Realtime Intrusion Risk Assessment Model based on Attack and Service Dependency Graphs

Network services are becoming larger and increasingly complex to manage. It is extremely critical to maintain the users QoS, the response time of applications, and critical services in high demand. On the other hand, we see impressive changes in the ways in which attackers gain access to systems and infect services. When an attack is detected, an Intrusion Response System (IRS) is responsible to accurately assess the value of the loss incurred by a compromised resource and apply the proper responses to mitigate attack. Without having a proper risk assessment, our automated IRS will reduce network performance, wrongly disconnect users from the network, or result in high costs for administrators reestablishing services, and become a DoS attack for our network, which will eventually have to be disabled. In this paper, we address these challenges and we propose a new model to combine the Attack Graph and Service Dependency Graph approaches to calculate the impact of an attack more accurately compared to other existing solutions. To show the effectiveness of our model, a sophisticated multi-step attack was designed to compromise a web server, as well as to acquire root privilege. Our results illustrate the efficiency of the proposed model and confirm the feasibility of the approach in real-time

Advanced reaction using risk assessment in intrusion detection systems

International audienceCurrent intrusion detection systems go beyond the detection of attacks and provide reaction mechanisms to cope with detected attacks or at least reduce their effect. Previous research works have proposed methods to automatically select possible countermeasures capable of ending the detected attack. But actually, countermeasures have side effects and can be as harmful as the detected attack. In this paper, we propose to improve the reaction selection process by giving means to quantify the effectiveness and select the countermeasure that has the minimum negative side effect on the information system. To achieve this goal, we adopt a risk assessment and analysis approach