scholarly journals Measuring Relative Attack Surfaces

2005 ◽  
pp. 109-137 ◽  
Author(s):  
Michael Howard ◽  
Jon Pincus ◽  
Jeannette M. Wing
Keyword(s):  
Attack Surfaces

scholarly journals Utilising Flow Aggregation to Classify Benign Imitating Attacks

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1761
Author(s):  
Hanan Hindy ◽  
Robert Atkinson ◽  
Christos Tachtatzis ◽  
Ethan Bayne ◽  
Miroslav Bures ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Network Traffic ◽  
Cyber Attacks ◽  
Detection Accuracy ◽  
Computational Power ◽  
Human Understanding ◽  
Flow Aggregation ◽  
Additional Level ◽  
Attack Surfaces

Cyber-attacks continue to grow, both in terms of volume and sophistication. This is aided by an increase in available computational power, expanding attack surfaces, and advancements in the human understanding of how to make attacks undetectable. Unsurprisingly, machine learning is utilised to defend against these attacks. In many applications, the choice of features is more important than the choice of model. A range of studies have, with varying degrees of success, attempted to discriminate between benign traffic and well-known cyber-attacks. The features used in these studies are broadly similar and have demonstrated their effectiveness in situations where cyber-attacks do not imitate benign behaviour. To overcome this barrier, in this manuscript, we introduce new features based on a higher level of abstraction of network traffic. Specifically, we perform flow aggregation by grouping flows with similarities. This additional level of feature abstraction benefits from cumulative information, thus qualifying the models to classify cyber-attacks that mimic benign traffic. The performance of the new features is evaluated using the benchmark CICIDS2017 dataset, and the results demonstrate their validity and effectiveness. This novel proposal will improve the detection accuracy of cyber-attacks and also build towards a new direction of feature extraction for complex ones.

Quantifying & minimizing attack surfaces containing moving target defenses

2015 ◽  
Author(s):  
Nathaniel Soule ◽  
Borislava Simidchieva ◽  
Fusun Yaman ◽  
Ronald Watro ◽  
Joseph Loyall ◽  
...  
Keyword(s):  
Moving Target ◽  
Attack Surfaces

Cybersecurity capabilities for critical infrastructure resilience

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Masike Malatji ◽  
Annlizé L. Marnewick ◽  
Suné Von Solms
Keyword(s):  
Cloud Computing ◽  
Best Practice ◽  
Critical Infrastructure ◽  
Integrated Approach ◽  
Cyber Attacks ◽  
Industrial Control Systems ◽  
Content Type ◽  
Goods And Services ◽  
Standards And Guidelines ◽  
Attack Surfaces

Purpose For many innovative organisations, Industry 4.0 paves the way for significant operational efficiencies, quality of goods and services and cost reductions. One of the ways to realise these benefits is to embark on digital transformation initiatives that may be summed up as the intelligent interconnectivity of people, processes, data and cyber-connected things. Sadly, this interconnectivity between the enterprise information technology (IT) and industrial control systems (ICS) environment introduces new attack surfaces for critical infrastructure (CI) operators. As a result of the ICS cybersecurity risk introduced by the interconnectivity between the enterprise IT and ICS networks, the purpose of this study is to identify the cybersecurity capabilities that CI operators must have to attain good cybersecurity resilience. Design/methodology/approach A scoping literature review of best practice international CI protection frameworks, standards and guidelines were conducted. Similar cybersecurity practices from these frameworks, standards and guidelines were grouped together under a corresponding National Institute of Standards and Technology (NIST) cybersecurity framework (CF) practice. Practices that could not be categorised under any of the existing NIST CF practices were considered new insights, and therefore, additions. Findings A CI cybersecurity capability framework comprising 29 capability domains (cybersecurity focus areas) was developed as an adaptation of the NIST CF with an added dimension. This added dimension emphasises cloud computing and internet of things (IoT) security. Each of the 29 cybersecurity capability domains is executed through various capabilities (cybersecurity processes and procedures). The study found that each cybersecurity capability can further be operationalised by a set of cybersecurity controls derived from various frameworks, standards and guidelines, such as COBIT®, CIS®, ISA/IEC 62443, ISO/IEC 27002 and NIST Special Publication 800-53. Practical implications CI sectors are immediately able to adopt the CI cybersecurity capability framework to evaluate their levels of resilience against cyber-attacks, given new attack surfaces introduced by the interconnectivity of cyber-connected things between the enterprise and ICS levels. Originality/value The authors present an added dimension to the NIST framework for CI cyber protection. In addition to emphasising cryptography, IoT and cloud computing security aspects, this added dimension highlights the need for an integrated approach to CI cybersecurity resilience instead of a piecemeal approach.

