Biblio

As malware detection algorithms and methods become more sophisticated, malware authors adopt equally sophisticated evasion mechanisms to defeat them. Anecdotal evidence claims Living-Off-The-Land (LotL) techniques are one of the major evasion techniques used in many malware attacks. These techniques leverage binaries already present in the system to conduct malicious actions. We present the first large-scale systematic investigation of the use of these techniques by malware on Windows systems.In this paper, we analyse how common the use of these native system binaries is across several malware datasets, containing a total of 31,805,549 samples. We identify an average 9.41% prevalence. Our results show that the use of LotL techniques is prolific, particularly in Advanced Persistent Threat (APT) malware samples where the prevalence is 26.26%, over twice that of commodity malware.To illustrate the evasive potential of LotL techniques, we test the usage of LotL techniques against several fully patched Windows systems in a local sandboxed environment and show that there is a generalised detection gap in 10 of the most popular anti-virus products.

In this paper, we propose a novel method, based on keystroke dynamics, to distinguish between fake and truthful personal information written via a computer keyboard. Our method does not need any prior knowledge about the user who is providing data. To our knowledge, this is the first work that associates the typing human behavior with the production of lies regarding personal information. Via experimental analysis involving 190 subjects, we assess that this method is able to distinguish between truth and lies on specific types of autobiographical information, with an accuracy higher than 75%. Specifically, for information usually required in online registration forms (e.g., name, surname and email), the typing behavior diverged significantly between truthful or untruthful answers. According to our results, keystroke analysis could have a great potential in detecting the veracity of self-declared information, and it could be applied to a large number of practical scenarios requiring users to input personal data remotely via keyboard.

In recent years, simple password-based authentication systems have increasingly proven ineffective for many classes of real-world devices. As a result, many researchers have concentrated their efforts on the design of new biometric authentication systems. This trend has been further accelerated by the advent of mobile devices, which offer numerous sensors and capabilities to implement a variety of mobile biometric authentication systems. Along with the advances in biometric authentication, however, attacks have also become much more sophisticated and many biometric techniques have ultimately proven inadequate in face of advanced attackers in practice. In this paper, we investigate the effectiveness of sensor-enhanced keystroke dynamics, a recent mobile biometric authentication mechanism that combines a particularly rich set of features. In our analysis, we consider different types of attacks, with a focus on advanced attacks that draw from general population statistics. Such attacks have already been proven effective in drastically reducing the accuracy of many state-of-the-art biometric authentication systems. We implemented a statistical attack against sensor-enhanced keystroke dynamics and evaluated its impact on detection accuracy. On one hand, our results show that sensor-enhanced keystroke dynamics are generally robust against statistical attacks with a marginal equal-error rate impact (textless0.14%). On the other hand, our results show that, surprisingly, keystroke timing features non-trivially weaken the security guarantees provided by sensor features alone. Our findings suggest that sensor dynamics may be a stronger biometric authentication mechanism against recently proposed practical attacks.

The Internet of Things (IoT) paradigm, in conjunction with the one of smart cities, is pursuing toward the concept of smart buildings, i.e., “intelligent” buildings able to receive data from a network of sensors and thus to adapt the environment. IoT sensors can monitor a wide range of environmental features such as the energy consumption inside a building at fine-grained level (e.g., for a specific wall-socket). Some smart buildings already deploy energy monitoring in order to optimize the energy use for good purposes (e.g., to save money, to reduce pollution). Unfortunately, such measurements raise a significant amount of privacy concerns. In this paper, we investigate the feasibility of recognizing the pair laptop-user (i.e., a user using her own laptop) from the energy traces produced by her laptop. We design MTPlug, a framework that achieves this goal relying on supervised machine learning techniques as pattern recognition in multivariate time series. We present a comprehensive implementation of this system and run a thorough set of experiments. In particular, we collected data by monitoring the energy consumption of two groups of laptop users, some office employees and some intruders, for a total of 27 people. We show that our system is able to build an energy profile for a laptop user with accuracy above 80%, in less than 3.5 hours of laptop usage. To the best of our knowledge, this is the first research that assesses the feasibility of laptop users profiling relying uniquely on fine-grained energy traces collected using wall-socket smart meters.