Air Force Institute of Technology Research Report 2013

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems Engineering and Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

A Design Approach to IoT Endpoint Security for Production Machinery Monitoring

The Internet of Things (IoT) has significant potential in upgrading legacy production machinery with monitoring capabilities to unlock new capabilities and bring economic benefits. However, the introduction of IoT at the shop floor layer exposes it to additional security risks with potentially significant adverse operational impact. This article addresses such fundamental new risks at their root by introducing a novel endpoint security-by-design approach. The approach is implemented on a widely applicable production-machinery-monitoring application by introducing real-time adaptation features for IoT device security through subsystem isolation and a dedicated lightweight authentication protocol. This paper establishes a novel viewpoint for the understanding of IoT endpoint security risks and relevant mitigation strategies and opens a new space of risk-averse designs that enable IoT benefits, while shielding operational integrity in industrial environments

Advanced Topics in Systems Safety and Security

This book presents valuable research results in the challenging field of systems (cyber)security. It is a reprint of the Information (MDPI, Basel) - Special Issue (SI) on Advanced Topics in Systems Safety and Security. The competitive review process of MDPI journals guarantees the quality of the presented concepts and results. The SI comprises high-quality papers focused on cutting-edge research topics in cybersecurity of computer networks and industrial control systems. The contributions presented in this book are mainly the extended versions of selected papers presented at the 7th and the 8th editions of the International Workshop on Systems Safety and Security—IWSSS. These two editions took place in Romania in 2019 and respectively in 2020. In addition to the selected papers from IWSSS, the special issue includes other valuable and relevant contributions. The papers included in this reprint discuss various subjects ranging from cyberattack or criminal activities detection, evaluation of the attacker skills, modeling of the cyber-attacks, and mobile application security evaluation. Given this diversity of topics and the scientific level of papers, we consider this book a valuable reference for researchers in the security and safety of systems

Multimodal Approach for Malware Detection

Although malware detection is a very active area of research, few works were focused on using physical properties (e.g., power consumption) and multimodal features for malware detection. We designed an experimental testbed that allowed us to run samples of malware and non-malicious software applications and to collect power consumption, network traffic, and system logs data, and subsequently to extract dynamic behavioral-based features. We also extracted code-based static features of both malware and non-malicious software applications. These features were used for malware detection based on: feature level fusion using power consumption and network traffic data, feature level fusion using network traffic data and system logs, and multimodal feature level and decision level fusion. The contributions when using feature level fusion of power consumption and network traffic data are: (1) We focused on detecting real malware using the extracted dynamic behavioral features (both power-based and network traffic-based) and supervised machine learning algorithms, which has not been done by any of the prior works. (2) We ran a large number of machine learning experiments, which allowed us to identify the best performing learner, DC voltage rails that led to the best malware detection performance, and the subset of features that are the best predictors for malware detection. (3) The comparison of malware detection performance was done using a comprehensive set of metrics that reflect different aspects of the quality of malware detection. In the case of the feature level fusion using network traffic data and system logs, the contributions are: (1) Most of the previous works that have used network flows-based features have done classification of the network traffic, while our focus was on classifying the software running in a machine as malware and non-malicious software using the extracted dynamic behavioral features. (2) We experimented with different sizes of the training set (i.e., 90%, 75%, 50%, and 25% of the data) and found that smaller training sets produced very good classification results. This aspect of our work has a practical value because the manual labeling of the training set is a tedious and time consuming process. In this dissertation we present a multimodal deep learning neural network that integrates different modalities (i.e., power consumption, system logs, network traffic, and code-based static data) using decision level fusion. We evaluated the performance of each modality individually, when using feature level fusion, and when using decision level fusion. The contributions of our multimodal approach are as follow: (1) Collecting data from different modalities allowed us to develop a multimodal approach to malware detection, which has not been widely explored by prior works. Even more, none of the previous works compared the performance of feature level fusion with decision level fusion, which is explored in this dissertation. (2) We proposed a multimodal decision level fusion malware detection approach using a deep neural network and compared its performance with the performance of feature level fusion approaches based on deep neural network and standard supervised machine learning algorithms (i.e., Random Forest, J48, JRip, PART, Naive Bayes, and SMO)

