Biblio

Information security management is time-consuming and error-prone. Apart from day-to-day operations, organizations need to comply with industrial regulations or government directives. Thus, organizations are looking for security tools to automate security management tasks and daily operations. Security Content Automation Protocol (SCAP) is a suite of specifications that help to automate security management tasks such as vulnerability measurement and policy compliance evaluation. SCAP benchmark provides detailed guidance on setting the security configuration of network devices, operating systems, and applications. Organizations can use SCAP benchmark to perform automated configuration compliance assessment on network devices, operating systems, and applications. This paper discusses SCAP benchmark components and the development of a SCAP benchmark for automating Cisco router security configuration compliance.

Due to the fact that the cyber security risks exist in industrial control system, risk assessment on Industrial Automation Platform (IAP) is discussed in this paper. The cyber security assessment model for IAP is built based on relevant standards at abroad. Fuzzy analytic hierarchy process and fuzzy comprehensive evaluation method based on entropy theory are utilized to evaluate the communication links' risk of IAP software. As a result, the risk weight of communication links which have impacts on platform and the risk level of this platform are given for further study on protective strategy. The assessment result shows that the methods used can evaluate this platform efficiently and practically.

Humans can easily find themselves in high cost situations where they must choose between suggestions made by an automated decision aid and a conflicting human decision aid. Previous research indicates that humans often rely on automation or other humans, but not both simultaneously. Expanding on previous work conducted by Lyons and Stokes (2012), the current experiment measures how trust in automated or human decision aids differs along with perceived risk and workload. The simulated task required 126 participants to choose the safest route for a military convoy; they were presented with conflicting information from an automated tool and a human. Results demonstrated that as workload increased, trust in automation decreased. As the perceived risk increased, trust in the human decision aid increased. Individual differences in dispositional trust correlated with an increased trust in both decision aids. These findings can be used to inform training programs for operators who may receive information from human and automated sources. Examples of this context include: air traffic control, aviation, and signals intelligence.

This paper proposes a methodology to assess cyber-related risks and to identify critical assets both at power grid and substation levels. The methodology is based on a two-pass engine model. The first pass engine is developed to identify the most critical substation(s) in a power grid. A mixture of Analytical hierarchy process (AHP) and (N-1) contingent analysis is used to calculate risks. The second pass engine is developed to identify risky assets within a substation and improve the vulnerability of a substation against the intrusion and malicious acts of cyber hackers. The risk methodology uniquely combines asset reliability, vulnerability and costs of attack into a risk index. A methodology is also presented to improve the overall security of a substation by optimally placing security agent(s) on the automation system.
 

This paper presents an ontological approach to perceive the current security status of the network. Computer network is a dynamic entity whose state changes with the introduction of new services, installation of new network operating system, and addition of new hardware components, creation of new user roles and by attacks from various actors instigated by aggressors. Various security mechanisms employed in the network does not give the complete picture of security of complete network. In this paper we have proposed taxonomy and ontology which may be used to infer impact of various events happening in the network on security status of the network. Vulnerability, Network and Attack are the main taxonomy classes in the ontology. Vulnerability class describes various types of vulnerabilities in the network which may in hardware components like storage devices, computing devices or networks devices. Attack class has many subclasses like Actor class which is entity executing the attack, Goal class describes goal of the attack, Attack mechanism class defines attack methodology, Scope class describes size and utility of the target, Automation level describes the automation level of the attack Evaluation of security status of the network is required for network security situational awareness. Network class has network operating system, users, roles, hardware components and services as its subclasses. Based on this taxonomy ontology has been developed to perceive network security status. Finally a framework, which uses this ontology as knowledgebase has been proposed.
 

Malicious applications can be introduced to attack users and services so as to gain financial rewards, individuals' sensitive information, company and government intellectual property, and to gain remote control of systems. However, traditional methods of malicious code detection, such as signature detection, behavior detection, virtual machine detection, and heuristic detection, have various weaknesses which make them unreliable. This paper presents the existing technologies of malicious code detection and a malicious code detection model is proposed based on behavior association. The behavior points of malicious code are first extracted through API monitoring technology and integrated into the behavior; then a relation between behaviors is established according to data dependence. Next, a behavior association model is built up and a discrimination method is put forth using pushdown automation. Finally, the exact malicious code is taken as a sample to carry out an experiment on the behavior's capture, association, and discrimination, thus proving that the theoretical model is viable.
 

