Fast-flux service networks (FFSNs), broadly used by botnets, are an evasive technique for conducting malicious behavior via rapid activities. FFSN detection easily fails in the case of poor performance and causes a high incidence of false positives due to the similarity of an FFSN to a content distribution network (CDN), a normal behavior for load balance. In this study, we propose a localized spatial geolocation detection (LSGD) system for identifying FFSNs in real time. We believe that the grid distribution of LSGD possesses a precise spatial locating capability for profiling the spatial relations between IP address resolutions. Furthermore, autonomous system numbers (ASNs) are used for enhancing localized geographic characteristics. The proposed system, incorporating LSGD, ASNs, and the domain name system (DNS), can respond well to identify potential FFSNs. The results of our experiment show that the proposed LSGD system has a better detection capability than state-of-the-art spatial or temporal detection approaches, with a lower false positive rate in real-time detection than the approach based on a spatial snapshot alone.
A CAPTCHA(Completely Automated Public Turing test to tell Computers and Humans Apart) is a security mechanism that can be used to distinguish between humans and machines. Most existing CAPTCHA systems are vulnerable against a so-called "third party human attack." The third party human attack employs hired human to solve challenges so that the CAPTCHA systems will no longer be effective. In this paper, we design an efficient and effective aspect to defend the attack. Following the aspect, we design and analyze a novel CAPTCHA system, Drag-n-Drop Interactive Masking CAPTCHA (DDIM CAPTCHA), to deal with both the traditional attacks and the third party human attack. The DDIM CAPTCHA retains the basic requirements of CAPTCHAs and adds the properties of interaction and masking. Through a series of analyses and experiments, the proposed Drag-nDrop CAPTCHA can be claimed to be a good approach for deployment to remedy the weaknesses of present CAPTCHA systems.Keywords-third party human attack; CAPTCHA; drag and drop.
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