UTP, Circus, and Isabelle

We dedicate this paper with great respect and friendship to He Jifeng on the occasion of his 80th birthday. Our research group owes much to him. The authors have over 150 publications on unifying theories of programming (UTP), a research topic Jifeng created with Tony Hoare. Our objective is to recount the history of Circus (a combination of Z, CSP, Dijkstra’s guarded command language, and Morgan’s refinement calculus) and the development of Isabelle/UTP. Our paper is in two parts. (1) We first discuss the activities needed to model systems: we need to formalise data models and their behaviours. We survey our work on these two aspects in the context of Circus. (2) Secondly, we describe our practical implementation of UTP in Isabelle/HOL. Mechanising UTP theories is the basis of novel verification tools. We also discuss ongoing and future work related to (1) and (2). Many colleagues have contributed to these works, and we acknowledge their support

Cyber Security of Critical Infrastructures

Critical infrastructures are vital assets for public safety, economic welfare, and the national security of countries. The vulnerabilities of critical infrastructures have increased with the widespread use of information technologies. As Critical National Infrastructures are becoming more vulnerable to cyber-attacks, their protection becomes a significant issue for organizations as well as nations. The risks to continued operations, from failing to upgrade aging infrastructure or not meeting mandated regulatory regimes, are considered highly significant, given the demonstrable impact of such circumstances. Due to the rapid increase of sophisticated cyber threats targeting critical infrastructures with significant destructive effects, the cybersecurity of critical infrastructures has become an agenda item for academics, practitioners, and policy makers. A holistic view which covers technical, policy, human, and behavioural aspects is essential to handle cyber security of critical infrastructures effectively. Moreover, the ability to attribute crimes to criminals is a vital element of avoiding impunity in cyberspace. In this book, both research and practical aspects of cyber security considerations in critical infrastructures are presented. Aligned with the interdisciplinary nature of cyber security, authors from academia, government, and industry have contributed 13 chapters. The issues that are discussed and analysed include cybersecurity training, maturity assessment frameworks, malware analysis techniques, ransomware attacks, security solutions for industrial control systems, and privacy preservation methods

Cyber Infrastructure Protection: Vol. II

View the Executive SummaryIncreased reliance on the Internet and other networked systems raise the risks of cyber attacks that could harm our nation’s cyber infrastructure. The cyber infrastructure encompasses a number of sectors including: the nation’s mass transit and other transportation systems; banking and financial systems; factories; energy systems and the electric power grid; and telecommunications, which increasingly rely on a complex array of computer networks, including the public Internet. However, many of these systems and networks were not built and designed with security in mind. Therefore, our cyber infrastructure contains many holes, risks, and vulnerabilities that may enable an attacker to cause damage or disrupt cyber infrastructure operations. Threats to cyber infrastructure safety and security come from hackers, terrorists, criminal groups, and sophisticated organized crime groups; even nation-states and foreign intelligence services conduct cyber warfare. Cyber attackers can introduce new viruses, worms, and bots capable of defeating many of our efforts. Costs to the economy from these threats are huge and increasing. Government, business, and academia must therefore work together to understand the threat and develop various modes of fighting cyber attacks, and to establish and enhance a framework to assess the vulnerability of our cyber infrastructure and provide strategic policy directions for the protection of such an infrastructure. This book addresses such questions as: How serious is the cyber threat? What technical and policy-based approaches are best suited to securing telecommunications networks and information systems infrastructure security? What role will government and the private sector play in homeland defense against cyber attacks on critical civilian infrastructure, financial, and logistical systems? What legal impediments exist concerning efforts to defend the nation against cyber attacks, especially in preventive, preemptive, and retaliatory actions?https://press.armywarcollege.edu/monographs/1527/thumbnail.jp

