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Register a new userMaster of Puppets: Analyzing And Attacking A Botnet For Fun And Profit
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Added by
Gianluca Stringhini
Publication date
2015
fields
Informatics Engineering
and research's language is
English
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A botnet is a network of compromised machines (bots), under the control of an attacker. Many of these machines are infected without their owners knowledge, and botnets are the driving force behind several misuses and criminal activities on the Internet (for example spam emails). Depending on its topology, a botnet can have zero or more command and control (C&C) servers, which are centralized machines controlled by the cybercriminal that issue commands and receive reports back from the co-opted bots. In this paper, we present a comprehensive analysis of the command and control infrastructure of one of the worlds largest proprietary spamming botnets between 2007 and 2012: Cutwail/Pushdo. We identify the key functionalities needed by a spamming botnet to operate effectively. We then develop a number of attacks against the command and control logic of Cutwail that target those functionalities, and make the spamming operations of the botnet less effective. This analysis was made possible by having access to the source code of the C&C software, as well as setting up our own Cutwail C&C server, and by implementing a clone of the Cutwail bot. With the help of this tool, we were able to enumerate the number of bots currently registered with the C&C server, impersonate an existing bot to report false information to the C&C server, and manipulate spamming statistics of an arbitrary bot stored in the C&C database. Furthermore, we were able to make the control server inaccessible by conducting a distributed denial of service (DDoS) attack. Our results may be used by law enforcement and practitioners to develop better techniques to mitigate and cripple other botnets, since many of findings are generic and are due to the workflow of C&C communication in general.
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Credit allows a lender to loan out surplus capital to a borrower. In the traditional economy, credit bears the risk that the borrower may default on its debt, the lender hence requires upfront collateral from the borrower, plus interest fee payments. Due to the atomicity of blockchain transactions, lenders can offer flash loans, i.e., loans that are only valid within one transaction and must be repaid by the end of that transaction. This concept has lead to a number of interesting attack possibilities, some of which were exploited in February 2020. This paper is the first to explore the implication of transaction atomicity and flash loans for the nascent decentralized finance (DeFi) ecosystem. We show quantitatively how transaction atomicity increases the arbitrage revenue. We moreover analyze two existing attacks with ROIs beyond 500k%. We formulate finding the attack parameters as an optimization problem over the state of the underlying Ethereum blockchain and the state of the DeFi ecosystem. We show how malicious adversaries can efficiently maximize an attack profit and hence damage the DeFi ecosystem further. Specifically, we present how two previously executed attacks can be boosted to result in a profit of 829.5k USD and 1.1M USD, respectively, which is a boost of 2.37x and 1.73x, respectively.
The proliferation of web applications has essentially transformed modern browsers into small but powerful operating systems. Upon visiting a website, user devices run implicitly trusted script code, the execution of which is confined within the browser to prevent any interference with the users system. Recent JavaScript APIs, however, provide advanced capabilities that not only enable feature-rich web applications, but also allow attackers to perform malicious operations despite the confined nature of JavaScript code execution. In this paper, we demonstrate the powerful capabilities that modern browser APIs provide to attackers by presenting MarioNet: a framework that allows a remote malicious entity to control a visitors browser and abuse its resources for unwanted computation or harmful operations, such as cryptocurrency mining, password-cracking, and DDoS. MarioNet relies solely on already available HTML5 APIs, without requiring the installation of any additional software. In contrast to previous browser-based botnets, the persistence and stealthiness characteristics of MarioNet allow the malicious computations to continue in the background of the browser even after the user closes the window or tab of the initial malicious website. We present the design, implementation, and evaluation of a prototype system, MarioNet, that is compatible with all major browsers, and discuss potential defense strategies to counter the threat of such persistent in-browser attacks. Our main goal is to raise awareness regarding this new class of attacks, and inform the design of future browser APIs so that they provide a more secure client-side environment for web applications.
We focus on the problem of botnet orchestration and discuss how attackers can leverage decentralised technologies to dynamically control botnets with the goal of having botnets that are resilient against hostile takeovers. We cover critical elements of the Bitcoin blockchain and its usage for `floating command and control servers. We further discuss how blockchain-based botnets can be built and include a detailed discussion of our implementation. We also showcase how specific Bitcoin APIs can be used in order to write extraneous data to the blockchain. Finally, while in this paper, we use Bitcoin to build our resilient botnet proof of concept, the threat is not limited to Bitcoin blockchain and can be generalized.
Neural data compression has been shown to outperform classical methods in terms of $RD$ performance, with results still improving rapidly. At a high level, neural compression is based on an autoencoder that tries to reconstruct the input instance from a (quantized) latent representation, coupled with a prior that is used to losslessly compress these latents. Due to limitations on model capacity and imperfect optimization and generalization, such models will suboptimally compress test data in general. However, one of the great strengths of learned compression is that if the test-time data distribution is known and relatively low-entropy (e.g. a camera watching a static scene, a dash cam in an autonomous car, etc.), the model can easily be finetuned or adapted to this distribution, leading to improved $RD$ performance. In this paper we take this concept to the extreme, adapting the full model to a single video, and sending model updates (quantized and compressed using a parameter-space prior) along with the latent representation. Unlike previous work, we finetune not only the encoder/latents but the entire model, and - during finetuning - take into account both the effect of model quantization and the additional costs incurred by sending the model updates. We evaluate an image compression model on I-frames (sampled at 2 fps) from videos of the Xiph dataset, and demonstrate that full-model adaptation improves $RD$ performance by ~1 dB, with respect to encoder-only finetuning.
Command and control (C&C) is the essential component of a botnet. In previous C&C using online social networks (OSNs), the botmasters identifiers are reversible. After a bot is analyzed, the botmasters accounts can be predicted in advance. Additionally, abnormal content from explicit commands may expose botmasters and raise anomalies on OSNs. To overcome these deficiencies, we proposed DeepC2, an AI-powered covert C&C method on OSNs. By leveraging neural networks, bots can find botmasters by avatars, which are converted into feature vectors and built into bots. Defenders cannot predict the botmasters accounts from the vectors in advance. Commands are embedded into normal contents (e.g., tweets and comments) using easy data augmentation and hash collision. Experiments on Twitter show that command-embedded contents can be generated efficiently, and bots can find botmasters and obtain commands accurately. Security analysis on different scenarios show that it is hard to predict the botmasters avatars. By demonstrating how AI may help promote covert communication on OSNs, this work provides a new perspective on botnet detection and confrontation.
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