9,390 research outputs found
Assessing and augmenting SCADA cyber security: a survey of techniques
SCADA systems monitor and control critical infrastructures of national importance such as power generation and distribution, water supply, transportation networks, and manufacturing facilities. The pervasiveness, miniaturisations and declining costs of internet connectivity have transformed these systems from strictly isolated to highly interconnected networks. The connectivity provides immense benefits such as reliability, scalability and remote connectivity, but at the same time exposes an otherwise isolated and secure system, to global cyber security threats. This inevitable transformation to highly connected systems thus necessitates effective security safeguards to be in place as any compromise or downtime of SCADA systems can have severe economic, safety and security ramifications. One way to ensure vital asset protection is to adopt a viewpoint similar to an attacker to determine weaknesses and loopholes in defences. Such mind sets help to identify and fix potential breaches before their exploitation. This paper surveys tools and techniques to uncover SCADA system vulnerabilities. A comprehensive review of the selected approaches is provided along with their applicability
Technical Report on Deploying a highly secured OpenStack Cloud Infrastructure using BradStack as a Case Study
Cloud computing has emerged as a popular paradigm and an attractive model for
providing a reliable distributed computing model.it is increasing attracting
huge attention both in academic research and industrial initiatives. Cloud
deployments are paramount for institution and organizations of all scales. The
availability of a flexible, free open source cloud platform designed with no
propriety software and the ability of its integration with legacy systems and
third-party applications are fundamental. Open stack is a free and opensource
software released under the terms of Apache license with a fragmented and
distributed architecture making it highly flexible. This project was initiated
and aimed at designing a secured cloud infrastructure called BradStack, which
is built on OpenStack in the Computing Laboratory at the University of
Bradford. In this report, we present and discuss the steps required in
deploying a secured BradStack Multi-node cloud infrastructure and conducting
Penetration testing on OpenStack Services to validate the effectiveness of the
security controls on the BradStack platform. This report serves as a practical
guideline, focusing on security and practical infrastructure related issues. It
also serves as a reference for institutions looking at the possibilities of
implementing a secured cloud solution.Comment: 38 pages, 19 figures
Applications of Machine Learning to Threat Intelligence, Intrusion Detection and Malware
Artificial Intelligence (AI) and Machine Learning (ML) are emerging technologies with applications to many fields. This paper is a survey of use cases of ML for threat intelligence, intrusion detection, and malware analysis and detection. Threat intelligence, especially attack attribution, can benefit from the use of ML classification. False positives from rule-based intrusion detection systems can be reduced with the use of ML models. Malware analysis and classification can be made easier by developing ML frameworks to distill similarities between the malicious programs. Adversarial machine learning will also be discussed, because while ML can be used to solve problems or reduce analyst workload, it also introduces new attack surfaces
External servers security
Romero Barrero, D. (2010). External servers security. http://hdl.handle.net/10251/9111.Archivo delegad
BlackWatch:increasing attack awareness within web applications
Web applications are relied upon by many for the services they provide. It is essential that applications implement appropriate security measures to prevent security incidents. Currently, web applications focus resources towards the preventative side of security. Whilst prevention is an essential part of the security process, developers must also implement a level of attack awareness into their web applications. Being able to detect when an attack is occurring provides applications with the ability to execute responses against malicious users in an attempt to slow down or deter their attacks. This research seeks to improve web application security by identifying malicious behaviour from within the context of web applications using our tool BlackWatch. The tool is a Python-based application which analyses suspicious events occurring within client web applications, with the objective of identifying malicious patterns of behaviour. This approach avoids issues typically encountered with traditional web application firewalls. Based on the results from a preliminary study, BlackWatch was effective at detecting attacks from both authenticated, and unauthenticated users. Furthermore, user tests with developers indicated BlackWatch was user friendly, and was easy to integrate into existing applications. Future work seeks to develop the BlackWatch solution further for public release
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