Visible to the public Biblio

Filters: Author is Rossow, Christian  [Clear All Filters]
Walla, Sebastian, Rossow, Christian.  2019.  MALPITY: Automatic Identification and Exploitation of Tarpit Vulnerabilities in Malware. 2019 IEEE European Symposium on Security and Privacy (EuroS P). :590—605.
Law enforcement agencies regularly take down botnets as the ultimate defense against global malware operations. By arresting malware authors, and simultaneously infiltrating or shutting down a botnet's network infrastructures (such as C2 servers), defenders stop global threats and mitigate pending infections. In this paper, we propose malware tarpits, an orthogonal defense that does not require seizing botnet infrastructures, and at the same time can also be used to slow down malware spreading and infiltrate its monetization techniques. A tarpit is a network service that causes a client to stay busy with a network operation. Our work aims to automatically identify network operations used by malware that will block the malware either forever or for a significant amount of time. We describe how to non-intrusively exploit such tarpit vulnerabilities in malware to slow down or, ideally, even stop malware. Using dynamic malware analysis, we monitor how malware interacts with the POSIX and Winsock socket APIs. From this, we infer network operations that would have blocked when provided certain network inputs. We augment this vulnerability search with an automated generation of tarpits that exploit the identified vulnerabilities. We apply our prototype MALPITY on six popular malware families and discover 12 previously-unknown tarpit vulnerabilities, revealing that all families are susceptible to our defense. We demonstrate how to, e.g., halt Pushdo's DGA-based C2 communication, hinder SalityP2P peers from receiving commands or updates, and stop Bashlite's spreading engine.
Jonker, Mattijs, King, Alistair, Krupp, Johannes, Rossow, Christian, Sperotto, Anna, Dainotti, Alberto.  2017.  Millions of Targets Under Attack: A Macroscopic Characterization of the DoS Ecosystem. Proceedings of the 2017 Internet Measurement Conference. :100–113.

Denial-of-Service attacks have rapidly increased in terms of frequency and intensity, steadily becoming one of the biggest threats to Internet stability and reliability. However, a rigorous comprehensive characterization of this phenomenon, and of countermeasures to mitigate the associated risks, faces many infrastructure and analytic challenges. We make progress toward this goal, by introducing and applying a new framework to enable a macroscopic characterization of attacks, attack targets, and DDoS Protection Services (DPSs). Our analysis leverages data from four independent global Internet measurement infrastructures over the last two years: backscatter traffic to a large network telescope; logs from amplification honeypots; a DNS measurement platform covering 60% of the current namespace; and a DNS-based data set focusing on DPS adoption. Our results reveal the massive scale of the DoS problem, including an eye-opening statistic that one-third of all / 24 networks recently estimated to be active on the Internet have suffered at least one DoS attack over the last two years. We also discovered that often targets are simultaneously hit by different types of attacks. In our data, Web servers were the most prominent attack target; an average of 3% of the Web sites in .com, .net, and .org were involved with attacks, daily. Finally, we shed light on factors influencing migration to a DPS.

Dietrich, Christian J., Rossow, Christian, Pohlmann, Norbert.  2013.  Exploiting Visual Appearance to Cluster and Detect Rogue Software. Proceedings of the 28th Annual ACM Symposium on Applied Computing. :1776–1783.

Rogue software, such as Fake A/V and ransomware, trick users into paying without giving return. We show that using a perceptual hash function and hierarchical clustering, more than 213,671 screenshots of executed malware samples can be grouped into subsets of structurally similar images, reflecting image clusters of one malware family or campaign. Based on the clustering results, we show that ransomware campaigns favor prepay payment methods such as ukash, paysafecard and moneypak, while Fake A/V campaigns use credit cards for payment. Furthermore, especially given the low A/V detection rates of current rogue software – sometimes even as low as 11% – our screenshot analysis approach could serve as a complementary last line of defense.

Krupp, Johannes, Backes, Michael, Rossow, Christian.  2016.  Identifying the Scan and Attack Infrastructures Behind Amplification DDoS Attacks. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. :1426–1437.

Amplification DDoS attacks have gained popularity and become a serious threat to Internet participants. However, little is known about where these attacks originate, and revealing the attack sources is a non-trivial problem due to the spoofed nature of the traffic. In this paper, we present novel techniques to uncover the infrastructures behind amplification DDoS attacks. We follow a two-step approach to tackle this challenge: First, we develop a methodology to impose a fingerprint on scanners that perform the reconnaissance for amplification attacks that allows us to link subsequent attacks back to the scanner. Our methodology attributes over 58% of attacks to a scanner with a confidence of over 99.9%. Second, we use Time-to-Live-based trilateration techniques to map scanners to the actual infrastructures launching the attacks. Using this technique, we identify 34 networks as being the source for amplification attacks at 98\textbackslash% certainty.

Welzel, Arne, Rossow, Christian, Bos, Herbert.  2014.  On Measuring the Impact of DDoS Botnets. Proceedings of the Seventh European Workshop on System Security. :3:1–3:6.

Miscreants use DDoS botnets to attack a victim via a large number of malware-infected hosts, combining the bandwidth of the individual PCs. Such botnets have thus a high potential to render targeted services unavailable. However, the actual impact of attacks by DDoS botnets has never been evaluated. In this paper, we monitor C&C servers of 14 DirtJumper and Yoddos botnets and record the DDoS targets of these networks. We then aim to evaluate the availability of the DDoS victims, using a variety of measurements such as TCP response times and analyzing the HTTP content. We show that more than 65% of the victims are severely affected by the DDoS attacks, while also a few DDoS attacks likely failed.