Posts by users on microblogs such as Twitter provide diverse real-time updates to major events. Unfortunately, not all the information are credible. Previous works that assess the credibility of information in Twitter have focused on extracting features from the Tweets. In this work, we present an interactive framework called iFACT for assessing the credibility of claims from tweets. The proposed framework collects independent evidence from web search results (WSR) and identify the dependencies between claims. It utilizes features from the search results to determine the probabilities that a claim is credible, not credible or inconclusive. Finally, the dependencies between claims are used to adjust the likelihood estimates of a claim being credible, not credible or inconclusive. iFACT allows users to be engaged in the credibility assessment process by providing feedback as to whether the web search results are relevant, support or contradict a claim. Experiment results on multiple real world datasets demonstrate the effectiveness of WSR features and its ability to generalize to claims of new events. Case studies show the usefulness of claim dependencies and how the proposed approach can give explanation to the credibility assessment process.

IP traceback plays an important role in cyber investigation processes, where the sources and the traversed paths of packets need to be identified. It has a wide range of applications, including network forensics, security auditing, network fault diagnosis, and performance testing. Despite a plethora of research on IP traceback, the Internet is yet to see a large-scale practical deployment of traceback. Some of the major challenges that still impede an Internet-scale traceback solution are, concern of disclosing Internet Service Provider (ISP's) internal network topologies (in other words, concern of privacy leak), poor incremental deployment, and lack of incentives for ISPs to provide traceback services. In this paper, we argue that cloud services offer better options for the practical deployment of an IP traceback system. We first present a novel cloud-based traceback architecture, which possesses several favorable properties encouraging ISPs to deploy traceback services on their networks. While this makes the traceback service more accessible, regulating access to traceback service in a cloud-based architecture becomes an important issue. Consequently, we address the access control problem in cloud-based traceback. Our design objective is to prevent illegitimate users from requesting traceback information for malicious intentions (such as ISPs topology discovery). To this end, we propose a temporal token-based authentication framework, called FACT, for authenticating traceback service queries. FACT embeds temporal access tokens in traffic flows, and then delivers them to end-hosts in an efficient manner. The proposed solution ensures that the entity requesting for traceback service is an actual recipient of the packets to be traced. Finally, we analyze and validate the proposed design using real-world Internet data sets.

IP traceback is a solution for attributing cyber attacks, and it is also useful for accounting user traffic and network diagnosis. Marking-based traceback (MBT) has been considered a promising traceback approach, and has received considerable attention. However, we find that the traceback message delivery problem in MBT, which is important to the successful completion of a traceback, has not been adequately studied in the literature. To address this issue, we present the design, analysis, and evaluation of opportunistic piggyback marking (OPM) for IP traceback in this paper. The OPM distinguishes itself from the existing works by decoupling the traceback message content encoding and delivery functions in MBT, and efficiently achieves expedited and robust traceback message delivery by exploiting piggyback marking opportunities. Based on the proposed OPM scheme, we then present the flexible marking-based traceback framework, which is a novel design paradigm for IP traceback and has several favorable features for practical deployment of IP traceback. Through the numerical analysis and the comprehensive simulation evaluations, we demonstrate that our design effectively reduces the traceback completion delay and router processing overhead, and increases the message delivery ratio compared with other baseline approaches.

The vast variety of information on Web forums makes them a valuable resource for various purposes such as scam detection, national security protection and sentiment analysis. However, it is challenging to extract useful information from Web forums accurately and efficiently. First, several page types exist in Web forums and content is presented in different formats in these pages. Second, the content on the forum pages is stored in the form of data blocks. For the information to be meaningful, it is necessary to extract the relevant data blocks separately. The main problem with generic content extraction systems is that they cannot distinguish among various pages nor extract information with the required granularity. Although, several content extraction methods exist for Web forums, these methods either do not satisfy the above requirements or use heuristics based approaches (such as assumptions on standard visual appearances, etc., resulting in limited applicability to different varieties of forum). In this paper, we propose a general and efficient content extraction method using the properties of links present in forum pages. The effectiveness of our proposed method is shown through our experimental results.

For the classification of logo images, there are significant challenges in the classification of merchandise logos such that only a few key points can be found in the relatively small logo images due to large variations in texture, poor illumination and generally, lack of discriminative features. This paper addresses these difficulties by introducing an integrated approach to classify merchandise logos with the combination of local edge-based descriptor-DAISY, spatial histogram and salient region detection. During the training phase, after carrying out the edge extraction, merchandise logos are described with a set of SIFT-like DAISY descriptors which is computed efficiently and densely along edge pixels. Visual word vocabulary generation and spatial histogram are used for describing the images/regions. Saliency map for object detection is adopted to narrow down and localize the logos. The feature map for approximating a non-linear kernel is also used to facilitate the classification by a linear SVM classifier. The experimental results demonstrate that the Edge-based DAISY (EDAISY) descriptor outperforms the state-of-the-art SIFT and DSIFT descriptors in terms of classification accuracy on a set of collected logo image dataset.

Web forums have become a major source of information gathering/mining due to a large amount of user generated content. Crawling of Web forums is necessary to gather/mine the information from them. However, a generic Web crawler is unable to efficiently and effectively crawl the Web forums because of the existence of many redundant and duplicate pages. In addition, there exists a crawling relationship among the useful pages that need to be considered. So, for efficient crawling, we need to intelligently crawl the Web forums by eliminating redundant and duplicate pages, and understanding the crawling relationship. Existing works in forum crawling use visual pattern recognition based methods, which make them extremely computational expensive. In this paper, we propose a novel light-weight crawling method using text and links properties of the pages in Web forums. Theoretical analysis and experimental results show the effectiveness and efficiency of the proposed method.