of margin trading usually face when using traditional account systems.

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