A risk index model for security incident prioritisation

With thousands of incidents identified by security appliances every day, the process of distinguishing which incidents are important and which are trivial is complicated. This paper proposes an incident prioritisation model, the Risk Index Model (RIM), which is based on risk assessment and the Analytic Hierarchy Process (AHP). The model uses indicators, such as criticality, maintainability, replaceability, and dependability as decision factors to calculate incidents’ risk index. The RIM was validated using the MIT DARPA LLDOS 1.0 dataset, and the results were compared against the combined priorities of the Common Vulnerability Scoring System (CVSS) v2 and Snort Priority. The experimental results have shown that 100% of incidents could be rated with RIM, compared to only 17.23% with CVSS. In addition, this study also improves the limitation of group priority in the Snort Priority (e.g. high, medium and low priority) by quantitatively ranking, sorting and listing incidents according to their risk index. The proposed study has also investigated the effect of applying weighted indicators at the calculation of the risk index, as well as the effect of calculating them dynamically. The experiments have shown significant changes in the resultant risk index as well as some of the top priority rankings

System Health Monitoring Using a Novel Method: Security Unified Process

Iterative and incremental mechanisms are not usually considered in existing approaches for information security management System (ISMS). In this paper, we propose SUP (security unified process) as a unified process to implement a successful and high-quality ISMS. A disciplined approach can be provided by SUP to assign tasks and responsibilities within an organization. The SUP architecture comprises static and dynamic dimensions; the static dimension, or disciplines, includes business modeling, assets, security policy, implementation, configuration and change management, and project management. The dynamic dimension, or phases, contains inception, analysis and design, construction, and monitoring. Risk assessment is a major part of the ISMS process. In SUP, we present a risk assessment model, which uses a fuzzy expert system to assess risks in organization. Since, the classification of assets is an important aspect of risk management and ensures that effective protection occurs, a Security Cube is proposed to identify organization assets as an asset classification model. The proposed model leads us to have an offline system health monitoring tool that is really a critical need in any organization

System health monitoring using a novel method : security unified process

Iterative and incremental mechanisms are not usually considered in existing approaches for information security management System (ISMS). In this paper, we propose SUP (security unified process) as a unified process to implement a successful and highquality ISMS. A disciplined approach can be provided by SUP to assign tasks and responsibilities within an organization. The SUP architecture comprises static and dynamic dimensions; the static dimension, or disciplines, includes business modeling, assets, security policy, implementation, configuration and change management, and project management. The dynamic dimension, or phases, contains inception, analysis and design, construction, and monitoring. Risk assessment is a major part of the ISMS process. In SUP, we present a risk assessment model, which uses a fuzzy expert system to assess risks in organization. Since, the classification of assets is an important aspect of risk management and ensures that effective protection occurs, a Security Cube is proposed to identify organization assets as an asset classification model. The proposed model leads us to have an offline system health monitoring tool that is really a critical need in any organization

Incident Prioritisation for Intrusion Response Systems

The landscape of security threats continues to evolve, with attacks becoming more serious and the number of vulnerabilities rising. To manage these threats, many security studies have been undertaken in recent years, mainly focusing on improving detection, prevention and response efficiency. Although there are security tools such as antivirus software and firewalls available to counter them, Intrusion Detection Systems and similar tools such as Intrusion Prevention Systems are still one of the most popular approaches. There are hundreds of published works related to intrusion detection that aim to increase the efficiency and reliability of detection, prevention and response systems. Whilst intrusion detection system technologies have advanced, there are still areas available to explore, particularly with respect to the process of selecting appropriate responses. Supporting a variety of response options, such as proactive, reactive and passive responses, enables security analysts to select the most appropriate response in different contexts. In view of that, a methodical approach that identifies important incidents as opposed to trivial ones is first needed. However, with thousands of incidents identified every day, relying upon manual processes to identify their importance and urgency is complicated, difficult, error-prone and time-consuming, and so prioritising them automatically would help security analysts to focus only on the most critical ones. The existing approaches to incident prioritisation provide various ways to prioritise incidents, but less attention has been given to adopting them into an automated response system. Although some studies have realised the advantages of prioritisation, they released no further studies showing they had continued to investigate the effectiveness of the process. This study concerns enhancing the incident prioritisation scheme to identify critical incidents based upon their criticality and urgency, in order to facilitate an autonomous mode for the response selection process in Intrusion Response Systems. To achieve this aim, this study proposed a novel framework which combines models and strategies identified from the comprehensive literature review. A model to estimate the level of risks of incidents is established, named the Risk Index Model (RIM). With different levels of risk, the Response Strategy Model (RSM) dynamically maps incidents into different types of response, with serious incidents being mapped to active responses in order to minimise their impact, while incidents with less impact have passive responses. The combination of these models provides a seamless way to map incidents automatically; however, it needs to be evaluated in terms of its effectiveness and performances. To demonstrate the results, an evaluation study with four stages was undertaken; these stages were a feasibility study of the RIM, comparison studies with industrial standards such as Common Vulnerabilities Scoring System (CVSS) and Snort, an examination of the effect of different strategies in the rating and ranking process, and a test of the effectiveness and performance of the Response Strategy Model (RSM). With promising results being gathered, a proof-of-concept study was conducted to demonstrate the framework using a live traffic network simulation with online assessment mode via the Security Incident Prioritisation Module (SIPM); this study was used to investigate its effectiveness and practicality. Through the results gathered, this study has demonstrated that the prioritisation process can feasibly be used to facilitate the response selection process in Intrusion Response Systems. The main contribution of this study is to have proposed, designed, evaluated and simulated a framework to support the incident prioritisation process for Intrusion Response Systems.Ministry of Higher Education in Malaysia and University of Malay

Asset Priority Risk Assessment Using Hidden Markov Models

Conducting risk assessment on organizational assets can be time consuming, burdensome, and misleading in many cases because of the dynamically changing security states of assets. Risk assessments may present inaccurate or false data if the organizational assets change in their security postures. Each asset can change its security status from secure, mitigated, vulnerable, or compromised states. The secure state is only temporary and imaginary; it may never exist. Therefore, it is accurate to say that each asset changes its security state within its mitigated, vulnerable, or compromised, state. If we can predict each asset’s security state prior to its actual state, we would have a good risk indicator for the organization’s mission-critical assets. In this paper, we explore possible security states from the insider’s perspective, as there are more security incidents initiated from inside than outside an organization. However, we are in a continuous loop of mitigating dynamically changing assets caused by both internal and external threats