In this paper, we propose a method that detects cryptojacking activities by analyzing content-agnostic network traffic flows. Our method first distinguishes crypto-mining activities by profiling the traffic with fast Fourier transform at each time window. It then generates the variation vectors between adjacent time windows and leverages a recurrent neural network to identify the cryptojacking patterns. Compared with the existing approaches, this method is privacy-preserving and can identify both browser-based and malware-based cryptojacking activities. Additionally, this method is easy to deploy. It can monitor all the devices within a network by accessing packet headers from the gateway router.
In this paper, we introduce a new, offer-based operational model for in-network DDoS defense and formulate the NP-hard rule selection problem for this model. We then design an algorithm that overcomes the fundamental limitations of the classical ACO framework and transform it with several key changes to make it applicable to the domain of in-network DDoS defense. Finally, we use a real-world Internet routing topology and two real-world DDoS traces, along with one synthetic trace that follows the attack distribution of the recent Mirai DDoS attack, to evaluate the efficacy and runtime of our algorithm against four other rule selection algorithms, and show our algorithm is near-optimal.
In this work, we elaborate on the critical missing gaps in DDoS defense evaluation and propose a new evaluation platform to help produce the missing defense analytics. To identify the impact of a defense solution in realistic network settings, our platform offers to emulate a mini Internet topology with realistic IP address space allocation and generate representational, closed-loop background traffic specific to particular networks. As such, our platform fulfills the prominent missing gaps in current DDoS research. In the end, we conduct some experiments to demonstrate the correctness and efficiency of our platform.
This paper is the poster version of Bridging Missing Gaps in Evaluating DDoS Research (CSET 2020). This poster paper introduced the world to our Sandbox.
In this paper, we review the state of Internet of Things (IoT) security research, with a focus on recent countermeasures that attempt to address vulnerabilities and attacks in IoT networks. Due to the fact that IoT encompasses a large range of significantly distinct environments, each of which merits their own survey, our survey focuses mainly on the smart home environment. Based on the papers surveyed, we pinpoint several challenges and open issues that have yet to be adequately addressed in the realm of IoT security research. Lastly, in order to address these open issues, we provide a list of future research directions on which we believe researchers should focus.
In this work, we introduce the Catch-22 attack, a distributed denial-of-service (DDoS) link-flooding attack that exploits real-world limitations of DDoS defense. An attacker in the Catch-22 attack leverages virtual private server (VPS) providers and residential proxy services as vehicles for assembling a botnet, and employs moving target attack techniques to not only maximize the amount of strain on DDoS defense, but also maximize the amount of collateral damage incurred by attacked networks, thereby wreaking havoc on wide swaths of the Internet. In fact, according to our preliminary evaluation, the Catch-22 attack can cause significant collateral damage to over thousands of websites from a major VPS provider. To the best of our knowledge, no existing work has yet to present a solution for such an attack, let alone study it.