Biblio

The current authentication systems based on password and pin code are not enough to guarantee attacks from malicious users. For this reason, in the last years, several studies are proposed with the aim to identify the users basing on their typing dynamics. In this paper, we propose a deep neural network architecture aimed to discriminate between different users using a set of keystroke features. The idea behind the proposed method is to identify the users silently and continuously during their typing on a monitored system. To perform such user identification effectively, we propose a feature model able to capture the typing style that is specific to each given user. The proposed approach is evaluated on a large dataset derived by integrating two real-world datasets from existing studies. The merged dataset contains a total of 1530 different users each writing a set of different typing samples. Several deep neural networks, with an increasing number of hidden layers and two different sets of features, are tested with the aim to find the best configuration. The final best classifier scores a precision equal to 0.997, a recall equal to 0.99 and an accuracy equal to 99% using an MLP deep neural network with 9 hidden layers. Finally, the performances obtained by using the deep learning approach are also compared with the performance of traditional decision-trees machine learning algorithm, attesting the effectiveness of the deep learning-based classifiers in the domain of keystroke analysis.

SCADA control systems use programmable logic controller to interface with critical machines. SCADA systems are used in critical infrastructures, for instance, to control smart grid, oil pipelines, water distribution and chemical manufacturing plants: an attacker taking control of a SCADA system could cause various damages, both to the infrastructure but also to people (for instance, adding chemical substances into a water distribution systems). In this paper we propose a method to detect attacks targeting SCADA systems. We exploit model checking, in detail we model logs from SCADA systems into a network of timed automata and, through timed temporal logic, we characterize the behaviour of a SCADA system under attack. Experiments performed on a SCADA water distribution system confirmed the effectiveness of the proposed method.

Phishing is a technique aimed to imitate an official websites of any company such as banks, institutes, etc. The purpose of phishing is to theft private and sensitive credentials of users such as password, username or PIN. Phishing detection is a technique to deal with this kind of malicious activity. In this paper we propose a method able to discriminate between web pages aimed to perform phishing attacks and legitimate ones. We exploit state of the art machine learning algorithms in order to build models using indicators that are able to detect phishing activities.

Distributed environments such as Internet of Things, have an increasing need of introducing access and usage control mechanisms, to manage the rights to perform specific operations and regulate the access to the plethora of information daily generated by these devices. Defining policies which are specific to these distributed environments could be a challenging and tedious task, mainly due to the large set of attributes that should be considered, hence the upcoming of unforeseen conflicts or unconsidered conditions. In this paper we propose a qualitative risk-based usage control model, aimed at enabling a framework where is possible to define and enforce policies at different levels of granularity. In particular, the proposed framework exploits the Analytic Hierarchy Process (AHP) to coalesce the risk value assigned to different attributes in relation to a specific operation, in a single risk value, to be used as unique attribute of usage control policies. Two sets of experiments that show the benefits both in policy definition and in performance, validate the proposed model, demonstrating the equivalence of enforcement among standard policies and the derived single-attributed policies.

Android operating system is constantly overwhelmed by new sophisticated threats and new zero-day attacks. While aggressive malware, for instance malicious behaviors able to cipher data files or lock the GUI, are not worried to circumvention users by infection (that can try to disinfect the device), there exist malware with the aim to perform malicious actions stealthy, i.e., trying to not manifest their presence to the users. This kind of malware is less recognizable, because users are not aware of their presence. In this paper we propose FormalDroid, a tool able to detect silent malicious beaviours and to localize the malicious payload in Android application. Evaluating real-world malware samples we obtain an accuracy equal to 0.94.