Cyber Physical System Security — DoS Attacks on Synchrophasor Networks in the Smart Grid

With the rapid increase of network-enabled sensors, switches, and relays, cyber-physical system security in the smart grid has become important. The smart grid operation demands reliable communication. Existing encryption technologies ensures the authenticity of delivered messages. However, commonly applied technologies are not able to prevent the delay or drop of smart grid communication messages. In this dissertation, the author focuses on the network security vulnerabilities in synchrophasor network and their mitigation methods. Side-channel vulnerabilities of the synchrophasor network are identified. Synchrophasor network is one of the most important technologies in the smart grid transmission system. Experiments presented in this dissertation shows that a DoS attack that exploits the side-channel vulnerability against the synchrophasor network can lead to the power system in stability. Side-channel analysis extracts information by observing implementation artifacts without knowing the actual meaning of the information. Synchrophasor network consist of Phasor Measurement Units (PMUs) use synchrophasor protocol to transmit measurement data. Two side-channels are discovered in the synchrophasor protocol. Side-channel analysis based Denial of Service (DoS) attacks differentiate the source of multiple PMU data streams within an encrypted tunnel and only drop selected PMU data streams. Simulations on a power system shows that, without any countermeasure, a power system can be subverted after an attack. Then, mitigation methods from both the network and power grid perspectives are carried out. From the perspective of network security study, side-channel analysis, and protocol transformation has the potential to assist the PMU communication to evade attacks lead with protocol identifications. From the perspective of power grid control study, to mitigate PMU DoS attacks, Cellular Computational Network (CCN) prediction of PMU data is studied and used to implement a Virtual Synchrophasor Network (VSN), which learns and mimics the behaviors of an objective power grid. The data from VSN is used by the Automatic Generation Controllers (AGCs) when the PMU packets are disrupted by DoS attacks. Real-time experimental results show the CCN based VSN effectively inferred the missing data and mitigated the negative impacts of DoS attacks. In this study, industry-standard hardware PMUs and Real-Time Digital Power System Simulator (RTDS) are used to build experimental environments that are as close to actual production as possible for this research. The above-mentioned attack and mitigation methods are also tested on the Internet. Man-In-The-Middle (MITM) attack of PMU traffic is performed with Border Gateway Protocol (BGP) hijacking. A side-channel analysis based MITM attack detection method is also investigated. A game theory analysis is performed to give a broade

Using Botnet Technologies to Counteract Network Traffic Analysis

Botnets have been problematic for over a decade. They are used to launch malicious activities including DDoS (Distributed-Denial-of-Service), spamming, identity theft, unauthorized bitcoin mining and malware distribution. A recent nation-wide DDoS attacks caused by the Mirai botnet on 10/21/2016 involving 10s of millions of IP addresses took down Twitter, Spotify, Reddit, The New York Times, Pinterest, PayPal and other major websites. In response to take-down campaigns by security personnel, botmasters have developed technologies to evade detection. The most widely used evasion technique is DNS fast-flux, where the botmaster frequently changes the mapping between domain names and IP addresses of the C&C server so that it will be too late or too costly to trace the C&C server locations. Domain names generated with Domain Generation Algorithms (DGAs) are used as the \u27rendezvous\u27 points between botmasters and bots. This work focuses on how to apply botnet technologies (fast-flux and DGA) to counteract network traffic analysis, therefore protecting user privacy. A better understanding of botnet technologies also helps us be pro-active in defending against botnets. First, we proposed two new DGAs using hidden Markov models (HMMs) and Probabilistic Context-Free Grammars (PCFGs) which can evade current detection methods and systems. Also, we developed two HMM-based DGA detection methods that can detect the botnet DGA-generated domain names with/without training sets. This helps security personnel understand the botnet phenomenon and develop pro-active tools to detect botnets. Second, we developed a distributed proxy system using fast-flux to evade national censorship and surveillance. The goal is to help journalists, human right advocates and NGOs in West Africa to have a secure and free Internet. Then we developed a covert data transport protocol to transform arbitrary message into real DNS traffic. We encode the message into benign-looking domain names generated by an HMM, which represents the statistical features of legitimate domain names. This can be used to evade Deep Packet Inspection (DPI) and protect user privacy in a two-way communication. Both applications serve as examples of applying botnet technologies to legitimate use. Finally, we proposed a new protocol obfuscation technique by transforming arbitrary network protocol into another (Network Time Protocol and a video game protocol of Minecraft as examples) in terms of packet syntax and side-channel features (inter-packet delay and packet size). This research uses botnet technologies to help normal users have secure and private communications over the Internet. From our botnet research, we conclude that network traffic is a malleable and artificial construct. Although existing patterns are easy to detect and characterize, they are also subject to modification and mimicry. This means that we can construct transducers to make any communication pattern look like any other communication pattern. This is neither bad nor good for security. It is a fact that we need to accept and use as best we can

