Counter Unmanned Aerial System (CUAS) security systems have unrealistic performance expectations hyped on marketing and idealistic testing environments. By developing an agent-based model to simulate these systems, an average performance metric can be obtained, thereby providing better representative values of true system performance.<br><br>Due to high cost, excessive risk, and exponentially large parameter possibilities, it is unrealistic to test a CUAS system for optimal performance in the real world. Agent-based simulation can provide the necessary variability at a low cost point and allow for numerous parametric possibilities to provide actionable output from the CUAS system. <br><br>This study describes and documents the Simulation of CUAS Networks and Sensors (SCANS) Framework in a novel attempt at developing a flexible modeling framework for CUAS systems based on device parameters. The core of the framework rests on sensor and communication device agents. These sensors, including Acoustic, Radar, Passive Radio Frequency (RF), and Camera, use input parameters, sensor specifications, and UAS specifications to calculate such values as the sound pressure level, received signal strength, and maximum viewable distance. The communication devices employ a nearest-neighbor routing protocol to pass messages from the system which are then logged by a command and control agent. <br><br>This framework allows for the flexibility of modeling nearly any CUAS system and is designed to be easily adjusted. The framework is capable of reporting true positives, true negatives, and false negatives in terms of UAS detection. For testing purposes, the SCANS Framework was deployed in AnyLogic and models were developed based on existing, published, empirical studies of sensors and detection UAS.<br>

The Internet of Things is deploying large numbers of bare-metal systems that have no protection against memory corruption and control-flow hijacking attacks. These attacks have enabled unauthorized entry to hotel rooms, malicious control of unmanned aerial vehicles, and invasions of privacy. Using static and dynamic analysis these systems can utilize state-of-the-art testing techniques to identify and<br>prevent memory-corruption errors and employ defenses against memory corruption and control-flow hijacking attacks in bare-metal systems that match or exceed those currently employed on desktop systems. This is shown using three case studies.<br><br>(1) EPOXY which, automatically applies data execution prevention, diversity, stack defenses, and separating privileged code from unprivileged code using a novel<br>technique called privileged overlaying. These protections prevent code injection attacks, and reduce the number of privileged instruction to 0.06% verses an unprotected<br>application.<br><br>(2) Automatic Compartments for Embedded Systems (ACES), which automatically creates compartments that enforce data integrity and code isolation within bare-metal applications. ACES enables exploring policies to best meet security and performance requirements for individual applications. Results show ACES' can form 10s of compartments within a single thread and has a 15% runtime overhead on average.<br><br><div>(3) HALucinator breaks the requirement for specialized hardware to perform bare-metal system testing. This enables state-of-the-art testing techniques –e.g., coverage based fuzzing – to scale with the availability of commodity computers, leading to the discovery of exploitable vulnerabilities in bare-metal systems. <br></div><div><br></div><div>Combined, these case studies advance the security of embedded system several decades and provide essential protections for today’s connected devices.</div>

This thesis investigates a method for tackling the verification of parametric systems, systems whose behaviour may depend on the value of one or more parameters. The range of allowable values for such parameters may, in general, be large or unknown. This results in a large number of instances of a system that require verification, one instance for each allowable combination of parameter values. When one or more parameters are unbounded, the family of systems that require verification becomes infinite. Computer protocols are one example of such parametric systems. They may have parameters such as the maximum sequence number or the maximum number of retransmissions. Traditional protocol verification approaches usually only analyse and verify properties of a parametric system for a small range of parameter values. It is impossible to verify in this way every concrete instance of an infinite family of systems. Also, the number of reachable states tends to increase dramatically with increasing parameter values, and thus the well known state explosion phenomenon also limits the range of parameters for which the system can be analysed. In this thesis, we concentrate on the parametric verification of the Stop-and-Wait Protocol (SWP), an elementary flow control protocol. We have used Coloured Petri Nets (CPNs) to model the SWP, operating over an in-order but lossy medium, with two unbounded parameters: the maximum sequence number; and the maximum number of retransmissions. A novel method has been used for symbolically representing the parametric reachability graph of our parametric SWP CPN model. This parametric reachability graph captures exactly the infinite family of reachability graphs resulting from the infinite family of SWP CPNs. The parametric reachability graph is represented symbolically as a set of closed-form algebraic expressions for the nodes and arcs of the reachability graph, expressed in terms of the two parameters. By analysing the reachability graphs of the SWP CPN model for small parameter values, structural regularities in the reachability graphs were identified and exploited to develop the appropriate algebraic expressions for the parametric reachability graph. These expressions can be analysed and manipulated directly, thus the properties that are verified from these expressions are verified for all instances of the system. Several properties of the SWP that are able to be verified directly from the parametric reachability graph have been identified. These include a proof of the size of the parametric reachability graph in terms of both parameters, absence of deadlocks (undesired terminal states), absence of livelocks (undesirable cycles of behaviour from which the protocol cannot escape), absence of dead transitions (actions that can never occur) and the upper bounds on the content of the underlying communication channel. These are verified from the algebraic expressions and thus hold for all parameter values. Significantly, language analysis is also carried out on the parametric SWP. The parametric reachability graph is translated into a parametric Finite State Automaton (FSA), capturing symbolically the infinite set of protocol languages (i.e. sequences of user observable events) by means of similar algebraic expressions to those of the parametric reachability graph. Standard FSA reduction techniques were applied in a symbolic fashion directly to the parametric FSA, firstly to obtain a deterministic representation of the parametric FSA, then to obtain an equivalent minimised FSA. It was found that the determinisation procedure removed the effect of the maximum number of retransmissions parameter, and the minimisation procedure removed the effect of the maximum sequence number parameter. Conformance of all instances of the SWP over both parameters to its desired service language is proved. The development of algebraic expressions to represent the infinite class of Stop-and-Wait Protocols, and the verification of properties (including language analysis) directly from these algebraic expressions, has demonstrated the potential of this method for the verification of more general parametric systems. This thesis provides a significant contribution toward the development of a general parametric verification methodology.

