A Characterization of Cybersecurity Posture from Network Telescope Data

Data-driven understanding of cybersecurity posture is an important problem that has not been adequately explored. In this paper, we analyze some real data collected by CAIDA's network telescope during the month of March 2013. We propose to formalize the concept of cybersecurity posture from the perspectives of three kinds of time series: the number of victims (i.e., telescope IP addresses that are attacked), the number of attackers that are observed by the telescope, and the number of attacks that are observed by the telescope. Characterizing cybersecurity posture therefore becomes investigating the phenomena and statistical properties exhibited by these time series, and explaining their cybersecurity meanings. For example, we propose the concept of {\em sweep-time}, and show that sweep-time should be modeled by stochastic process, rather than random variable. We report that the number of attackers (and attacks) from a certain country dominates the total number of attackers (and attacks) that are observed by the telescope. We also show that substantially smaller network telescopes might not be as useful as a large telescope

Assessing Internet-wide Cyber Situational Awareness of Critical Sectors

In this short paper, we take a first step towards empirically assessing Internet-wide malicious activities generated from and targeted towards Internet-scale business sectors (i.e., financial, health, education, etc.) and critical infrastructure (i.e., utilities, manufacturing, government, etc.). Facilitated by an innovative and a collaborative large-scale effort, we have conducted discussions with numerous Internet entities to obtain rare and private information related to allocated IP blocks pertaining to the aforementioned sectors and critical infrastructure. To this end, we employ such information to attribute Internet-scale maliciousness to such sectors and realms, in an attempt to provide an in-depth analysis of the global cyber situational posture. We draw upon close to 16.8 TB of darknet data to infer probing activities (typically generated by malicious/infected hosts) and DDoS backscatter, from which we distill IP addresses of victims. By executing week-long measurements, we observed an alarming number of more than 11,000 probing machines and 300 DDoS attack victims hosted by critical sectors. We also generate rare insights related to the maliciousness of various business sectors, including financial, which typically do not report their hosted and targeted illicit activities for reputation-preservation purposes. While we treat the obtained results with strict confidence due to obvious sensitivity reasons, we postulate that such generated cyber threat intelligence could be shared with sector/critical infrastructure operators, backbone networks and Internet service providers to contribute to the overall threat remediation objective

Enlightening the Darknets: Augmenting Darknet Visibility with Active Probes

Darknets collect unsolicited traffic reaching unused address spaces. They provide insights into malicious activities, such as the rise of botnets and DDoS attacks. However, darknets provide a shallow view, as traffic is never responded. Here we quantify how their visibility increases by responding to traffic with interactive responders with increasing levels of interaction. We consider four deployments: Darknets, simple, vertical bound to specific ports, and, a honeypot that responds to all protocols on any port. We contrast these alternatives by analyzing the traffic attracted by each deployment and characterizing how traffic changes throughout the responder lifecycle on the darknet. We show that the deployment of responders increases the value of darknet data by revealing patterns that would otherwise be unobservable. We measure Side-Scan phenomena where once a host starts responding, it attracts traffic to other ports and neighboring addresses. uncovers attacks that darknets and would not observe, e.g. large-scale activity on non-standard ports. And we observe how quickly senders can identify and attack new responders. The “enlightened” part of a darknet brings several benefits and offers opportunities to increase the visibility of sender patterns. This information gain is worth taking advantage of, and we, therefore, recommend that organizations consider this option

Guide to Developing Cybersecurity Programs for NSF Science and Engineering Projects, v1

This document is a product of the Center for Trustworthy Scientific Cyberinfrastructure (CTSC). CTSC is supported by the National Science Foundation under Grant Number OCI-1234408. Any opinions,findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation

Abstracts 2018: Highlights of Student Research and Creative Endeavors

What follows is a collection of abstracts summarizing the scholarship conducted by undergraduates at Columbus State University during the 2017-2018 academic year. These projects highlight undergraduates research in a variety of disciplines, ranging from literary analysis to laboratory-based sciences. The abstracts represent many ongoing projects on our campus and catalog those that have been published or presented. This volume begins with projects that have been selected for presentations at national, regional and statewide disciplinary conferences. Among them are several that have garnered awards for outstanding undergraduate scholarship. Projects that have received competitive research grants, including our campus Student Research and Creative Endeavor (SRACE) Grants, are also featured. Many undergraduates have presented their work to our local community, either through the dissemination of best practices in nursing to regional hospitals, colloquium presentations of lecture-recitals at the RiverCenter for the Performing Arts, or at Columbus State University\u27s Tower Day held in April 2018. Together these abstracts demonstrate the commitment of our faculty to engage students in their disciplines and represent outstanding mentorship that occurs on and off our campus throughout the year. Our students have amassed an impressive collection of projects that contributes to both academia and our local community, and these abstracts will hopefully inspire others to delve into scientific and creative inquiry.https://csuepress.columbusstate.edu/abstracts/1012/thumbnail.jp

Automating Cyber Analytics

Model based security metrics are a growing area of cyber security research concerned with measuring the risk exposure of an information system. These metrics are typically studied in isolation, with the formulation of the test itself being the primary finding in publications. As a result, there is a flood of metric specifications available in the literature but a corresponding dearth of analyses verifying results for a given metric calculation under different conditions or comparing the efficacy of one measurement technique over another. The motivation of this thesis is to create a systematic methodology for model based security metric development, analysis, integration, and validation. In doing so we hope to fill a critical gap in the way we view and improve a system’s security. In order to understand the security posture of a system before it is rolled out and as it evolves, we present in this dissertation an end to end solution for the automated measurement of security metrics needed to identify risk early and accurately. To our knowledge this is a novel capability in design time security analysis which provides the foundation for ongoing research into predictive cyber security analytics. Modern development environments contain a wealth of information in infrastructure-as-code repositories, continuous build systems, and container descriptions that could inform security models, but risk evaluation based on these sources is ad-hoc at best, and often simply left until deployment. Our goal in this work is to lay the groundwork for security measurement to be a practical part of the system design, development, and integration lifecycle. In this thesis we provide a framework for the systematic validation of the existing security metrics body of knowledge. In doing so we endeavour not only to survey the current state of the art, but to create a common platform for future research in the area to be conducted. We then demonstrate the utility of our framework through the evaluation of leading security metrics against a reference set of system models we have created. We investigate how to calibrate security metrics for different use cases and establish a new methodology for security metric benchmarking. We further explore the research avenues unlocked by automation through our concept of an API driven S-MaaS (Security Metrics-as-a-Service) offering. We review our design considerations in packaging security metrics for programmatic access, and discuss how various client access-patterns are anticipated in our implementation strategy. Using existing metric processing pipelines as reference, we show how the simple, modular interfaces in S-MaaS support dynamic composition and orchestration. Next we review aspects of our framework which can benefit from optimization and further automation through machine learning. First we create a dataset of network models labeled with the corresponding security metrics. By training classifiers to predict security values based only on network inputs, we can avoid the computationally expensive attack graph generation steps. We use our findings from this simple experiment to motivate our current lines of research into supervised and unsupervised techniques such as network embeddings, interaction rule synthesis, and reinforcement learning environments. Finally, we examine the results of our case studies. We summarize our security analysis of a large scale network migration, and list the friction points along the way which are remediated by this work. We relate how our research for a large-scale performance benchmarking project has influenced our vision for the future of security metrics collection and analysis through dev-ops automation. We then describe how we applied our framework to measure the incremental security impact of running a distributed stream processing system inside a hardware trusted execution environment