484 research outputs found

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

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    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

    Hidden and Uncontrolled - On the Emergence of Network Steganographic Threats

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    Network steganography is the art of hiding secret information within innocent network transmissions. Recent findings indicate that novel malware is increasingly using network steganography. Similarly, other malicious activities can profit from network steganography, such as data leakage or the exchange of pedophile data. This paper provides an introduction to network steganography and highlights its potential application for harmful purposes. We discuss the issues related to countering network steganography in practice and provide an outlook on further research directions and problems.Comment: 11 page

    Outsmarting Network Security with SDN Teleportation

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    Software-defined networking is considered a promising new paradigm, enabling more reliable and formally verifiable communication networks. However, this paper shows that the separation of the control plane from the data plane, which lies at the heart of Software-Defined Networks (SDNs), introduces a new vulnerability which we call \emph{teleportation}. An attacker (e.g., a malicious switch in the data plane or a host connected to the network) can use teleportation to transmit information via the control plane and bypass critical network functions in the data plane (e.g., a firewall), and to violate security policies as well as logical and even physical separations. This paper characterizes the design space for teleportation attacks theoretically, and then identifies four different teleportation techniques. We demonstrate and discuss how these techniques can be exploited for different attacks (e.g., exfiltrating confidential data at high rates), and also initiate the discussion of possible countermeasures. Generally, and given today's trend toward more intent-based networking, we believe that our findings are relevant beyond the use cases considered in this paper.Comment: Accepted in EuroSP'1

    DYNAMIC DATA EXFILTRATION OVER COMMON PROTOCOLS VIA SOCKET LAYER PROTOCOL CUSTOMIZATION

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    Obfuscated data exfiltration perpetrated by malicious actors presents a significant threat to organizations looking to protect sensitive data. Socket layer protocol customization presents the potential to enhance obfuscated data exfiltration by providing a protocol-agnostic means of embedding targeted data within application payloads of established socket connections. Fully evaluating and characterizing this technique will serve as an important step in the development of suitable mitigations. This thesis evaluated the performance of this method of data exfiltration through experimentation to determine its viability and identify its limitations. The evaluation assessed the effectiveness of exfiltration via socket layer customization with various application protocols and characterized its use to determine the most suitable protocols. Basic host-based and network-based security controls were introduced to test the exfiltration method’s ability to bypass typical security controls implemented to prevent data exfiltration. The experimentation results indicate that this exfiltration method is both viable and applicable across multiple application protocols. It proved flexible enough in its design and configuration to bypass basic host-based access controls and general network intrusion prevention system packet inspection. Deep packet inspection was identified as a potential solution; however, the required inspection and filtering granularity might make implementation infeasible.Office of Naval Research, Arlington, VA 22203-1995Outstanding ThesisPetty Officer First Class, United States NavyApproved for public release. Distribution is unlimited

    Towards a Near-real-time Protocol Tunneling Detector based on Machine Learning Techniques

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    In the very last years, cybersecurity attacks have increased at an unprecedented pace, becoming ever more sophisticated and costly. Their impact has involved both private/public companies and critical infrastructures. At the same time, due to the COVID-19 pandemic, the security perimeters of many organizations expanded, causing an increase of the attack surface exploitable by threat actors through malware and phishing attacks. Given these factors, it is of primary importance to monitor the security perimeter and the events occurring in the monitored network, according to a tested security strategy of detection and response. In this paper, we present a protocol tunneling detector prototype which inspects, in near real time, a company's network traffic using machine learning techniques. Indeed, tunneling attacks allow malicious actors to maximize the time in which their activity remains undetected. The detector monitors unencrypted network flows and extracts features to detect possible occurring attacks and anomalies, by combining machine learning and deep learning. The proposed module can be embedded in any network security monitoring platform able to provide network flow information along with its metadata. The detection capabilities of the implemented prototype have been tested both on benign and malicious datasets. Results show 97.1% overall accuracy and an F1-score equals to 95.6%.Comment: 12 pages, 4 figures, 4 table
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