Over the past three decades, agricultural machinery has made the transition from purely mechanical systems to hybrid machines, reliant on both mechanical and electronic systems. A this transformation continues, the most modern agricultural machinery uses networked systems that require a network connection to function to their full potential. In rural areas, providing this network connection has proven difficult. Obstacles, distance from access points, and incomplete coverage of cellular connection are all challenges to be overcome. “Off the shelf” commercial-grade Wi-Fi equipment, including many products from Ubiquiti like the Bullet M2 transceiver and the PowerBeam point-to-point linking system, as well as antennas by Terrawave, Crane, and Hawking, were installed in a purpose-built system which could be implemented on a production farm. This system consisted of a tower-mounted access point which used an antenna with a 65^o beamwidth, and the test included distances up to 1150 meters in an agricultural setting with corn and soybeans. Some sensors were stationary and the other platform was a tractor following a path around the farm with both 8dBi and 15dBi gain antennas. Through all tests, throughput never dropped below 5 Mb/s, and the latency of successful connections never exceeded 20ms. Packets were rarely dropped and never accounted for a significant portion of all packet transmission attempts. Environmental effects like immediate precipitation, crop heights, recent rainfall, and ambient temperature had little or no effect on wireless network characteristics. As a result, it was proven that as long as line-of-sight was maintained, reliable wireless connectivity could be achieved despite varying conditions using microwave radiation. Network throughput was marginally affected by the change in free space path loss due to increased distance between the access point and the client, as well as travel by the mobile client outside the beamwidth of the access point. By enabling this coverage, it is hoped that the implementation of new agricultural technology utilizing a live network connection will progress more rapidly.

In recent years, there has been a rapid growth in deployment and usage of realtime network applications, such as Voice-over-IP, video calls/video conferencing, live network seminars, and networked gaming. The continued increase in the popularity of realtime applications requires a more intense focus on the provision of strict guarantees for Quality of Service (QoS) parameters such as delay, jitter and packet loss in access networks. At the same time, wireless networking technologies have become increasingly popular with a wide array of devices such as laptop computers, Personal Digital Assistants (PDAs), and cellular phones being sold with built-in WiFi and WiMAX interfaces. For realtime applications to be popular over wireless networks, simple, robust and effective QoS mechanisms suited for a variety of heterogeneous wireless networks must be devised. Implementing the same QoS mechanisms across multiple neighbouring networks aids seamless handover by ensuring that a flow will be treated in the same way, both before and after handover. To provide guaranteed QoS, an access network should limit load using an admission control algorithm. In this research, we propose a method to provide effective admission control for variable bit rate realtime flows, based on the Central Limit Theorem. Our objective is to estimate the percentage of packets that will be delayed beyond a predefined delay threshold, based on the mean and variance of all the flows in the system. Any flow that will increase the percentage of delayed packets beyond an acceptable threshold can then be rejected. Using simulations we have shown that the proposed method provides a very effective control of the total system load, guaranteeing the QoS for a set of accepted flows with negligible reductions in the system throughput. To ensure that flow data is transmitted according to the QoS requirements of a flow, a scheduling algorithm must handle data intelligently. We propose methods to allow more efficient scheduling by utilising existing Medium Access Control mechanisms to exchange flow information. We also propose a method to determine the delay-dependent "value" of a packet based on the QoS requirements of the flow. Using this value in scheduling is shown to increase the number of packets sent before a predetermined deadline. We propose a measure of fairness in scheduling that is calculated according to how well each flow's QoS requirements are met. We then introduce a novel scheduling paradigm, Delay Loss Controlled-Earliest Deadline First (DLC-EDF), which is shown to provide better QoS for all flows compared to other scheduling mechanisms studied. We then study the performance of our admission control and scheduling methods working together, and propose a feedback mechanism that allows the admission control threshold to be tuned to maximise the efficient usage of available bandwidth in the network, while ensuring that the QoS requirements of all realtime flows are met. We also examine heterogeneous/vertical handover, providing an overview of the technologies supporting seamless handover. The issues studied in this area include a method of using the Signal to Noise Ratio to trigger handover in heterogeneous networks and QoS Mapping between heterogeneous networks. Our proposed method of QoS mapping establishes the minimum set of QoS parameters applicable to individual flows, and then maps these parameters into system parameter formats for both 802.11e and 802.16e networks.

