Characterization and modeling of top spam botnets

The increasing impact of the Internet in the global economy has transformed Botnets into one of the most relevant security threats for citizens, organizations and governments. Despite the significant efforts that have been made over the last years to understand this phenomenon and develop detection techniques and countermeasures, this continues to be a field with big challenges to address. Several approaches can be taken to study Botnets: analyze its source code, which can be a hard task because it is usually unavailable; study the control mechanism, particularly the activity of its Command and Control server(s); study its behavior, by measuring real traffic and collecting relevant statistics. In this work, we have installed some of the most popular spam Botnets, captured the originated traffic and characterized it in order to identify the main trends/patterns of their activity. From the intensive statistics that were collected, it was possible to conclude that there are distinct features between Botnets that can be explored to build efficient detection methodologies. Based on this study, the second part of the paper proposes a generic and systematic model to describe the network dynamics whenever a Botnet threat is detected, defining all actors, dimensions, states and actions that need to be taken into account at each moment. We believe that this type of modeling approach is the basis for developing systematic and integrated frameworks and strategies to predict and fight Botnet threats in an efficient way.This research was supported by Fundação para a Ciência e a Tecnologia, under research project PTDC/EEA-TEL/101880/2008

Malicious botnet survivability mechanism evolution forecasting by means of a genetic algorithm

Botnets are considered to be among the most dangerous modern malware types and the biggest current threats to global IT infrastructure. Botnets are rapidly evolving, and therefore forecasting their survivability strategies is important for the development of countermeasure techniques. The article propose the botnet-oriented genetic algorithm based model framework, which aimed at forecasting botnet survivability mechanisms. The model may be used as a framework for forecasting the evolution of other characteristics. The efficiency of different survivability mechanisms is evaluated by applying the proposed fitness function. The model application area also covers scientific botnet research and modelling tasks. Article in English. Kenkėjiškų botnet tinklų išgyvenamumo mechanizmų evoliucijos prognozavimas genetinio algoritmo priemonėmis Santrauka. Botnet tinklai pripažįstami kaip vieni pavojingiausių šiuolaikinių kenksmingų programų ir vertinami kaip viena iš didžiausių grėsmių tarptautinei IT infrastruktūrai. Botnettinklai greitai evoliucionuoja, todėl jų savisaugos mechanizmų evoliucijos prognozavimas yra svarbus planuojant ir kuriant kontrpriemones. Šiame straipsnyje pateikiamas genetiniu algoritmu pagrįstas modelis, skirtas Botnet tinklų savisaugos mechanizmų evoliucijai prognozuoti, kuris taip pat gali būti naudojamas kaip pagrindas kitų Botnet tinklų savybių evoliucijai modeliuoti. Skirtingi savisaugos mechanizmai vertinami taikant siūlomą tinkamumo funkciją. Raktiniai žodžiai: Botnet; genetinis algoritmas; prognozė; savisauga; evoliucija; modeli

Command & Control: Understanding, Denying and Detecting - A review of malware C2 techniques, detection and defences

In this survey, we first briefly review the current state of cyber attacks, highlighting significant recent changes in how and why such attacks are performed. We then investigate the mechanics of malware command and control (C2) establishment: we provide a comprehensive review of the techniques used by attackers to set up such a channel and to hide its presence from the attacked parties and the security tools they use. We then switch to the defensive side of the problem, and review approaches that have been proposed for the detection and disruption of C2 channels. We also map such techniques to widely-adopted security controls, emphasizing gaps or limitations (and success stories) in current best practices.Comment: Work commissioned by CPNI, available at c2report.org. 38 pages. Listing abstract compressed from version appearing in repor

Adversarial behaviours knowledge area

The technological advancements witnessed by our society in recent decades have brought improvements in our quality of life, but they have also created a number of opportunities for attackers to cause harm. Before the Internet revolution, most crime and malicious activity generally required a victim and a perpetrator to come into physical contact, and this limited the reach that malicious parties had. Technology has removed the need for physical contact to perform many types of crime, and now attackers can reach victims anywhere in the world, as long as they are connected to the Internet. This has revolutionised the characteristics of crime and warfare, allowing operations that would not have been possible before. In this document, we provide an overview of the malicious operations that are happening on the Internet today. We first provide a taxonomy of malicious activities based on the attacker’s motivations and capabilities, and then move on to the technological and human elements that adversaries require to run a successful operation. We then discuss a number of frameworks that have been proposed to model malicious operations. Since adversarial behaviours are not a purely technical topic, we draw from research in a number of fields (computer science, criminology, war studies). While doing this, we discuss how these frameworks can be used by researchers and practitioners to develop effective mitigations against malicious online operations.Published versio

On Detection of Current and Next-Generation Botnets.

Botnets are one of the most serious security threats to the Internet and its end users. A botnet consists of compromised computers that are remotely coordinated by a botmaster under a Command and Control (C&C) infrastructure. Driven by financial incentives, botmasters leverage botnets to conduct various cybercrimes such as spamming, phishing, identity theft and Distributed-Denial-of-Service (DDoS) attacks. There are three main challenges facing botnet detection. First, code obfuscation is widely employed by current botnets, so signature-based detection is insufficient. Second, the C&C infrastructure of botnets has evolved rapidly. Any detection solution targeting one botnet instance can hardly keep up with this change. Third, the proliferation of powerful smartphones presents a new platform for future botnets. Defense techniques designed for existing botnets may be outsmarted when botnets invade smartphones. Recognizing these challenges, this dissertation proposes behavior-based botnet detection solutions at three different levels---the end host, the edge network and the Internet infrastructure---from a small scale to a large scale, and investigates the next-generation botnet targeting smartphones. It (1) addresses the problem of botnet seeding by devising a per-process containment scheme for end-host systems; (2) proposes a hybrid botnet detection framework for edge networks utilizing combined host- and network-level information; (3) explores the structural properties of botnet topologies and measures network components' capabilities of large-scale botnet detection at the Internet infrastructure level; and (4) presents a proof-of-concept mobile botnet employing SMS messages as the C&C and P2P as the topology to facilitate future research on countermeasures against next-generation botnets. The dissertation makes three primary contributions. First, the detection solutions proposed utilize intrinsic and fundamental behavior of botnets and are immune to malware obfuscation and traffic encryption. Second, the solutions are general enough to identify different types of botnets, not a specific botnet instance. They can also be extended to counter next-generation botnet threats. Third, the detection solutions function at multiple levels to meet various detection needs. They each take a different perspective but are highly complementary to each other, forming an integrated botnet detection framework.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91382/1/gracez_1.pd