The academic community has long acknowledged the existence of malicious botnets, however to date, very little is known about the behavior of these distributed computing platforms. To the best of our knowledge, botnet behavior has never been methodically studied, botnet prevalence on the Internet is mostly a mystery, and the botnet life cycle has yet to be modeled. Uncertainty abounds. In this paper, we attempt to clear the fog surrounding botnets by constructing a multifaceted and distributed measurement infrastructure. Throughout a period of more than three months, we used this infrastructure to track 192 unique IRC botnets of size ranging from a few hundred to several thousand infected end-hosts. Our results show that botnets represent a major contributor to unwanted Internet traffic-27% of all malicious connection attempts observed from our distributed darknet can be directly attributed to botnetrelated spreading activity. Furthermore, we discovered evidence of botnet infections in 11% of the 800,000 DNS domains we examined, indicating a high diversity among botnet victims. Taken as a whole, these results not only highlight the prominence of botnets, but also provide deep insights that may facilitate further research to curtail this phenomenon.

Today, web injection manifests in many forms, but fundamentally occurs when malicious and unwanted actors tamper directly with browser sessions for their own profit. In this work we illuminate the scope and negative impact of one of these forms, ad injection, in which users have ads imposed on them in addition to, or different from, those that websites originally sent them. We develop a multi-staged pipeline that identifies ad injection in the wild and captures its distribution and revenue chains. We find that ad injection has entrenched itself as a cross-browser monetization platform impacting more than 5% of unique daily IP addresses accessing Google-tens of millions of users around the globe. Injected ads arrive on a client's machine through multiple vectors: our measurements identify 50,870 Chrome extensions and 34,407 Windows binaries, 38% and 17% of which are explicitly malicious. A small number of software developers support the vast majority of these injectors who in turn syndicate from the larger ad ecosystem. We have contacted the Chrome Web Store and the advertisers targeted by ad injectors to alert each of the deceptive practices involved.

We present a technique to infer a worm's infection sequence from traffic traces collected at a network telescope. We analyze the fidelity of the infection evolution as inferred by our technique, and explore its effectiveness under varying constraints including the scanning rate of the worm, the size of the vulnerable population, and the size of the telescope itself. Moreover, we provide guidance regarding the point at which our method's accuracy diminishes beyond practical value. As we show empirically, this point is reached well after a few hundred initial infected hosts (possibly including "patient zero") has been reliably identified with more than 80% accuracy. We generalize our mechanism by exploiting the change in the pattern of inter-arrival times exhibited during the early stages of such an outbreak to detect the presence and approximate size of the hit-list. Our mechanism is resilient to varying parameters like the worm scanning rate and the size of the vulnerable population, and can provide significant insights into the characteristics of the hit-list even under spreading dynamics that exceed that of currently known worms. Lastly, to illustrate the practicality of our solution, we apply our approach to real-world traces of the Witty worm and provide a refined estimate on the previously suspected hit-list size.

joined Google in 2003 and is currently a principal software engineer in the Infrastructure Security Group. His areas of interest include computer and network security, as well as large-scale distributed systems. He serves on the Usenix board of directors. MOHEEB ABU RAJAB

This study presents an analysis of the impact of mitigation on computer worm propagation in Mobile Ad-hoc Networks (MANETS)

While malware models have become increasingly accurate over the past few years, none of the existing proposals accounts for the use of Network Address Translation (NAT). This oversight is problematic since many network customers use NAT in their local networks. In fact, measurements we collected from a distributed honeynet show that approximately 19% of the infected hosts reside in NATted domains. To account for this fact, we present a model that can be used to understand the impact of varying levels of NAT deployment on malware that spread by preferentially scanning the IP space. Using this model, we show that NATting impedes malware propagation in several ways and can have a significant impact on non-uniform scanning worms as it invalidates the implicit assumption that vulnerable hosts reside in densely populated subnets.

Abstract. Reliable network demographics are quickly becoming a much sought-after digital commodity. However, as the need for more refined Internet demographics has grown, so too has the tension between privacy and utility. Unfortunately, current techniques lean too much in favor of functional requirements over protecting the privacy of users. For example, the most prominent proposals for measuring the relative popularity of a website depend on the deployment of client-side measurement agents that are generally perceived as infringing on users' privacy, thereby limiting their wide scale adoption. Moreover, the client-side nature of these techniques also makes them susceptible to various manipulation tactics that undermine the integrity of their results. In this paper, we propose a new estimation technique that uses DNS cache probing to infer the density of clients accessing a given service. Compared to earlier techniques, our scheme is less invasive as it does not reveal user-specific traits, and is more robust against manipulation. We demonstrate the flexibility of our approach through two important security applications. First, we illustrate how our scheme can be used as a lightweight technique for measuring and verifying the relative popularity rank of different websites. Second, using data from several hundred botnets, we apply our technique to indirectly measure the infected population of this increasing Internet phenomenon.

Abstract. Passive network monitors, known as telescopes or darknets, have been invaluable in detecting and characterizing malware outbreaks. However, as the use of such monitors becomes commonplace, it is likely that malware will evolve to actively detect and evade them. This paper highlights the threat of simple, yet effective, evasive attacks that undermine the usefulness of passive monitors. Our results raise an alarm to the research and operational communities to take proactive countermeasures before we are forced to defend against similar attacks appearing in the wild. Specifically, we show how lightweight, coordinated sampling of the IP address space can be used to successfully detect and evade passive network monitors. Equally troubling is the fact that in doing so attackers can locate the "live" IP space clusters and divert malware scanning solely toward active networks. We show that evasive attacks exploiting this knowledge are also extremely fast, overtaking the entire vulnerable population within seconds.