Opportunistic communication techniques have shown to provide significant performance improvements in centralised random access wireless networks. The key mechanism of opportunistic communication is to send back-to-back data packets whenever the channel quality is deemed "good". Recently there have been attempts to introduce opportunistic communication techniques in distributed wireless networks such as wireless ad hoc networks. In line of this research, we propose a new paradigm of medium access control, called Channel MAC based on the channel randomness and opportunistic communication principles. Scheduling in Channel MAC depends on the instance at which the channel quality improves beyond a threshold, while neighbouring nodes are deemed to be silent. Once a node starts transmitting, it will keep transmitting until the channel becomes "bad". We derive an analytical throughput equation of the proposed MAC in a multiple access environment and validate it by simulations. It is observed that Channel MAC outperforms IEEE 802.11 for all probabilities of good channel condition and all numbers of nodes. For higher number of nodes, Channel MAC achieves higher throughput at lower probabilities of good channel condition increasing the operating range. Furthermore, the total throughput of the network grows with increasing number of nodes considering negligible propagation delay in the network. A scalable channel prediction scheme is required to implement the practical Channel MAC protocol in practice. We propose a mean-value based channel prediction scheme, which provides prediction with enough accuracy to be used in the Channel MAC protocol. NS2 simulation result shows that the Channel MAC protocol outperforms the IEEE 802.11 in throughput due to its channel diversity mechanism in spite of the prediction errors and packet collisions. Next, we extend the Channel MAC protocol to support multi-rate communications. At present, two prominent multi-rate mechanisms, Opportunistic Auto Rate (OAR) and Receiver Based Auto Rate (RBAR) are unable to adapt to short term changes in channel conditions during transmission as well as to use optimum power and throughput during packet transmissions. On the other hand, using channel predictions, each source-destinations pair in Channel MAC can fully utilise the non-fade durations. We combine the scheduling of Channel MAC and the rate adaptive transmission based on the channel state information to design the 'Rate Adaptive Channel MAC' protocol. However, to implement the Rate adaptive Channel MAC, we need to use a channel prediction scheme to identify transmission opportunities as well as auto rate adaptation mechanism to select rates and number of packets to transmit during those times. For channel prediction, we apply the scheme proposed for the practical implementation of Channel MAC. We propose a "safety margin" based technique to provide auto rate adaptation. Simulation results show that a significant performance improvement can be achieved by Rate adaptive Channel MAC as compared to existing rate adaptive protocols such as OAR.

Opportunistic communication techniques have shown to provide significant performance improvements in centralised random access wireless networks. The key mechanism of opportunistic communication is to send back-to-back data packets whenever the channel quality is deemed "good". Recently there have been attempts to introduce opportunistic communication techniques in distributed wireless networks such as wireless ad hoc networks. In line of this research, we propose a new paradigm of medium access control, called Channel MAC based on the channel randomness and opportunistic communication principles. Scheduling in Channel MAC depends on the instance at which the channel quality improves beyond a threshold, while neighbouring nodes are deemed to be silent. Once a node starts transmitting, it will keep transmitting until the channel becomes "bad". We derive an analytical throughput equation of the proposed MAC in a multiple access environment and validate it by simulations. It is observed that Channel MAC outperforms IEEE 802.11 for all probabilities of good channel condition and all numbers of nodes. For higher number of nodes, Channel MAC achieves higher throughput at lower probabilities of good channel condition increasing the operating range. Furthermore, the total throughput of the network grows with increasing number of nodes considering negligible propagation delay in the network. A scalable channel prediction scheme is required to implement the practical Channel MAC protocol in practice. We propose a mean-value based channel prediction scheme, which provides prediction with enough accuracy to be used in the Channel MAC protocol. NS2 simulation result shows that the Channel MAC protocol outperforms the IEEE 802.11 in throughput due to its channel diversity mechanism in spite of the prediction errors and packet collisions. Next, we extend the Channel MAC protocol to support multi-rate communications. At present, two prominent multi-rate mechanisms, Opportunistic Auto Rate (OAR) and Receiver Based Auto Rate (RBAR) are unable to adapt to short term changes in channel conditions during transmission as well as to use optimum power and throughput during packet transmissions. On the other hand, using channel predictions, each source-destinations pair in Channel MAC can fully utilise the non-fade durations. We combine the scheduling of Channel MAC and the rate adaptive transmission based on the channel state information to design the 'Rate Adaptive Channel MAC' protocol. However, to implement the Rate adaptive Channel MAC, we need to use a channel prediction scheme to identify transmission opportunities as well as auto rate adaptation mechanism to select rates and number of packets to transmit during those times. For channel prediction, we apply the scheme proposed for the practical implementation of Channel MAC. We propose a "safety margin" based technique to provide auto rate adaptation. Simulation results show that a significant performance improvement can be achieved by Rate adaptive Channel MAC as compared to existing rate adaptive protocols such as OAR.