scholarly journals Identifying Attack Surfaces in the Evolving Space Industry Using Reference Architectures

2020 ◽  
Author(s):  
Matthew Bradbury ◽  
Carsten Maple ◽  
Hu Yuan ◽  
Ugur Ilker Atmaca ◽  
Sara Cannizzaro
Keyword(s):  
Space Industry ◽  
Attack Surfaces

scholarly journals A Generalized Threat Model for Visual Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3629
Author(s):  
Jennifer Simonjan ◽  
Sebastian Taurer ◽  
Bernhard Dieber
Keyword(s):  
Sensor Networks ◽  
Global Network ◽  
Large Network ◽  
Distributed Network ◽  
Visual Sensor ◽  
Visual Sensor Network ◽  
Visual Sensor Networks ◽  
Threat Model ◽  
Network Applications ◽  
Attack Surfaces

Today, visual sensor networks (VSNs) are pervasively used in smart environments such as intelligent homes, industrial automation or surveillance. A major concern in the use of sensor networks in general is their reliability in the presence of security threats and cyberattacks. Compared to traditional networks, sensor networks typically face numerous additional vulnerabilities due to the dynamic and distributed network topology, the resource constrained nodes, the potentially large network scale and the lack of global network knowledge. These vulnerabilities allow attackers to launch more severe and complicated attacks. Since the state-of-the-art is lacking studies on vulnerabilities in VSNs, a thorough investigation of attacks that can be launched against VSNs is required. This paper presents a general threat model for the attack surfaces of visual sensor network applications and their components. The outlined threats are classified by the STRIDE taxonomy and their weaknesses are classified using CWE, a common taxonomy for security weaknesses.

Designing for Attack Surfaces: Keep Your Friends Close, but Your Enemies Closer

2015 ◽  
pp. 55-74 ◽  
Author(s):  
Trent Jaeger ◽  
Xinyang Ge ◽  
Divya Muthukumaran ◽  
Sandra Rueda ◽  
Joshua Schiffman ◽  
...  
Keyword(s):  
Attack Surfaces

scholarly journals Attacks Generation by Detecting Attack Surfaces

2014 ◽  
Vol 32 ◽  
pp. 529-536 ◽  
Author(s):  
Samir Ouchani ◽  
Gabriele Lenzini
Keyword(s):  
Attack Surfaces

scholarly journals Cyberattack detection in vehicles using characteristic functions, artificial neural networks, and visual analysis

2021 ◽  
Vol 20 (4) ◽  
pp. 845-868
Author(s):  
Yannick Chevalier ◽  
Florian Fenzl ◽  
Maxim Kolomeets ◽  
Roland Rieke ◽  
Andrey Chechulin ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Autonomous Vehicles ◽  
Visual Analysis ◽  
Characteristic Functions ◽  
Vehicle Data ◽  
Artificial Neural ◽  
Attack Surfaces ◽  
Vehicle Network

The connectivity of autonomous vehicles induces new attack surfaces and thusthe demand for sophisticated cybersecurity management. Thus, it is important to ensure thatin-vehicle network monitoring includes the ability to accurately detect intrusive behavior andanalyze cyberattacks from vehicle data and vehicle logs in a privacy-friendly manner. For thispurpose, we describe and evaluate a method that utilizes characteristic functions and compareit with an approach based on artificial neural networks. Visual analysis of the respective eventstreams complements the evaluation. Although the characteristic functions method is an order ofmagnitude faster, the accuracy of the results obtained is at least comparable to those obtainedwith the artificial neural network. Thus, this method is an interesting option for implementation inin-vehicle embedded systems. An important aspect for the usage of the analysis methods within acybersecurity framework is the explainability of the detection results.

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