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Adversarial attacks have recently gained popularity due to their simplicity, impact, and applicability to a wide range of machine learning scenarios. However, knowledge of a particular security scenario can be advantageous for adversaries to craft better attacks. In other words, in some scenarios, attackers may come up naturally with ad hoc black-box attack techniques inspired directly by problem space characteristics rather than using generic adversarial techniques. This paper explores an intuitive attack technique based on reusing legitimate user inputs and applying it to mouse-based behavioral biometrics and keyboard-based behavioral biometrics. Moreover, it compares the model's effectiveness against adversarial machine learning attacks, achieving attack success rates up to 87 and 86% for the mouse and keyboard settings, respectively. We show that attacks leveraging domain knowledge have higher transferability when applied to various machine-learning techniques and are more challenging to defend against. We also propose countermeasures against such attacks and discuss their effectiveness. [ABSTRACT FROM AUTHOR]

The Domain Name System (DNS) exfiltration is an activity in which an infected device sends data to the attacker's server by encoding it in DNS request messages. Because of the frequent use of DNS exfiltration for malicious purposes, exfiltration detection gained attention from the research community which proposed several predominantly machine learning-based methods. The majority of previous studies used publicly available DNS exfiltration tools with the default configuration parameters, resulting in datasets created from DNS exfiltration requests that are usually significantly longer, have more DNS name labels, and higher character entropy than average regular DNS requests. This further led to overly optimistic detection rates. In this paper, we have explored some of the strategies an attacker could use to avoid exfiltration detection. First, we have explored the impact of DNS exfiltration tools' parameter variation on the exfiltration detection accuracy. Second, we have modified the DNSExfiltrator tool to produce exfiltration requests which have significantly lower character entropy. This approach proved to be capable of deceiving classifiers based on single DNS request features. Only around 1% of modified DNS requests shorter or equal to 9 bytes, and less than one third of DNS exfiltration requests in the overall population were accurately detected. In addition, we present a methodology and an aggregated feature set (including inter-request timing statistics) which can be used for accurate DNS exfiltration in this kind of adversarial settings. [ABSTRACT FROM AUTHOR]