Dynamic cyber-incident response

Permission to make digital or hard copies of this publication for internal use within NATO and for personal or educational use when for non-profi t or non-commercial purposes is granted providing that copies bear this notice and a full citation on the first page. Any other reproduction or transmission requires prior written permission by NATO CCD COE.Traditional cyber-incident response models have not changed significantly since the early days of the Computer Incident Response with even the most recent incident response life cycle model advocated by the US National Institute of Standards and Technology (Cichonski, Millar, Grance, & Scarfone, 2012) bearing a striking resemblance to the models proposed by early leaders in the field e.g. Carnegie-Mellon University (West-Brown, et al., 2003) and the SANS Institute (Northcutt, 2003). Whilst serving the purpose of producing coherent and effective response plans, these models appear to be created from the perspectives of Computer Security professionals with no referenced academic grounding. They attempt to defend against, halt and recover from a cyber-attack as quickly as possible. However, other actors inside an organisation may have priorities which conflict with these traditional approaches and may ultimately better serve the longer-term goals and objectives of an organisation

Cyber situational awareness: from geographical alerts to high-level management

This paper focuses on cyber situational awareness and describes a visual analytics solution for monitoring and putting in tight relation data from network level with the organization business. The goal of the proposed solution is to make different security profiles (network security officer, network security manager, and financial security manager) aware of the actual network state (e.g., risk and attack progress) and the impact it actually has on the business tasks, making clear the relationships that exist between the network level and the business level. The proposed solution is instantiated on the ACEA infrastructure, the Italian company that provides power and water purification services to cities in central Italy (millions of end users

Sonification of Network Traffic Flow for Monitoring and Situational Awareness

Maintaining situational awareness of what is happening within a network is challenging, not least because the behaviour happens within computers and communications networks, but also because data traffic speeds and volumes are beyond human ability to process. Visualisation is widely used to present information about the dynamics of network traffic dynamics. Although it provides operators with an overall view and specific information about particular traffic or attacks on the network, it often fails to represent the events in an understandable way. Visualisations require visual attention and so are not well suited to continuous monitoring scenarios in which network administrators must carry out other tasks. Situational awareness is critical and essential for decision-making in the domain of computer network monitoring where it is vital to be able to identify and recognize network environment behaviours.Here we present SoNSTAR (Sonification of Networks for SiTuational AwaReness), a real-time sonification system to be used in the monitoring of computer networks to support the situational awareness of network administrators. SoNSTAR provides an auditory representation of all the TCP/IP protocol traffic within a network based on the different traffic flows between between network hosts. SoNSTAR raises situational awareness levels for computer network defence by allowing operators to achieve better understanding and performance while imposing less workload compared to visual techniques. SoNSTAR identifies the features of network traffic flows by inspecting the status flags of TCP/IP packet headers and mapping traffic events to recorded sounds to generate a soundscape representing the real-time status of the network traffic environment. Listening to the soundscape allows the administrator to recognise anomalous behaviour quickly and without having to continuously watch a computer screen.Comment: 17 pages, 7 figures plus supplemental material in Github repositor

A taxonomy of malicious traffic for intrusion detection systems

With the increasing number of network threats it is essential to have a knowledge of existing and new network threats to design better intrusion detection systems. In this paper we propose a taxonomy for classifying network attacks in a consistent way, allowing security researchers to focus their efforts on creating accurate intrusion detection systems and targeted datasets