With the boom of web and social networking, the amount of generated text data has increased enormously. Much of this data can be considered and modeled as a stream and the volume of such data necessitates the application of automated text classification strategies. Although streaming data classification is not new, considering text data streams for classification purposes has been extensively researched only recently. Before applying any classification method in text data streams, it is imperative that we apply them for existing well-known non-stream text data sets and evaluate their performance. One of the many characteristics of text data that has been pursued for research is its multi-labelity. A single text document may cover multiple class-labels at the same time and hence gives rise to the concept of multi-labelity. From classification perspective, an immediate drawback of such a characteristic is that traditional binary or multi-class classification techniques perform poorly on multi-label text data. In this paper, we extend our previously formulated SISC (Semi-supervised Impurity based Subspace Clustering) [1] approach and its multi-label variation SISC-ML [2]. We call this new algorithm H-SISC (Hierarchical SISC). H-SISC captures the underlying correlation that exists between each pair of class labels in a multi-label environment. Developing a robust multi-label classifier will allow us to apply such a model in classifying streaming text data more effectively. We have experimented with well known text data sets and empirical evaluation on these real world multi-label NASA ASRS (Aviation Safety Reporting System), Reuters and 20 Newsgroups data sets reveals that our proposed approach outperforms other state-of-the-art text classification as well as subspace clustering algorithms.

Evaluation of network security is an essential step in securing any network. This evaluation can help security professionals in making optimal decisions about how to design security countermeasures, to choose between alternative security architectures, and to systematically modify security configurations in order to improve security. However, the security of a network depends on a number of dynamically changing factors such as emergence of new vulnerabilities and threats, policy structure and network traffic. Identifying, quantifying and validating these factors using security metrics is a major challenge in this area. In this paper, we propose a novel security metric framework that identifies and quantifies objectively the most significant security risk factors, which include existing vulnerabilities, historical trend of vulnerability of the remotely accessible services, prediction of potential vulnerabilities for any general network service and their estimated severity and finally policy resistance to attack propagation within the network. We then describe our rigorous validation experiments using real- life vulnerability data of the past 6 years from National Vulnerability Database (NVD) [10] to show the high accuracy and confidence of the proposed metrics. Some previous works have considered vulnerabilities using code analysis. However, as far as we know, this is the first work to study and analyze these metrics for network security evaluation using publicly available vulnerability information and security policy configuration.

Security of a network depends on a number of dynamically changing factors. These include emergence of new vulnerabilities and threats, policy structure and network traffic. Due to the dynamic nature of these factors, identifying security metrics that measure objectively the quality of security configuration pose a major challenge. Moreover, this evaluation must be done dynamically to handle real time changes in the threat toward the network. In this paper, we extend our security metric framework [2] that identifies and quantifies objectively the most significant security risk factors, which include existing vulnerabilities, historical trend of vulnerabilities of remotely accessible services, prediction of potential vulnerabilities for any general network service and their estimated severity and finally propagation of an attack within the network. We have implemented this framework as a user-friendly tool called Risk based prOactive seCurity cOnfiguration maNAger (ROCONA) and showed how this tool simplifies security configuration management using risk measurement and mitigation.