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

Intrusion Detection for Cyber-Physical Attacks in Cyber-Manufacturing System

In the vision of Cyber-Manufacturing System (CMS) , the physical components such as products, machines, and tools are connected, identifiable and can communicate via the industrial network and the Internet. This integration of connectivity enables manufacturing systems access to computational resources, such as cloud computing, digital twin, and blockchain. The connected manufacturing systems are expected to be more efficient, sustainable and cost-effective. However, the extensive connectivity also increases the vulnerability of physical components. The attack surface of a connected manufacturing environment is greatly enlarged. Machines, products and tools could be targeted by cyber-physical attacks via the network. Among many emerging security concerns, this research focuses on the intrusion detection of cyber-physical attacks. The Intrusion Detection System (IDS) is used to monitor cyber-attacks in the computer security domain. For cyber-physical attacks, however, there is limited work. Currently, the IDS cannot effectively address cyber-physical attacks in manufacturing system: (i) the IDS takes time to reveal true alarms, sometimes over months; (ii) manufacturing production life-cycle is shorter than the detection period, which can cause physical consequences such as defective products and equipment damage; (iii) the increasing complexity of network will also make the detection period even longer. This gap leaves the cyber-physical attacks in manufacturing to cause issues like over-wearing, breakage, defects or any other changes that the original design didn’t intend. A review on the history of cyber-physical attacks, and available detection methods are presented. The detection methods are reviewed in terms of intrusion detection algorithms, and alert correlation methods. The attacks are further broken down into a taxonomy covering four dimensions with over thirty attack scenarios to comprehensively study and simulate cyber-physical attacks. A new intrusion detection and correlation method was proposed to address the cyber-physical attacks in CMS. The detection method incorporates IDS software in cyber domain and machine learning analysis in physical domain. The correlation relies on a new similarity-based cyber-physical alert correlation method. Four experimental case studies were used to validate the proposed method. Each case study focused on different aspects of correlation method performance. The experiments were conducted on a security-oriented manufacturing testbed established for this research at Syracuse University. The results showed the proposed intrusion detection and alert correlation method can effectively disclose unknown attack, known attack and attack interference that causes false alarms. In case study one, the alarm reduction rate reached 99.1%, with improvement of detection accuracy from 49.6% to 100%. The case studies also proved the proposed method can mitigate false alarms, detect attacks on multiple machines, and attacks from the supply chain. This work contributes to the security domain in cyber-physical manufacturing systems, with the focus on intrusion detection. The dataset collected during the experiments has been shared with the research community. The alert correlation methodology also contributes to cyber-physical systems, such as smart grid and connected vehicles, which requires enhanced security protection in today’s connected world

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

Actionable Intelligence-Oriented Cyber Threat Modeling Framework

Amid the growing challenges of cybersecurity, the new paradigm of cyber threat intelligence (or CTI) has gained momentum to better deal with cyber threats. There, however, has been one fundamental and very practical problem of information overload organizations face in constructing an effective CTI program. We developed a cyber threat intelligence prototype that automatically and dynamically performs the correlation of business assets, vulnerabilities, and cyber threat information in a scoped setting to remediate the challenge of information overload. Conveniently called TIME (for Threat Intelligence Modeling Environment), it repeats the cycle of: (1) collect internal asset data; (2) gather vulnerability and threat data; (3) correlate vulnerabilities with assets; and (4) derive CTI and alerts significant internal asset-related vulnerabilities in a timely manner. For this, it takes advantage of CTI reports produced by online sites and several NIST standards intended to formalize vulnerability and threat management

An Assessment of Practical Hands-On Lab Activities in Network Security Management

With the advancement in technology over the past decades, networks have become increasingly large and complex. In the meantime, cyberattacks have become highly sophisticated making them difficult to detect. These changes make securing a network more challenging than ever before. Hence, it is critical to prepare a comprehensive guide of network security management for students assist them in becoming network security professionals. The objective of this paper is to introduce a variety of techniques related to network security management, such as Simple Network Management Protocol (SNMP), event management, security policy management, risk management, access control, and remote monitoring. With the usage of these techniques, malicious activities from outsiders and misuse by insiders can be effectively monitored and managed. A network learning environment is proposed for students to practice network security management experiments. In addition, hands-on lab exercises are suggested. These activities will help students become familiar with the operations of network security management and allow them to further apply practical skills to protect networks

Minimization of DDoS false alarm rate in Network Security; Refining fusion through correlation

Intrusion Detection Systems are designed to monitor a network environment and generate alerts whenever abnormal activities are detected. However, the number of these alerts can be very large making their evaluation a difficult task for a security analyst. Alert management techniques reduce alert volume significantly and potentially improve detection performance of an Intrusion Detection System. This thesis work presents a framework to improve the effectiveness and efficiency of an Intrusion Detection System by significantly reducing the false positive alerts and increasing the ability to spot an actual intrusion for Distributed Denial of Service attacks. Proposed sensor fusion technique addresses the issues relating the optimality of decision-making through correlation in multiple sensors framework. The fusion process is based on combining belief through Dempster Shafer rule of combination along with associating belief with each type of alert and combining them by using Subjective Logic based on Jøsang theory. Moreover, the reliability factor for any Intrusion Detection System is also addressed accordingly in order to minimize the chance of false diagnose of the final network state. A considerable number of simulations are conducted in order to determine the optimal performance of the proposed prototype

Artificial Intelligence for Cybersecurity: Towards Taxonomy-based Archetypes and Decision Support

Cybersecurity is a critical success factor for more resilient companies, organizations, and societies against cyberattacks. Artificial intelligence (AI)-driven cybersecurity solutions have the ability to detect and respond to cyber threats and attacks and other malicious activities. For this purpose, the most important resource is security-relevant data from networks, cloud systems, clients, e-mails, and previous cyberattacks. AI, the key technology, can automatically detect, for example, anomalies and malicious behavior. Consequently, the market for AI-driven cybersecurity solutions is growing significantly. We develop a taxonomy of AI-driven cybersecurity business models by classifying 229 real-world services. Building on that, we derive four specific archetypes using a cluster analysis toward a comprehensive academic knowledge base of business model elements. To reduce complexity and simplify the results of the taxonomy and archetypes, we propose DETRAICS, a decision tree for AI-driven cybersecurity services. Practitioners, decision-makers, and researchers benefit from DETRAICS to select the most suitable AI-driven service