Abstract-An emerging threat vector, embedded malware inside popular document formats, has become rampant since 2008. Owed to its wide-spread use and Javascript support, PDF has been the primary vehicle for delivering embedded exploits. Unfortunately, existing defenses are limited in effectiveness, vulnerable to evasion, or computationally expensive to be employed as an on-line protection system. In this paper, we propose a context-aware approach for detection and confinement of malicious Javascript in PDF. Our approach statically extracts a set of static features and inserts context monitoring code into a document. When an instrumented document is opened, the context monitoring code inside will cooperate with our runtime monitor to detect potential infection attempts in the context of Javascript execution. Thus, our detector can identify malicious documents by using both static and runtime features. To validate the effectiveness of our approach in a realworld setting, we first conduct a security analysis, showing that our system is able to remain effective in detection and be robust against evasion attempts even in the presence of sophisticated adversaries. We implement a prototype of the proposed system, and perform extensive experiments using 18623 benign PDF samples and 7370 malicious samples. Our evaluation results demonstrate that our approach can accurately detect and confine malicious Javascript in PDF with minor performance overhead.
Abstract. Click fraud-malicious clicks at the expense of pay-per-click advertisers-is posing a serious threat to the Internet economy. Although click fraud has attracted much attention from the security community, as the direct victims of click fraud, advertisers still lack effective defense to detect click fraud independently. In this paper, we propose a novel approach for advertisers to detect click frauds and evaluate the return on investment (ROI) of their ad campaigns without the helps from ad networks or publishers. Our key idea is to proactively test if visiting clients are full-fledged modern browsers and passively scrutinize user engagement. In particular, we introduce a new functionality test and develop an extensive characterization of user engagement. Our detection can significantly raise the bar for committing click fraud and is transparent to users. Moreover, our approach requires little effort to be deployed at the advertiser side. To validate the effectiveness of our approach, we implement a prototype and deploy it on a large production website; and then we run 10-day ad campaigns for the website on a major ad network. The experimental results show that our proposed defense is effective in identifying both clickbots and human clickers, while incurring negligible overhead at both the server and client sides.
In a dangling DNS record (Dare), the resources pointed to by the DNS record are invalid, but the record itself has not yet been purged from DNS. In this paper, we shed light on a largely overlooked threat in DNS posed by dangling DNS records. Our work reveals that Dare can be easily manipulated by adversaries for domain hijacking. In particular, we identify three attack vectors that an adversary can harness to exploit Dares. In a large-scale measurement study, we uncover 467 exploitable Dares in 277 Alexa top 10,000 domains and 52 edu zones, showing that Dare is a real, prevalent threat. By exploiting these Dares, an adversary can take full control of the (sub)domains and can even have them signed with a Certificate Authority (CA). It is evident that the underlying cause of exploitable Dares is the lack of authenticity checking for the resources to which that DNS record points. We then propose three defense mechanisms to effectively mitigate Dares with little human effort.
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