This paper focuses on reporting of Internet malicious activity (or mal-activity in short) by public blacklists with the objective of providing a systematic characterization of what has been reported over the years, and more importantly, the evolution of reported activities. Using an initial seed of 22 blacklists, covering the period from January 2007 to June 2017, we collect more than 51 million mal-activity reports involving 662K unique IP addresses worldwide. Leveraging the Wayback Machine, antivirus (AV) tool reports and several additional public datasets (e.g., BGP Route Views and Internet registries) we enrich the data with historical meta-information including geo-locations (countries), autonomous system (AS) numbers and types of mal-activity. Furthermore, we use the initially labelled dataset of ~1.57 million mal-activities (obtained from public blacklists) to train a machine learning classifier to classify the remaining unlabeled dataset of ~44 million mal-activities obtained through additional sources. We make our unique collected dataset (and scripts used) publicly available for further research. The main contributions of the paper are a novel means of report collection, with a machine learning approach to classify reported activities, characterization of the dataset and, most importantly, temporal analysis of mal-activity reporting behavior. Inspired by P2P behavior modeling, our analysis shows that some classes of mal-activities (e.g., phishing) and a small number of mal-activity sources are persistent, suggesting that either blacklist-based prevention systems are ineffective or have unreasonably long update periods. Our analysis also indicates that resources can be better utilized by focusing on heavy mal-activity contributors, which constitute the bulk of mal-activities.

Modern applications are often split into separate client and server tiers that communicate via message passing over the network. One well-understood threat to privacy for such applications is the leakage of sensitive user information either in transit or at the server. In response, an array of defensive techniques have been developed to identify or block unintended or malicious information leakage. However, prior work has primarily considered privacy leaks originating at the client directed at the server, while leakage in the reverse direction -- from the server to the client -- is comparatively under-studied. The question of whether and to what degree this leakage constitutes a threat remains an open question. We answer this question in the affirmative with Hush, a technique for semi-automatically identifying Server-based InFormation OvershariNg (SIFON) vulnerabilities in multi-tier applications. In particular, the technique detects SIFON vulnerabilities using a heuristic that overshared sensitive information from server-side APIs will not be displayed by the application's user interface. The technique first performs a scalable static program analysis to screen applications for potential vulnerabilities, and then attempts to confirm these candidates as true vulnerabilities with a partially-automated dynamic analysis. Our evaluation over a large corpus of Android applications demonstrates the effectiveness of the technique by discovering several previously-unknown SIFON vulnerabilities in eight applications.

Internet censorship is enforced by numerous governments worldwide, however, due to the lack of publicly available information, as well as the inherent risks of performing active measurements, it is often hard for the research community to investigate censorship practices in the wild. Thus, the leak of 600GB worth of logs from 7 Blue Coat SG-9000 proxies, deployed in Syria to filter Internet traffic at a country scale, represents a unique opportunity to provide a detailed snapshot of a real-world censorship ecosystem.

Users' anonymity and privacy are among the major concerns of today's Internet. Anonymizing networks are then poised to become an important service to support anonymous-driven Internet communications and consequently enhance users' privacy protection. Indeed, Tor an example of anonymizing networks based on onion routing concept attracts more and more volunteers, and is now popular among dozens of thousands of Internet users. Surprisingly, very few researches shed light on such an anonymizing network. Beyond providing global statistics on the typical usage of Tor in the wild, we show that Tor is actually being is-used, as most of the observed traffic belongs to P2P applications. In particular, we quantify the BitTorrent traffic and show that the load of the latter on the Tor network is underestimated because of encrypted BitTorrent traffic (that can go unnoticed). Furthermore, this paper provides a deep analysis of both the HTTP and BitTorrent protocols giving a complete overview of their usage. We do not only report such usage in terms of traffic size and number of connections but also depict how users behave on top of Tor. We also show that Tor usage is now diverted from the onion routing concept and that Tor exit nodes are frequently used as 1-hop SOCKS proxies, through a so-called tunneling technique. We provide an efficient method allowing an exit node to detect such an abnormal usage. Finally, we report our experience in effectively crawling bridge nodes, supposedly revealed sparingly in Tor.