On Robust Covert Channels Inside DNS

International audienceCovert channels inside DNS allow evasion of networks which only provide a restricted access to the Internet. By encapsulating data inside DNS requests and replies exchanged with a server located outside the restricted network, several existing implementations provide either an IP over DNS tunnel, or a socket-like service (TCP over DNS). This paper contributes a detailed overview of the challenges faced by the design of such tunnels, and describes the existing implementations. Then, it introduces TUNS, our prototype of an IP over DNS tunnel, focused on simplicity and protocol compliance. Comparison of TUNS and the other implementations showed that this approach is successful: TUNS works on all the networks we tested, and provides reasonable performance despite its use of less efficient encapsulation techniques, especially when facing degraded network conditions

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

A Dynamic, Broad Spectrum Approach to Covert Channels

In most covert channel systems, bandwidth is sacrificed for covertness. By using a dynamic, broad spectrum approach it was possible to use multiple covert channels to create a larger pipeline for data transmission. This approach utilized a monitor to determine how much data the network would be able to support before suspicion would be drawn to the change in traffic utility. The system then dispersed this traffic to each protocol proportionately using their monitored utility as a guide. A feedback channel was also utilized to determine if channel transmissions were successful and disabled any channels for future use that the network security blocked. In this way, a robust channel system was created that also increased bandwidth of communication

Deteção de atividades ilícitas de software Bots através do DNS

DNS is a critical component of the Internet where almost all Internet applications and organizations rely on. Its shutdown can deprive them from being part of the Internet, and hence, DNS is usually the only protocol to be allowed when Internet access is firewalled. The constant exposure of this protocol to external entities force corporations to always be observant of external rogue software that may misuse the DNS to establish covert channels and perform multiple illicit activities, such as command and control and data exfiltration. Most current solutions for bot malware and botnet detection are based on Deep Packet Inspection techniques, such as analyzing DNS query payloads, which may reveal private and sensitive information. In addiction, the majority of existing solutions do not consider the usage of licit and encrypted DNS traffic, where Deep Packet Inspection techniques are impossible to be used. This dissertation proposes mechanisms to detect malware bots and botnet behaviors on DNS traffic that are robust to encrypted DNS traffic and that ensure the privacy of the involved entities by analyzing instead the behavioral patterns of DNS communications using descriptive statistics over collected network metrics such as packet rates, packet lengths, and silence and activity periods. After characterizing DNS traffic behaviors, a study of the processed data is conducted, followed by the training of Novelty Detection algorithms with the processed data. Models are trained with licit data gathered from multiple licit activities, such as reading the news, studying, and using social networks, in multiple operating systems, browsers, and configurations. Then, the models were tested with similar data, but containing bot malware traffic. Our tests show that our best performing models achieve detection rates in the order of 99%, and 92% for malware bots using low throughput rates. This work ends with some ideas for a more realistic generation of bot malware traffic, as the current DNS Tunneling tools are limited when mimicking licit DNS usages, and for a better detection of malware bots that use low throughput rates.O DNS é um componente crítico da Internet, já que quase todas as aplicações e organizações que a usam dependem dele para funcionar. A sua privação pode deixá-las de fazerem parte da Internet, e por causa disso, o DNS é normalmente o único protocolo permitido quando o acesso à Internet está restrito. A exposição constante deste protocolo a entidades externas obrigam corporações a estarem sempre atentas a software externo ilícito que pode fazer uso indevido do DNS para estabelecer canais secretos e realizar várias atividades ilícitas, como comando e controlo e exfiltração de dados. A maioria das soluções atuais para detecção de malware bots e de botnets são baseadas em técnicas inspeção profunda de pacotes, como analizar payloads de pedidos de DNS, que podem revelar informação privada e sensitiva. Além disso, a maioria das soluções existentes não consideram o uso lícito e cifrado de tráfego DNS, onde técnicas como inspeção profunda de pacotes são impossíveis de serem usadas. Esta dissertação propõe mecanismos para detectar comportamentos de malware bots e botnets que usam o DNS, que são robustos ao tráfego DNS cifrado e que garantem a privacidade das entidades envolvidas ao analizar, em vez disso, os padrões comportamentais das comunicações DNS usando estatística descritiva em métricas recolhidas na rede, como taxas de pacotes, o tamanho dos pacotes, e os tempos de atividade e silêncio. Após a caracterização dos comportamentos do tráfego DNS, um estudo sobre os dados processados é realizado, sendo depois usados para treinar os modelos de Detecção de Novidades. Os modelos são treinados com dados lícitos recolhidos de multiplas atividades lícitas, como ler as notícias, estudar, e usar redes sociais, em multiplos sistemas operativos e com multiplas configurações. De seguida, os modelos são testados com dados lícitos semelhantes, mas contendo também tráfego de malware bots. Os nossos testes mostram que com modelos de Detecção de Novidades é possível obter taxas de detecção na ordem dos 99%, e de 98% para malware bots que geram pouco tráfego. Este trabalho finaliza com algumas ideas para uma geração de tráfego ilícito mais realista, já que as ferramentas atuais de DNS tunneling são limitadas quando usadas para imitar usos de DNS lícito, e para uma melhor deteção de situações onde malware bots geram pouco tráfego.Mestrado em Engenharia de Computadores e Telemátic

Machine Learning based Anomaly Detection for Cybersecurity Monitoring of Critical Infrastructures

openManaging critical infrastructures requires to increasingly rely on Information and Communi- cation Technologies. The last past years showed an incredible increase in the sophistication of attacks. For this reason, it is necessary to develop new algorithms for monitoring these infrastructures. In this scenario, Machine Learning can represent a very useful ally. After a brief introduction on the issue of cybersecurity in Industrial Control Systems and an overview of the state of the art regarding Machine Learning based cybersecurity monitoring, the present work proposes three approaches that target different layers of the control network architecture. The first one focuses on covert channels based on the DNS protocol, which can be used to establish a command and control channel, allowing attackers to send malicious commands. The second one focuses on the field layer of electrical power systems, proposing a physics-based anomaly detection algorithm for Distributed Energy Resources. The third one proposed a first attempt to integrate physical and cyber security systems, in order to face complex threats. All these three approaches are supported by promising results, which gives hope to practical applications in the next future.openXXXIV CICLO - SCIENZE E TECNOLOGIE PER L'INGEGNERIA ELETTRONICA E DELLE TELECOMUNICAZIONI - Elettromagnetismo, elettronica, telecomunicazioniGaggero, GIOVANNI BATTIST

Multi-Stage Detection Technique for DNS-Based Botnets

Domain Name System (DNS) is one of the most widely used protocols in the Internet. The main purpose of the DNS protocol is mapping user-friendly domain names to IP addresses. Unfortunately, many cyber criminals deploy the DNS protocol for malicious purposes, such as botnet communications. In this type of attack, the botmasters tunnel communications between the Command and Control (C&C) servers and the bot-infected machines within DNS request and response. Designing an effective approach for botnet detection has been done previously based on specific botnet types Since botnet communications are characterized by different features, botmasters may evade detection methods by modifying some of these features. This research aims to design and implement a multi-staged detection approach for Domain Generation Algorithm (DGA), Fast Flux Service Network, and Domain Flux-based botnets, as well as encrypted DNS tunneled-based botnets using the BRO Network Security Monitor. This approach is able to detect DNS-based botnet communications by relying on analyzing different techniques used for finding the C&C server, as well as encrypting the malicious traffic