Biblio

Online information security labs intended for training and facilitating hands-on learning for distance students at master’s level are not easy to develop and administer. This research focuses on analyzing the results of a DSR project for design, development, and implementation of an InfoSec lab. This research work contributes to the existing research by putting forth an initial outline of a generalized model for design theory for InfoSec labs aimed at hands-on education of students in the field of information security. The anatomy of design theory framework is used to analyze the necessary components of the anticipated design theory for InfoSec labs in future.

We present a scalable optimization algorithm and its parallel implementation for two-stage stochastic programming problems of large-scale, particularly the security constrained optimal power flow models routinely used in electrical power grid operations. Such problems can be prohibitively expensive to solve on industrial scale with the traditional methods or in serial. The algorithm decomposes the problem into first-stage and second-stage optimization subproblems which are then scheduled asynchronously for efficient evaluation in parallel. Asynchronous evaluations are crucial in achieving good balancing and parallel efficiency because the second-stage optimization subproblems have highly varying execution times. The algorithm employs simple local second-order approximations of the second-stage optimal value functions together with exact first- and second-order derivatives for the first-stage subproblems to accelerate convergence. To reduce the number of the evaluations of computationally expensive second-stage subproblems required by line search, we devised a flexible mechanism for controlling the step size that can be tuned to improve performance for individual class of problems. The algorithm is implemented in C++ using MPI non-blocking calls to overlap computations with communication and boost parallel efficiency. Numerical experiments of the algorithm are conducted on Summit and Lassen supercomputers at Oak Ridge and Lawrence Livermore National Laboratories and scaling results show good parallel efficiency.

With the increasing use of information and communication technology in electrical power grids, the security of energy supply is increasingly threatened by cyber-attacks. Traditional cyber-security measures, such as firewalls or intrusion detection/prevention systems, can be used as mitigation and prevention measures, but their effective use requires a deep understanding of the potential threat landscape and complex attack processes in energy information systems. Given the complexity and lack of detailed knowledge of coordinated, timed attacks in smart grid applications, we need information and insight into realistic attack scenarios in an appropriate and practical setting. In this paper, we present a man-in-the-middle-based attack scenario that intercepts process communication between control systems and field devices, employs false data injection techniques, and performs data corruption such as sending false commands to field devices. We demonstrate the applicability of the presented attack scenario in a physical smart grid laboratory environment and analyze the generated data under normal and attack conditions to extract domain-specific knowledge for detection mechanisms.

This is a full paper for innovate practice. Building a private cloud or using a public cloud is now feasible at many institutions. This paper presents the innovative design of cloudbased labs and programming assignments for a networking course and a cybersecurity course, and our experiences of innovatively using the private cloud at our institution to support these learning activities. It is shown by the instructor's observations and student survey data that our approach benefits learning and teaching. This approach makes it possible and secure to develop some learning activities that otherwise would not be allowed on physical servers. It enables the instructor to support students' desire of developing programs in their preferred programming languages. It allows students to debug and test their programs on the same platform to be used by the instructor for testing and grading. The instructor does not need to spend extra time administrating the computing environments. A majority (88% or more) of the students agree that working on those learning activities in the private cloud not only helps them achieve the course learning objectives, but also prepares them for their future careers.

Information security is a top priority in government and industry because high consequence cyber incidents continue with regularity. The blue teamers that protect cyber systems cannot stop or even know about all these incidents, so they must take measures to tolerate these incursions in addition to preventing and detecting them. We propose dynamically compartmentalizing subject networks into collaboration zones and limiting the communication between these zones. In this article, we demonstrate this technique's effect on the attacker and the defender for various parameter settings using discrete-time simulation. Based on our results, we conclude that dynamic cyber zone defense is a viable intrusion tolerance technique and should be considered for technology transfer.

Conversational agents assist traditional teaching-learning instruments in proposing new designs for knowledge creation and learning analysis, across organizational environments. Means of building common educative background in both industry and academic fields become of interest for ensuring educational effectiveness and consistency. Such a context requires transferable practices and becomes the basis for the Agile adoption into Higher Education, at both curriculum and operational levels. The current work proposes a model for delivering Agile Scrum training through an assistive web-based conversational service, where analytics are collected to provide an overview on learners' knowledge path. Besides its specific applicability into Software Engineering (SE) industry, the model is to assist the academic SE curriculum. A user-acceptance test has been carried out among 200 undergraduate students and patterns of interaction have been depicted for 2 conversational strategies.

One of the challenges in supplying the communities with wider access to scientific databases is the need for knowledge of database languages like Structured Query Language (SQL). Although the SQL language has been published in many forms, not everybody is able to write SQL queries. Another challenge is that it might not be practical to make the public aware of the structure of databases. There is a need for novice users to query relational databases using their natural language. To solve this problem, many natural language interfaces to structured databases have been developed. The goal is to provide a more intuitive method for generating database queries and delivering responses. Through social media, which makes it possible to interact with a wide section of the population, and with the help of natural language processing, researchers at the Atmospheric Radiation Measurement (ARM) Data Center at Oak Ridge National Laboratory (ORNL) have developed a concept to enable easy search and retrieval of data from several environmental data centers for the scientific community through social media.Using a machine learning framework that maps natural language text to thousands of datasets, instruments, variables, and data streams, the prototype system would allow users to request data through Twitter and receive a link (via tweet) to applicable data results on the project's search catalog tailored to their key words. This automated identification of relevant data from various petascale archives at ORNL could increase convenience, access, and use of the project's data by the broader community. In this paper we discuss how some data-intensive projects at ORNL are using innovative ways to help in data discovery.

