Biblio

Biometry is often used as a part of the multi-factor authentication in order to improve the security of IT systems. In this paper, we propose the palmprint-based solution for user identity verification. In particular, we present a new approach to feature extraction. The proposed method is based both on texture and color information. Our experiments show that using the proposed hybrid features allows for achieving satisfactory accuracy without increasing requirements for additional computational resources. It is important from our perspective since the proposed method is dedicated to smartphones and other handhelds in mobile verification scenarios.

Numerous IoT applications, like building automation or process control of industrial sites, exist today. These applications inherently have a strong connection to the physical world. Hence, IT security threats cannot only cause problems like data leaks but also safety issues which might harm people. Attacks on IT systems are not only performed by outside attackers but also insiders like administrators. For this reason, we present ongoing work on a Byzantine fault tolerant configuration management system (CMS) that provides control over administrators, restrains their rights, and enforces separation of concerns. We reach this goal by conducting a configuration management process that requires multi-party authorization for critical configurations to prevent individual malicious administrators from performing undesired actions. Only after a configuration has been authorized by multiple experts, it is applied to the targeted devices. For the whole configuration management process, our CMS guarantees accountability and traceability. Lastly, our system is tamper-resistant as we leverage Hyperledger Fabric, which provides a distributed execution environment for our CMS and a blockchain-based distributed ledger that we use to store the configurations. A beneficial side effect of this approach is that our CMS is also suitable to manage configurations for infrastructure shared across different organizations that do not need to trust each other.

The increasing amount of malware variants seen in the wild is causing problems for Antivirus Software vendors, unable to keep up by creating signatures for each. The methods used to develop a signature, static and dynamic analysis, have various limitations. Machine learning has been used by Antivirus vendors to detect malware based on the information gathered from the analysis process. However, adversarial examples can cause machine learning algorithms to miss-classify new data. In this paper we describe a method for malware analysis by converting malware binaries to images and then preparing those images for training within a Generative Adversarial Network. These unsupervised deep neural networks are not susceptible to adversarial examples. The conversion to images from malware binaries should be faster than using dynamic analysis and it would still be possible to link malware families together. Using the Generative Adversarial Network, malware detection could be much more effective and reliable.

IT system risk assessments are indispensable due to increasing cyber threats within our ever-growing IT systems. Moreover, laws and regulations urge organizations to conduct risk assessments regularly. Even though there exist several risk management frameworks and methodologies, they are in general high level, not defining the risk metrics, risk metrics values and the detailed risk assessment formulas for different risk views. To address this need, we define a novel risk assessment methodology specific to IT systems. Our model is quantitative, both asset and vulnerability centric and defines low and high level risk metrics. High level risk metrics are defined in two general categories; base and attack graph-based. In our paper, we provide a detailed explanation of formulations in each category and make our implemented software publicly available for those who are interested in applying the proposed methodology to their IT systems.

The theory of robust control models the controller-disturbance interaction as a game where disturbance is nonstrategic. The proviso of a deliberately malicious (strategic) attacker should be considered to increase the robustness of infrastructure systems. This has become especially important since many IT systems supporting critical functionalities are vulnerable to exploits by attackers. While the usefulness of game theory methods for modeling cyber-security is well established in the literature, new game theoretic models of cyber-physical security are needed for deriving useful insights on "optimal" attack plans and defender responses, both in terms of allocation of resources and operational strategies of these players. This whitepaper presents some progress and challenges in using game-theoretic models for security of infrastructure networks. Main insights from the following models are presented: (i) Network security game on flow networks under strategic edge disruptions; (ii) Interdiction problem on distribution networks under node disruptions; (iii) Inspection game to monitor commercial non-technical losses (e.g. energy diversion); and (iv) Interdependent security game of networked control systems under communication failures. These models can be used to analyze the attacker-defender interactions in a class of cyber-physical security scenarios.

Industrial Control Systems (ICS) which among others are comprised of Supervisory Control and Data Acquisition (SCADA) and Distributed Control Systems (DCS) are used to control industrial processes. ICS have now been connected to other Information Technology (IT) systems and have as a result become vulnerable to Advanced Persistent Threats (APT). APTs are targeted attacks that use zero-day attacks to attack systems. Current ICS security mechanisms fail to deter APTs from infiltrating ICS. An analysis of possible solutions to deter APTs was done. This paper proposes the use of Artificial Immune Systems to secure ICS from APTs.