Jared M. Smith

Robert A. Bridges, Sean Oesch, Miki E. Verma et al.

In this paper, we present a scientific evaluation of four prominent malware detection tools to assist an organization with two primary questions: To what extent do ML-based tools accurately classify previously- and never-before-seen files? Is it worth purchasing a network-level malware detector? To identify weaknesses, we tested each tool against 3,536 total files (2,554 or 72\% malicious, 982 or 28\% benign) of a variety of file types, including hundreds of malicious zero-days, polyglots, and APT-style files, delivered on multiple protocols. We present statistical results on detection time and accuracy, consider complementary analysis (using multiple tools together), and provide two novel applications of the recent cost-benefit evaluation procedure of Iannacone \& Bridges. While the ML-based tools are more effective at detecting zero-day files and executables, the signature-based tool may still be an overall better option. Both network-based tools provide substantial (simulated) savings when paired with either host tool, yet both show poor detection rates on protocols other than HTTP or SMTP. Our results show that all four tools have near-perfect precision but alarmingly low recall, especially on file types other than executables and office files -- 37% of malware tested, including all polyglot files, were undetected. Priorities for researchers and takeaways for end users are given.

Tyler McDaniel, Jared M. Smith, Max Schuchard

We present the Maestro attack, a novel Link Flooding Attack (LFA) that leverages control-plane traffic engineering techniques to concentrate botnet-sourced Distributed Denial of Service flows on transit links. Executed from a compromised or malicious Autonomous System (AS), Maestro advertises specific-prefix routes poisoned for selected ASes to collapse inbound traffic paths onto a single target link. A greedy heuristic fed by publicly available AS relationship data iteratively builds the set of ASes to poison. Given a compromised BGP speaker with advantageous positioning relative to the target link in the Internet topology, an adversary can expect to enhance total flow density by more than 30%. For a large botnet (e.g., Mirai), that translates to augmenting a DDoS by more than a million additional infected hosts. Interestingly, the size of the adversary-controlled AS plays little role in this amplification effect. Devastating attacks on core links can be executed by small, resource-limited ASes. To understand the scope of the attack, we evaluate widespread Internet link vulnerability across several metrics, including BGP betweenness and botnet flow density. We then assess where an adversary must be positioned to execute the attack most successfully. Finally, we present effective mitigations for network operators seeking to insulate themselves from this attack.

Jared M. Smith, Kyle Birkeland, Tyler McDaniel et al.

The security of the Internet's routing infrastructure has underpinned much of the past two decades of distributed systems security research. However, the converse is increasingly true. Routing and path decisions are now important for the security properties of systems built on top of the Internet. In particular, BGP poisoning leverages the de facto routing protocol between Autonomous Systems (ASes) to maneuver the return paths of upstream networks onto previously unusable, new paths. These new paths can be used to avoid congestion, censors, geo-political boundaries, or any feature of the topology which can be expressed at an AS-level. Given the increase in BGP poisoning usage as a security primitive, we set out to evaluate poisoning feasibility in practice beyond simulation. To that end, using an Internet-scale measurement infrastructure, we capture and analyze over 1,400 instances of BGP poisoning across thousands of ASes as a mechanism to maneuver return paths of traffic. We analyze in detail the performance of steering paths, the graph-theoretic aspects of available paths, and re-evaluate simulated systems with this data. We find that the real-world evidence does not completely support the findings from simulated systems published in the literature. We also analyze filtering of BGP poisoning across types of ASes and ISP working groups. We explore the connectivity concerns when poisoning by reproducing a decade old experiment to uncover the current state of an Internet triple the size. We build predictive models for understanding an ASes' vulnerability to poisoning. Finally, an exhaustive measurement of an upper bound on the maximum path length of the Internet is presented, detailing how security research should react to ASes leveraging poisoned long paths. In total, our results and analysis expose the real-world impact of BGP poisoning on past and future security research.