The internet is the home for massive volumes of valuable data constantly being created, making it difficult for users to find information relevant to them. In recent times, online users have been relying on the recommendations made by websites to narrow down the options. Online reviews have also become an increasingly important factor in the final choice of a customer. Unfortunately, attackers have found ways to manipulate both reviews and recommendations to mislead users. A Recommendation System is a special type of information filtering system adapted by online vendors to provide suggestions to their customers based on their requirements. Collaborative filtering is one of the most widely used recommendation systems; unfortunately, it is prone to shilling/profile injection attacks. Such attacks alter the recommendation process to promote or demote a particular product. On the other hand, many spammers write deceptive reviews to change the credibility of a product/service. This work aims to address these issues by treating the review manipulation and shilling attack scenarios independently. For the shilling attacks, we build an efficient Reinforcement Learning-based shilling attack method. This method reduces the uncertainty associated with the item selection process and finds the most optimal items to enhance attack reach while treating the recommender system as a black box. Such practical online attacks open new avenues for research in building more robust recommender systems. When it comes to review manipulations, we introduce a method to use a deep structure embedding approach that preserves highly nonlinear structural information and the dynamic aspects of user reviews to identify and cluster the spam users. It is worth mentioning that, in the experiment with real datasets, our method captures about 92\% of all spam reviewers using an unsupervised learning approach.<br>

With the growing business impact of distributed big data analytics jobs, it has become crucial to optimize their execution and resource consumption. In most cases, such jobs consist of multiple sub-entities called tasks and are executed online in a large shared distributed computing system. The ability to accurately estimate runtime properties and coordinate execution of sub-entities of a job allows a scheduler to efficiently schedule jobs for optimal scheduling. This thesis presents the first study that highlights spatial dimension, an inherent property of distributed jobs, and underscores its importance in efficient cluster job scheduling. We develop two new classes of spatial dimension based algorithms to<br>address the two primary challenges of cluster scheduling. First, we propose, validate, and design two complete systems that employ learning algorithms exploiting spatial dimension. We demonstrate high similarity in runtime properties between sub-entities of the same job by detailed trace analysis on four different industrial cluster traces. We identify design challenges and propose principles for a sampling based learning system for two examples, first for a coflow scheduler, and second for a cluster job scheduler.<br>We also propose, design, and demonstrate the effectiveness of new multi-task scheduling algorithms based on effective synchronization across the spatial dimension. We underline and validate by experimental analysis the importance of synchronization between sub-entities (flows, tasks) of a distributed entity (coflow, data analytics jobs) for its efficient execution. We also highlight that by not considering sibling sub-entities when scheduling something it may also lead to sub-optimal overall cluster performance. We propose, design, and implement a full coflow scheduler based on these assertions.

Cloud computing allows on-demand provision, configuration and assignment of computing resources with minimum cost and effort for users and administrators. Managing the physical infrastructure that underlies cloud computing services relies on the need to provision and manage bare-metal computer hardware. Hence there is a need for quick loading of operating systems in bare-metal and virtual machines to service the demands of users. The focus of the study is on developing a technique to load these machines remotely, which is complicated by the fact that the machines can be present in different Ethernet broadcast domains, physically distant from the provisioning server. The use of available bare-metal provisioning frameworks require significant skills and time. Moreover, there is no easily implementable standard method of booting across separate and different Ethernet broadcast domains. This study proposes a new framework to provision bare-metal hardware remotely using layer 2 services in a secure manner. This framework is a composition of existing tools that can be assembled to build the framework.