Biblio
The power industrial control system is an important part of the national critical Information infrastructure. Its security is related to the national strategic security and has become an important target of cyber attacks. In order to solve the problem that the vulnerability detection technology of power industrial control system cannot meet the requirement of non-destructive, this paper proposes an industrial control vulnerability analysis technology combined with dynamic and static analysis technology. On this basis, an industrial control non-destructive vulnerability detection system is designed, and a simulation verification platform is built to verify the effectiveness of the industrial control non-destructive vulnerability detection system. These provide technical support for the safety protection research of the power industrial control system.
ISSN: 2693-289X
Cyber threats directly affect the critical reliability and availability of modern Industry Control Systems (ICS) in respects of operations and processes. Where there are a variety of vulnerabilities and cyber threats, it is necessary to effectively evaluate cyber security risks, and control uncertainties of cyber environments, and quantitative evaluation can be helpful. To effectively and timely control the spread and impact produced by attacks on ICS networks, a probabilistic Multi-Attribute Vulnerability Criticality Analysis (MAVCA) model for impact estimation and prioritised remediation is presented. This offer a new approach for combining three major attributes: vulnerability severities influenced by environmental factors, the attack probabilities relative to the vulnerabilities, and functional dependencies attributed to vulnerability host components. A miniature ICS testbed evaluation illustrates the usability of the model for determining the weakest link and setting security priority in the ICS. This work can help create speedy and proactive security response. The metrics derived in this work can serve as sub-metrics inputs to a larger quantitative security metrics taxonomy; and can be integrated into the security risk assessment scheme of a larger distributed system.
With wide applications like surveillance and imaging, securing underwater acoustic Mobile Ad-hoc NETworks (MANET) becomes a double-edged sword for oceanographic operations. Underwater acoustic MANET inherits vulnerabilities from 802.11-based MANET which renders traditional cryptographic approaches defenseless. A Trust Management Framework (TMF), allowing maintained confidence among participating nodes with metrics built from their communication activities, promises secure, efficient and reliable access to terrestrial MANETs. TMF cannot be directly applied to the underwater environment due to marine characteristics that make it difficult to differentiate natural turbulence from intentional misbehavior. This work proposes a trust model to defend underwater acoustic MANETs against attacks using a machine learning method with carefully chosen communication metrics, and a cloud model to address the uncertainty of trust in harsh underwater environments. By integrating the trust framework of communication with the cloud model to combat two kinds of uncertainties: fuzziness and randomness, trust management is greatly improved for underwater acoustic MANETs.
Signature-based Intrusion Detection Systems (IDS) are a key component in the cybersecurity defense strategy for any network being monitored. In order to improve the efficiency of the intrusion detection system and the corresponding mitigation action, it is important to address the problem of false alarms. In this paper, we present a comparative analysis of two approaches that consider the false alarm minimization and alarm correlation techniques. The output of this analysis provides us the elements to propose a parallelizable strategy designed to achieve better results in terms of precision, recall and alarm load reduction in the prioritization of alarms. We use Prelude SIEM as the event normalizer in order to process security events from heterogeneous sensors and to correlate them. The alarms are verified using the dynamic network context information collected from the vulnerability analysis, and they are prioritized using the HP Arsight priority formula. The results show an important reduction in the volume of alerts, together with a high precision in the identification of false alarms.
In the present paper, the problem of networked control system (NCS) cyber security is considered. The geometric approach is used to evaluate the security and vulnerability level of the controlled system. The proposed results are about the so-called false data injection attacks and show how imperfectly known disturbances can be used to perform undetectable, or at least stealthy, attacks that can make the NCS vulnerable to attacks from malicious outsiders. A numerical example is given to illustrate the approach.
There are over 1 billion websites today, and most of them are designed using content management systems. Cybersecurity is one of the most discussed topics when it comes to a web application and protecting the confidentiality, integrity of data has become paramount. SQLi is one of the most commonly used techniques that hackers use to exploit a security vulnerability in a web application. In this paper, we compared SQLi vulnerabilities found on the three most commonly used content management systems using a vulnerability scanner called Nikto, then SQLMAP for penetration testing. This was carried on default WordPress, Drupal and Joomla website pages installed on a LAMP server (Iocalhost). Results showed that each of the content management systems was not susceptible to SQLi attacks but gave warnings about other vulnerabilities that could be exploited. Also, we suggested practices that could be implemented to prevent SQL injections.
Reliable operation of power systems is a primary challenge for the system operators. With the advancement in technology and grid automation, power systems are becoming more vulnerable to cyber-attacks. The main goal of adversaries is to take advantage of these vulnerabilities and destabilize the system. This paper describes a game-theoretic approach to attacker / defender modeling in power systems. In our models, the attacker can strategically identify the subset of substations that maximize damage when compromised. However, the defender can identify the critical subset of substations to protect in order to minimize the damage when an attacker launches a cyber-attack. The algorithms for these models are applied to the standard IEEE-14, 39, and 57 bus examples to identify the critical set of substations given an attacker and a defender budget.
Recent years, the issue of cyber security has become ever more prevalent in the analysis and design of electrical cyber-physical systems (ECPSs). In this paper, we present the TrueTime Network Library for modeling the framework of ECPSs and focuses on the vulnerability analysis of ECPSs under DoS attacks. Model predictive control algorithm is used to control the ECPS under disturbance or attacks. The performance of decentralized and distributed control strategies are compared on the simulation platform. It has been proved that DoS attacks happen at dada collecting sensors or control instructions actuators will influence the system differently.