Abstract:
With the advent of Web 2.0, the Web is acting as a platform which enables end-user content generation. As a major type of social media in Web 2.0, Web forums facilitate intensive interactions among participants. International Jihadist groups often use Web forums to promote violence and distribute propaganda materials. These Dark Web forums are heterogeneous and widely distributed. Therefore, how to access and analyze the forum messages and interactions among participants is becoming an issue. This paper presents a general framework for Web forum data integration. Specifically, a Web-based knowledge portal, the Dark Web Forums Portal, is built based on the framework. The portal incorporates the data collected from different international Jihadist forums and provides several important analysis functions, including forum browsing and searching (in single forum and across multiple forums), forum statistics analysis, multilingual translation, and social network visualization. Preliminary results of our user study show that the Dark Web Forums Portal helps users locate information quickly and effectively. Users found the forum statistics analysis, multilingual translation, and social network visualization functions of the portal to be particularly valuable. ©2009 IEEE.
Abstract:
While e-government is propelling and maturing steadily, advanced technological capabilities alone cannot guarantee agencies' realizing the full benefits of the enabling computer-based systems. This study analyzes information systems in e-government settings by examining agencies' satisfaction with an electronic record management system (ERMS). Specifically, we investigate key satisfaction determinants that include regulatory compliance, job relevance, and satisfaction with support services for using the ERMS. We test our model and the hypotheses in it, using a large-scale survey that involves a total of 1,652 government agencies in Taiwan. Our results show significant effects of regulatory compliance on job relevance and satisfaction with support services, which in turn determine government agencies' satisfaction with an ERMS. Our data exhibit a reasonably good fit to our model, which can explain a significant portion of the variance in agencies' satisfaction with an ERMS. Our findings have several important implications to research and practice, which are also discussed. © 2009 Springer Berlin Heidelberg.
Abstract:
Based on the available crime and intelligence knowledge, federal, state, and local authorities can make timely and accurate decisions to select effective strategies and tactics as well as allocate the appropriate amount of resources to detect, prevent, and respond to future attacks. Facing the critical mission of international security and various data and technical challenges, there is a pressing need to develop the science of security informatics. The main objective is the development of advanced information technologies, systems, algorithms, and databases for security-related applications using an integrated technological, organizational, and policy-based approach. Intelligent systems have much to contribute for this emerging field. © 2010 IEEE.
Abstract:
Fake websites have become increasingly pervasive, generating billions of dollars in fraudulent revenue at the expense of unsuspecting Internet users. The design and appearance of these websites makes it difficult for users to manually identify them as fake. Automated detection systems have emerged as a mechanism for combating fake websites, however most are fairly simplistic in terms of their fraud cues and detection methods employed. Consequently, existing systems are susceptible to the myriad of obfuscation tactics used by fraudsters, resulting in highly ineffective fake website detection performance. In light of these deficiencies, we propose the development of a new class of fake website detection systems that are based on statistical learning theory (SLT). Using a design science approach, a prototype system was developed to demonstrate the potential utility of this class of systems. We conducted a series of experiments, comparing the proposed system against several existing fake website detection systems on a test bed encompassing 900 websites. The results indicate that systems grounded in SLT can more accurately detect various categories of fake websites by utilizing richer sets of fraud cues in combination with problem-specific knowledge. Given the hefty cost exacted by fake websites, the results have important implications for E-commerce and online security.
Abstract:
Social media is frequently used as a platform for the exchange of information and opinions as well as propaganda dissemination. But online content can be misused for the distribution of illicit information, such as violent postings in web forums. Illicit content is highly distributed in social media, while non-illicit content is unspecific and topically diverse. It is costly and time consuming to label a large amount of illicit content (positive examples) and non-illicit content (negative examples) to train classification systems. Nevertheless, it is relatively easy to obtain large volumes of unlabeled content in social media. In this article, an artificial immune system-based technique is presented to address the difficulties in the illicit content identification in social media. Inspired by the positive selection principle in the immune system, we designed a novel labeling heuristic based on partially supervised learning to extract high-quality positive and negative examples from unlabeled datasets.The empirical evaluation results from two large hate group web forums suggest that our proposed approach generally outperforms the benchmark techniques and exhibits more stable performance. © 2011 ASIS&T.