By accurately measuring risk for enterprise networks, attack graphs allow network defenders to understand the most critical threats and select the most effective countermeasures. This paper describes substantial enhancements to the NetSPA attack graph system required to model additional present-day threats (zero-day exploits and client-side attacks) and countermeasures (intrusion prevention systems, proxy firewalls, personal firewalls, and host-based vulnerability scans). Point-to-point reachability algorithms and structures were extensively redesigned to support "reverse" reachability computations and personal firewalls. Host-based vulnerability scans are imported and analyzed. Analysis of an operational network with 84 hosts demonstrates that client-side attacks pose a serious threat. Experiments on larger simulated networks demonstrated that NetSPA's previous excellent scaling is maintained. Less than two minutes are required to completely analyze a four-enclave simulated network with more than 40,000 hosts protected by personal firewalls.

Over the past decade, the reliance on Unmanned Aerial Systems (UAS) to carry out critical missions has grown drastically. With an increased reliance on UAS as mission assets and the dependency of UAS on cyber resources, cyber security of UAS must be improved by adopting sound security principles and relevant technologies from the computing community. On the other hand, the traditional avionics community, being aware of the importance of cyber security, is looking at new architecture and designs that can accommodate both the traditional safety oriented principles as well as the cyber security principles and techniques. It is with the effective and timely convergence of these domains that a holistic approach and co-design can meet the unique requirements of modern systems and operations. In this paper, authors from both the cyber security and avionics domains describe our joint effort and insights obtained during the course of designing secure and resilient embedded avionics systems.

Verifying that hardware design implementations adhere to specifications is a time intensive and sometimes intractable problem due to the massive size of the system's state space. Formal methods techniques can be used to prove certain tractable specification properties; however, they are expensive, and often require subject matter experts to develop and solve. Nonetheless, hardware verification is a critical process to ensure security and safety properties are met, and encapsulates problems associated with trust and reliability. For complex designs where coverage of the entire state space is unattainable, prioritizing regions most vulnerable to security or reliability threats would allow efficient allocation of valuable verification resources. Stackelberg security games model interactions between a defender, whose goal is to assign resources to protect a set of targets, and an attacker, who aims to inflict maximum damage on the targets after first observing the defender's strategy. In equilibrium, the defender has an optimal security deployment strategy, given the attacker's best response. We apply this Stackelberg security framework to synthesized hardware implementations using the design's network structure and logic to inform defender valuations and verification costs. The defender's strategy in equilibrium is thus interpreted as a prioritization of the allocation of verification resources in the presence of an adversary. We demonstrate this technique on several open-source synthesized hardware designs.

Summary form only given. In this presentation, several issues regarding operating system security will be investigated. The general problems of OS security are to be addressed. We also discuss why we should consider the security aspects of the OS, and when a secure OS is needed. We delve into the topic of secure OS design as well focusing on covert channel analysis. The specific operating systems under consideration include Windows and Android.
 

App vetting is the process of approving or rejecting an app prior to deployment on a mobile device. • The decision to approve or reject an app is based on the organization's security requirements and the type and severity of security vulnerabilities found in the app. • Security vulnerabilities including Cross Site Scripting (XSS), information leakage, authentication and authorization, session management, and SQL injection can be exploited to steal information or control a device.
 

We consider the problem of designing (or augmenting) an electric power system at a minimum cost such that it satisfies the N-k-ε survivability criterion. This survivability criterion is a generalization of the well-known N-k criterion, and it requires that at least (1-εj) fraction of the steady-state demand be met after failures of j components, for j=0,1,...,k. The network design problem adds another level of complexity to the notoriously hard contingency analysis problem, since the contingency analysis is only one of the requirements for the design optimization problem. We present a mixed-integer programming formulation of this problem that takes into account both transmission and generation expansion. We propose an algorithm that can avoid combinatorial explosion in the number of contingencies, by seeking vulnerabilities in intermediary solutions and constraining the design space accordingly. Our approach is built on our ability to identify such system vulnerabilities quickly. Our empirical studies on modified instances of the IEEE 30-bus and IEEE 57-bus systems show the effectiveness of our methods. We were able to solve the transmission and generation expansion problems for k=4 in approximately 30 min, while other approaches failed to provide a solution at the end of 2 h.

In network intrusion detection research, one popular strategy for finding attacks is monitoring a network's activity for anomalies: deviations from profiles of normality previously learned from benign traffic, typically identified using tools borrowed from the machine learning community. However, despite extensive academic research one finds a striking gap in terms of actual deployments of such systems: compared with other intrusion detection approaches, machine learning is rarely employed in operational "real world" settings. We examine the differences between the network intrusion detection problem and other areas where machine learning regularly finds much more success. Our main claim is that the task of finding attacks is fundamentally different from these other applications, making it significantly harder for the intrusion detection community to employ machine learning effectively. We support this claim by identifying challenges particular to network intrusion detection, and provide a set of guidelines meant to strengthen future research on anomaly detection.