Monitoring security of enterprise hosts via DNS data analysis

Enterprise Networks are growing in scale and complexity, with heterogeneous connected assets needing to be secured in different ways. Nevertheless, virtually all connected assets use the Domain Name System (DNS) for address resolution. Thus DNS has become a convenient vehicle for attackers to covertly perform Command and Control (C&C) communication, data theft, and service disruption across a wide range of assets. Enterprise security appliances that monitor network traffic typically allow all DNS traffic through as it is vital for accessing any web service; they may at best match against a database of known malicious patterns, and are therefore ineffective against zero-day attacks. This thesis focuses on three high-impact cyber-attacks that leverage DNS, specifically data exfiltration, malware C&C communication, and service disruption. Using big data (over 10B packets) of DNS network traffic collected from a University campus and a Government research organization over six months, we illustrate the anatomy of these attacks, train machines for automatically detecting such attacks, and evaluate their efficacy in the field. The contributions of this thesis are three-fold: Our first contribution tackles data exfiltration using DNS. We analyze outgoing DNS queries to identify many stateless attributes such as the number of characters, the number of labels, and the entropy of the domain name to distinguish malicious data exfiltration queries from legitimate ones. We train our machines using ground-truth obtained from a public list of top 10K legitimate domains and empirically validate and tune our models to achieve over 98% accuracy in correctly distinguish legitimate DNS queries from malicious ones, the latter coming from known malware domains as well as synthetically generated using popular DNS exfiltration tools. Our second contribution tackles malware C&C communication using DNS. We analyze DNS outgoing queries to identify more than twenty families of DGA (Domain Generation Algorithm)-enabled malware when communicating with their C&C servers. We identify attributes of network traffic that commences following the resolution of a DGA-based DNS query. We train three protocol-specific one-class classifier models, for HTTP, HTTPS and UDP flows, using public packet traces of known malware. We develop a monitoring system that uses reactive rules to automatically and selectively mirror TCP/UDP flows (between internal hosts and malware servers) pertinent to DGA queries for diagnosis by the trained models. We deploy our system in the field and evaluate its performance to show that it flags more than 2000 internal assets as potentially infected, generating more than a million suspicious flows, of which more than 97% are verified to be malicious by an off-the-shelf intrusion detection system. Our third contribution studies the use of DNS for service disruption. We analyze incoming DNS messages, with a specific focus on non-existent (NXD) DNS responses, to distinguish benign from malicious NXDs. We highlight two attack scenarios based on their requested domain names. Using NXD behavioral attributes of internal hosts, we develop multi-staged iForest classification models to detect internal hosts launching service disruption attacks. We show how our models can detect infected hosts that generate high-volume and low-volume distributed NXD-based attacks on public resolvers and/or authoritative name servers with an accuracy of over 99% in correctly classifying legitimate hosts. Our work shines a light on a critical vector in enterprise security and equips the enterprise network operator with the means to detect and block sophisticated attackers who use DNS as a vehicle for malware C&C communication, data exfiltration, and service disruption

Event and Intrusion Detection Systems for Cyber-Physical Power Systems

High speed data from Wide Area Measurement Systems (WAMS) with Phasor Measurement Units (PMU) enables real and non-real time monitoring and control of power systems. The information and communication infrastructure used in WAMS efficiently transports information but introduces cyber security vulnerabilities. Adversaries may exploit such vulnerabilities to create cyber-attacks against the electric power grid. Control centers need to be updated to be resilient not only to well-known power system contingencies but also to cyber-attacks. Therefore, a combined event and intrusion detection systems (EIDS) is required that can provide precise classification for optimal response. This dissertation describes a WAMS cyber-physical power system test bed that was developed to generate datasets and perform cyber-physical power system research related to cyber-physical system vulnerabilities, cyber-attack impact studies, and machine learning algorithms for EIDS. The test bed integrates WAMS components with a Real Time Digital Simulator (RTDS) with hardware in the loop (HIL) and includes various sized power systems with a wide variety of implemented power system and cyber-attack scenarios. This work developed a novel data processing and compression method to address the WAMS big data problem. The State Tracking and Extraction Method (STEM) tracks system states from measurements and creates a compressed sequence of states for each observed scenario. Experiments showed STEM reduces data size significantly without losing key event information in the dataset that is useful to train EIDS and classify events. Two EIDS are proposed and evaluated in this dissertation. Non-Nested Generalized Exemplars (NNGE) is a rule based classifier that creates rules in the form of hyperrectangles to classify events. NNGE uses rule generalization to create a model that has high accuracy and fast classification time. Hoeffding adaptive trees (HAT) is a decision tree classifier and uses incremental learning which is suitable for data stream mining. HAT creates decision trees on the fly from limited number of instances, uses low memory, has fast evaluation time, and adapts to concept changes. The experiments showed NNGE and HAT with STEM make effective EIDS that have high classification accuracy, low false positives, low memory usage, and fast classification times