On the Generation of Cyber Threat Intelligence: Malware and Network Traffic Analyses

In recent years, malware authors drastically changed their course on the subject of threat design and implementation. Malware authors, namely, hackers or cyber-terrorists perpetrate new forms of cyber-crimes involving more innovative hacking techniques. Being motivated by financial or political reasons, attackers target computer systems ranging from personal computers to organizations’ networks to collect and steal sensitive data as well as blackmail, scam people, or scupper IT infrastructures. Accordingly, IT security experts face new challenges, as they need to counter cyber-threats proactively. The challenge takes a continuous allure of a fight, where cyber-criminals are obsessed by the idea of outsmarting security defenses. As such, security experts have to elaborate an effective strategy to counter cyber-criminals. The generation of cyber-threat intelligence is of a paramount importance as stated in the following quote: “the field is owned by who owns the intelligence”. In this thesis, we address the problem of generating timely and relevant cyber-threat intelligence for the purpose of detection, prevention and mitigation of cyber-attacks. To do so, we initiate a research effort, which falls into: First, we analyze prominent cyber-crime toolkits to grasp the inner-secrets and workings of advanced threats. We dissect prominent malware like Zeus and Mariposa botnets to uncover their underlying techniques used to build a networked army of infected machines. Second, we investigate cyber-crime infrastructures, where we elaborate on the generation of a cyber-threat intelligence for situational awareness. We adapt a graph-theoretic approach to study infrastructures used by malware to perpetrate malicious activities. We build a scoring mechanism based on a page ranking algorithm to measure the badness of infrastructures’ elements, i.e., domains, IPs, domain owners, etc. In addition, we use the min-hashing technique to evaluate the level of sharing among cyber-threat infrastructures during a period of one year. Third, we use machine learning techniques to fingerprint malicious IP traffic. By fingerprinting, we mean detecting malicious network flows and their attribution to malware families. This research effort relies on a ground truth collected from the dynamic analysis of malware samples. Finally, we investigate the generation of cyber-threat intelligence from passive DNS streams. To this end, we design and implement a system that generates anomalies from passive DNS traffic. Due to the tremendous nature of DNS data, we build a system on top of a cluster computing framework, namely, Apache Spark [70]. The integrated analytic system has the ability to detect anomalies observed in DNS records, which are potentially generated by widespread cyber-threats

Context-aware Security for Vehicles and Fleets: A Survey

Vehicles are becoming increasingly intelligent and connected. Interfaces for communication with the vehicle, such as WiFi and 5G, enable seamless integration into the user’s life, but also cyber attacks on the vehicle. Therefore, research is working on in-vehicle countermeasures such as authentication, access controls, or intrusion detection. Recently, legal regulations have also become effective that require automobile manufacturers to set up a monitoring system for fleet-wide security analysis. The growing amount of software, networking, and the automation of driving create new challenges for security. Context-awareness, situational understanding, adaptive security, and threat intelligence are necessary to cope with these ever-increasing risks. In-vehicle security should be adaptive to secure the car in an infinite number of (driving) situations. For fleet-wide analysis and alert triage, knowledge and understanding of the circumstances are required. Context-awareness, nonetheless, has been sparsely considered in the field of vehicle security. This work aims to be a precursor to context-aware, adaptive and intelligent security for vehicles and fleets. To this end, we provide a comprehensive literature review that analyzes the vehicular as well as related domains. Our survey is mainly characterized by the detailed analysis of the context information that is relevant for vehicle security in the future

