Biblio

Protecting critical infrastructure from cyber threats is one of the most important obligations of governments to ensure the national and social security of the society. Developing national cyber situational awareness platform provides a protection of critical infrastructures. In such a way, each infrastructure, independently, generates its own situational awareness and shares it with other infrastructures through a national sharing and alerting center. The national information sharing and alerting center collects cyber information of infrastructures and draws a picture of national situational awareness by examining the potential effects of received threats on other infrastructures and predicting the national cyber status in near future. This paper represents the conceptual architecture for such national sharing system and suggests some brief description of its implementation.

Nowadays, phishing is one of the most usual web threats with regards to the significant growth of the World Wide Web in volume over time. Phishing attackers always use new (zero-day) and sophisticated techniques to deceive online customers. Hence, it is necessary that the anti-phishing system be real-time and fast and also leverages from an intelligent phishing detection solution. Here, we develop a reliable detection system which can adaptively match the changing environment and phishing websites. Our method is an online and feature-rich machine learning technique to discriminate the phishing and legitimate websites. Since the proposed approach extracts different types of discriminative features from URLs and webpages source code, it is an entirely client-side solution and does not require any service from the third-party. The experimental results highlight the robustness and competitiveness of our anti-phishing system to distinguish the phishing and legitimate websites.