10,092 research outputs found
The Effect of Security Education and Expertise on Security Assessments: the Case of Software Vulnerabilities
In spite of the growing importance of software security and the industry
demand for more cyber security expertise in the workforce, the effect of
security education and experience on the ability to assess complex software
security problems has only been recently investigated. As proxy for the full
range of software security skills, we considered the problem of assessing the
severity of software vulnerabilities by means of a structured analysis
methodology widely used in industry (i.e. the Common Vulnerability Scoring
System (\CVSS) v3), and designed a study to compare how accurately individuals
with background in information technology but different professional experience
and education in cyber security are able to assess the severity of software
vulnerabilities. Our results provide some structural insights into the complex
relationship between education or experience of assessors and the quality of
their assessments. In particular we find that individual characteristics matter
more than professional experience or formal education; apparently it is the
\emph{combination} of skills that one owns (including the actual knowledge of
the system under study), rather than the specialization or the years of
experience, to influence more the assessment quality. Similarly, we find that
the overall advantage given by professional expertise significantly depends on
the composition of the individual security skills as well as on the available
information.Comment: Presented at the Workshop on the Economics of Information Security
(WEIS 2018), Innsbruck, Austria, June 201
Artificial intelligence in the cyber domain: Offense and defense
Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41
Trusted CI Experiences in Cybersecurity and Service to Open Science
This article describes experiences and lessons learned from the Trusted CI
project, funded by the US National Science Foundation to serve the community as
the NSF Cybersecurity Center of Excellence. Trusted CI is an effort to address
cybersecurity for the open science community through a single organization that
provides leadership, training, consulting, and knowledge to that community. The
article describes the experiences and lessons learned of Trusted CI regarding
both cybersecurity for open science and managing the process of providing
centralized services to a broad and diverse community.Comment: 8 pages, PEARC '19: Practice and Experience in Advanced Research
Computing, July 28-August 1, 2019, Chicago, IL, US
Classification hardness for supervised learners on 20 years of intrusion detection data
This article consolidates analysis of established (NSL-KDD) and new intrusion detection datasets (ISCXIDS2012, CICIDS2017, CICIDS2018) through the use of supervised machine learning (ML) algorithms. The uniformity in analysis procedure opens up the option to compare the obtained results. It also provides a stronger foundation for the conclusions about the efficacy of supervised learners on the main classification task in network security. This research is motivated in part to address the lack of adoption of these modern datasets. Starting with a broad scope that includes classification by algorithms from different families on both established and new datasets has been done to expand the existing foundation and reveal the most opportune avenues for further inquiry. After obtaining baseline results, the classification task was increased in difficulty, by reducing the available data to learn from, both horizontally and vertically. The data reduction has been included as a stress-test to verify if the very high baseline results hold up under increasingly harsh constraints. Ultimately, this work contains the most comprehensive set of results on the topic of intrusion detection through supervised machine learning. Researchers working on algorithmic improvements can compare their results to this collection, knowing that all results reported here were gathered through a uniform framework. This work's main contributions are the outstanding classification results on the current state of the art datasets for intrusion detection and the conclusion that these methods show remarkable resilience in classification performance even when aggressively reducing the amount of data to learn from
SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach
This paper presents the development of a Supervisory Control and Data
Acquisition (SCADA) system testbed used for cybersecurity research. The testbed
consists of a water storage tank's control system, which is a stage in the
process of water treatment and distribution. Sophisticated cyber-attacks were
conducted against the testbed. During the attacks, the network traffic was
captured, and features were extracted from the traffic to build a dataset for
training and testing different machine learning algorithms. Five traditional
machine learning algorithms were trained to detect the attacks: Random Forest,
Decision Tree, Logistic Regression, Naive Bayes and KNN. Then, the trained
machine learning models were built and deployed in the network, where new tests
were made using online network traffic. The performance obtained during the
training and testing of the machine learning models was compared to the
performance obtained during the online deployment of these models in the
network. The results show the efficiency of the machine learning models in
detecting the attacks in real time. The testbed provides a good understanding
of the effects and consequences of attacks on real SCADA environmentsComment: E-Preprin
Recommended from our members
The Federal Cybersecurity Workforce: Background and Congressional Oversight Issues for the Departments of Defense and Homeland Security
[Excerpt] This report examines congressional oversight of two strategies undertaken by Congress and the executive branch to strengthen the federal cybersecurity workforce: (1) initiatives to define and identify the federal cybersecurity workforce, and (2) hiring and pay flexibilities applicable to cybersecurity positions at DOD and DHS. This report focuses on DOD and DHS because of their key roles in federal cybersecurity and because the majority of hiring and pay flexibilities for cybersecurity professionals authorized by Congress apply to DOD and DHS
- …