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 investigate the emergence of the exploit-as-a-service model for driveby browser compromise. In this regime, attackers pay for an exploit kit or service to do the "dirty work" of exploiting a victim's browser, decoupling the complexities of browser and plugin vulnerabilities from the challenges of generating traffic to a website under the attacker's control. Upon a successful exploit, these kits load and execute a binary provided by the attacker, effectively transferring control of a victim's machine to the attacker.

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 Web site 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 article, 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 Web sites. Second, using data from several hundred botnets, we apply our technique to indirectly measure the infected population of this increasing Internet phenomenon.

This paper describes flow-cookies which defend against DDoS flooding attacks using bandwidth amplification. "Flow-cookies" is a mechanism in which a Website can reliably send filtering requests to a cooperating node in the network, leveraging its protection bandwidth. In this approach, a third party provider installs a flow-cookies enabled middlebox called the cookie box, in the network at a high bandwidth link. All traffic to or from the protected Web server must traverse the cookie box. The cookie box guarantees that all packets that pass between it and the server belong to a legitimate TCP flow with a valid sender. This implementation is able to operate at gigabit speeds including per-packet IP filtering of millions of addresses. This approach is also very effective against high volume SYN flooding attacks