Integrated Framework for Data Quality and Security Evaluation on Mobile Devices

Data quality (DQ) is an important concept that is used in the design and employment of information, data management, decision making, and engineering systems with multiple applications already available for solving specific problems. Unfortunately, conventional approaches to DQ evaluation commonly do not pay enough attention or even ignore the security and privacy of the evaluated data. In this research, we develop a framework for the DQ evaluation of the sensor originated data acquired from smartphones, that incorporates security and privacy aspects into the DQ evaluation pipeline. The framework provides support for selecting the DQ metrics and implementing their calculus by integrating diverse sensor data quality and security metrics. The framework employs a knowledge graph to facilitate its adaptation in new applications development and enables knowledge accumulation. Privacy aspects evaluation is demonstrated by the detection of novel and sophisticated attacks on data privacy on the example of colluded applications attack recognition. We develop multiple calculi for DQ and security evaluation, such as a hierarchical fuzzy rules expert system, neural networks, and an algebraic function. Case studies that demonstrate the framework\u27s performance in solving real-life tasks are presented, and the achieved results are analyzed. These case studies confirm the framework\u27s capability of performing comprehensive DQ evaluations. The framework development resulted in producing multiple products, and tools such as datasets and Android OS applications. The datasets include the knowledge base of sensors embedded in modern mobile devices and their quality analysis, technological signals recordings of smartphones during the normal usage, and attacks on users\u27 privacy. These datasets are made available for public use and can be used for future research in the field of data quality and security. We also released under an open-source license a set of Android OS tools that can be used for data quality and security evaluation

A Novel Approach to Determining Real-Time Risk Probabilities in Critical Infrastructure Industrial Control Systems

Critical Infrastructure Industrial Control Systems are substantially different from their more common and ubiquitous information technology system counterparts. Industrial control systems, such as distributed control systems and supervisory control and data acquisition systems that are used for controlling the power grid, were not originally designed with security in mind. Geographically dispersed distribution, an unfortunate reliance on legacy systems and stringent availability requirements raise significant cybersecurity concerns regarding electric reliability while constricting the feasibility of many security controls. Recent North American Electric Reliability Corporation Critical Infrastructure Protection standards heavily emphasize cybersecurity concerns and specifically require entities to categorize and identify their Bulk Electric System cyber systems; and, have periodic vulnerability assessments performed on those systems. These concerns have produced an increase in the need for more Critical Infrastructure Industrial Control Systems specific cybersecurity research. Industry stakeholders have embraced the development of a large-scale test environment through the Department of Energy’s National Supervisory Control and Data Acquisition Test-bed program; however, few individuals have access to this program. This research developed a physical industrial control system test-bed on a smaller-scale that provided an environment for modeling a simulated critical infrastructure sector performing a set of automated processes for the purpose of exploring solutions and studying concepts related to compromising control systems by way of process-tampering through code exploitation, as well as, the ability to passively and subsequently identify any risks resulting from such an event. Relative to the specific step being performed within a production cycle, at a moment in time when sensory data samples were captured and analyzed, it was possible to determine the probability of a real-time risk to a mock Critical Infrastructure Industrial Control System by comparing the sample values to those derived from a previously established baseline. This research achieved such a goal by implementing a passive, spatial and task-based segregated sensor network, running in parallel to the active control system process for monitoring and detecting risk, and effectively identified a real-time risk probability within a Critical Infrastructure Industrial Control System Test-bed. The practicality of this research ranges from determining on-demand real-time risk probabilities during an automated process, to employing baseline monitoring techniques for discovering systems, or components thereof, exploited along the supply chain