The reliability theory used in the design of complex systems including electric grids assumes random component failures and is thus unsuited to analyzing security risks due to attackers that intentionally damage several components of the system. In this paper, a security risk analysis methodology is proposed consisting of vulnerability analysis and impact analysis. Vulnerability analysis is a method developed by security engineers to identify the attacks that are relevant for the system under study, and in this paper, the analysis is applied on the communications network topology of the electric grid automation system. Impact analysis is then performed through co-simulation of automation and the electric grid to assess the potential damage from the attacks. This paper makes an extensive review of vulnerability and impact analysis methods and relevant system modeling techniques from the fields of security and industrial automation engineering, with a focus on smart grid automation, and then applies and combines approaches to obtain a security risk analysis methodology. The methodology is demonstrated with a case study of fault location, isolation and supply restoration smart grid automation.

Today's more reliable communication technology, together with the availability of higher computational power, have paved the way for introduction of more advanced automation systems based on distributed intelligence and multi-agent technology. However, abundance of data, while making these systems more powerful, can at the same time act as their biggest vulnerability. In a web of interconnected devices and components functioning within an automation framework, potential impact of malfunction in a single device, either through internal failure or external damage/intrusion, may lead to detrimental side-effects spread across the whole underlying system. The potentially large number of devices, along with their inherent interrelations and interdependencies, may hinder the ability of human operators to interpret events, identify their scope of impact and take remedial actions if necessary. Through utilization of the concepts of graph-theoretic fuzzy cognitive maps (FCM) and expert systems, this paper puts forth a solution that is able to reveal weak links and vulnerabilities of an automation system, should it become exposed to partial internal failure or external damage. A case study has been performed on the IEEE 34-bus test distribution system to show the efficiency of the proposed scheme.

This paper proposes a methodology to assess cyber-related risks and to identify critical assets both at power grid and substation levels. The methodology is based on a two-pass engine model. The first pass engine is developed to identify the most critical substation(s) in a power grid. A mixture of Analytical hierarchy process (AHP) and (N-1) contingent analysis is used to calculate risks. The second pass engine is developed to identify risky assets within a substation and improve the vulnerability of a substation against the intrusion and malicious acts of cyber hackers. The risk methodology uniquely combines asset reliability, vulnerability and costs of attack into a risk index. A methodology is also presented to improve the overall security of a substation by optimally placing security agent(s) on the automation system.

This paper proposes a methodology to assess cyber-related risks and to identify critical assets both at power grid and substation levels. The methodology is based on a two-pass engine model. The first pass engine is developed to identify the most critical substation(s) in a power grid. A mixture of Analytical hierarchy process (AHP) and (N-1) contingent analysis is used to calculate risks. The second pass engine is developed to identify risky assets within a substation and improve the vulnerability of a substation against the intrusion and malicious acts of cyber hackers. The risk methodology uniquely combines asset reliability, vulnerability and costs of attack into a risk index. A methodology is also presented to improve the overall security of a substation by optimally placing security agent(s) on the automation system.

Malicious applications can be introduced to attack users and services so as to gain financial rewards, individuals' sensitive information, company and government intellectual property, and to gain remote control of systems. However, traditional methods of malicious code detection, such as signature detection, behavior detection, virtual machine detection, and heuristic detection, have various weaknesses which make them unreliable. This paper presents the existing technologies of malicious code detection and a malicious code detection model is proposed based on behavior association. The behavior points of malicious code are first extracted through API monitoring technology and integrated into the behavior; then a relation between behaviors is established according to data dependence. Next, a behavior association model is built up and a discrimination method is put forth using pushdown automation. Finally, the exact malicious code is taken as a sample to carry out an experiment on the behavior's capture, association, and discrimination, thus proving that the theoretical model is viable.