An Expert System for Automatic Software Protection

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A machine learning approach for smart computer security audit

This thesis presents a novel application of machine learning technology to automate network security audit and penetration testing processes in particular. A model-free reinforcement learning approach is presented. It is characterized by the absence of the environmental model. The model is derived autonomously by the audit system while acting in the tested computer network. The penetration testing process is specified as a Markov decision process (MDP) without definition of reward and transition functions for every state/action pair. The presented approach includes application of traditional and modified Q-learning algorithms. A traditional Q-learning algorithm learns the action-value function stored in the table, which gives the expected utility of executing a particular action in a particular state of the penetration testing process. The modified Q-learning algorithm differs by incorporation of the state space approximator and representation of the action-value function as a linear combination of features. Two deep architectures of the approximator are presented: autoencoder joint with artificial neural network (ANN) and autoencoder joint with recurrent neural network (RNN). The autoencoder is used to derive the feature set defining audited hosts. ANN is intended to approximate the state space of the audit process based on derived features. RNN is a more advanced version of the approximator and differs by the existence of the additional loop connections from hidden to input layers of the neural network. Such architecture incorporates previously executed actions into new inputs. It gives the opportunity to audit system learn sequences of actions leading to the goal of the audit, which is defined as receiving administrator rights on the host. The model-free reinforcement learning approach based on traditional Q-learning algorithms was also applied to reveal new vulnerabilities, buffer overflow in particular. The penetration testing system showed the ability to discover a string, exploiting potential vulnerability, by learning its formation process on the go. In order to prove the concept and to test the efficiency of an approach, audit tool was developed. Presented results are intended to demonstrate the adaptivity of the approach, performance of the algorithms and deep machine learning architectures. Different sets of hyperparameters are compared graphically to test the ability of convergence to the optimal action policy. An action policy is a sequence of actions, leading to the audit goal (getting admin rights on the remote host). The testing environment is also presented. It consists of 80+ virtual machines based on a vSphere virtualization platform. This combination of hosts represents a typical corporate network with Users segment, Demilitarized zone (DMZ) and external segment (Internet). The network has typical corporate services available: web server, mail server, file server, SSH, SQL server. During the testing process, the audit system acts as an attacker from the Internet

Towards Automatic Risk Analysis and Mitigation of Software Applications

This paper proposes a novel semi-automatic risk analysis approach that not only identifies the threats against the assets in a software application, but it is also able to quantify their risks and to suggests the software protections to mitigate them. Built on a formal model of the software, attacks, protections and their relationships, our implementation has shown promising performance on real world applications. This work represents a first step towards a user-friendly expert system for the protection of software applications

Assessing and augmenting SCADA cyber security: a survey of techniques

SCADA systems monitor and control critical infrastructures of national importance such as power generation and distribution, water supply, transportation networks, and manufacturing facilities. The pervasiveness, miniaturisations and declining costs of internet connectivity have transformed these systems from strictly isolated to highly interconnected networks. The connectivity provides immense benefits such as reliability, scalability and remote connectivity, but at the same time exposes an otherwise isolated and secure system, to global cyber security threats. This inevitable transformation to highly connected systems thus necessitates effective security safeguards to be in place as any compromise or downtime of SCADA systems can have severe economic, safety and security ramifications. One way to ensure vital asset protection is to adopt a viewpoint similar to an attacker to determine weaknesses and loopholes in defences. Such mind sets help to identify and fix potential breaches before their exploitation. This paper surveys tools and techniques to uncover SCADA system vulnerabilities. A comprehensive review of the selected approaches is provided along with their applicability