Distributed Energy Resources (DER), like solar panels, are projected to take over power generation responsibilities. This will happen during the transition of the current power grid to the Smart Grid. Due to the importance of this power to society, it is crucial that the grid stays stable. DER devices are similar to IoT devices in scale, low user interaction and the use of firmware. IoT cyberattacks have been shown to have the ability to scale horizontally quickly. A vulnerability in DER devices could lead to such a scalable attack if the market for DER is oligopolistic. Due to the same underlying economic drivers such as economy-of-scale, market-for-lemons, first-mover-advantage and tragedy-of-the-commons, DER devices will likely have the same issues as IoT devices had if nothing is changed. This research focuses on the role of the grid’s transition state and the DER market’s state in introducing this risk. Eight thousand one hundred (8100) scenarios were created based on a combination of parameters describing these states. An agent-based model created for this research simulated the grid and obtained the required data. Results indicate that the grid and market parameters can introduce a cyber risk into the Smart Grid. The results show that if 5% of the households are infected, an attacker could abuse them to manipulate the grid, perhaps a blackout. Furthermore, related work did not show any references to this particular risk and some proposed grid monitoring solutions include the usage of neighbouring DER to monitor. An attack of this nature would be able to manipulate such a monitoring solution. If the risk of an oligopolistic DER market is not considered, the Smart Grid may not have any ways of effective monitoring or mitigation. Recommendations for policymakers and regulators were made as part of this research. The first recommendation is to allow the collection of real-time information on the grid-connected DER by grid operators. Furthermore, consideration has to be made to the usage of forced patching on DER. A delay in patching could impact the grid too much. Finally, the recommendation is to develop a policy on the local diversity of DER.  Devices with the same firmware should not be allowed to obtain a critical mass in a region.

Improving the energy efficiency of electric vehicles has various significant benefits, such as increasing the driving range. The Thermal Management System (TMS) plays a large role in optimizing the vehicle energy consumption and battery lifetime. In this thesis, a nonlinear Model Predictive control (MPC) strategy is presented to regulate the battery, motor and inverter temperatures to minimize vehicle energy consumption and maximize battery lifetime, two conflicting objectives traded off by a single tunable parameter, whilst staying within temperature limits to ensure safety. The control dynamics are nonlinear and discontinuous, due to valves in the system that can only be fully opened or fully closed, leading to a nonlinear mixed-integer optimization problem. A nonlinear model of the TMS including actuators and electric components is formulated that is validated by using simulation results over three drive cycles for moderate and hot ambient conditions. Mean temperature deviations between -0.22 and 0.25 °C are achieved for the battery, and between 0.14 and 1.48 °C for the motors, depending on the drive cycle. Using outer convexification, the optimization problem is reformulated as a continuous problem, which can be solved efficiently. The control strategy is tested in a simulation environment for both moderate and hot ambient temperatures and three different drive cycles. The strategy is compared to a benchmark strategy that uses a finite-state machine and PID-based control loops. A decrease in power consumption between 7% and 11% is achieved for 5 out of 6 use cases whilst additionally decreasing the ageing rate by 0% to 7%. For one use case, an increase in energy consumption is achieved by 2.5%, but the relative ageing rate is decreased by 44%. At hot temperatures, improvements are mostly achieved due to finding an energy-efficient battery cooling trajectory. At moderate temperatures, improvements are mostly achieved by increased motor cooling to take advantage of the temperature-dependent motor losses. The results obtained using a continuous solver are also compared to those obtained using a mixed-integer solver. Minimal loss in performance is seen compared to the mixed-integer solver, whilst requiring a significantly lower computation time that is within the sample time, making the MPC strategy fast enough for real-time control in the simulation environment. Finally, the effect of using noisy forecast information is used to test the robustness of the controller. Using noisy forecast information instead of perfect forecast information, the energy consumption and ageing rate increase by 0.0% to 1.2%.

Accurate short-term traffic forecasting plays a crucial role in Intelligent Transportation Systems for effective traffic management and planning. In this study, the performances of three popular forecasting models are explored: Long Short-Term Memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), and Facebook's Prophet, for short-term traffic prediction. The models were trained and evaluated using a dataset of traffic flow data collected from 161 detectors over a specific time period. The experimental results reveal that ARIMA outperformed LSTM and Prophet in terms of Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). This suggests that while deep learning methods, such as LSTM, are generally acknowledged to outperform ARIMA in short-term traffic forecasting, this study reveals that there are specific scenarios where such well-accepted fact needs to be tested.

The amount of cars on the roads is increasing at a rapid pace, causing traffic jams to become commonplace. One way to decrease the amount of traffic congestion is by building an Intelligent Transportation System (ITS) which helps traffic flow optimally. An important tool for an ITS is short term traffic forecasting. Better forecasts will enable the ITS to proactively prevent congestion. Recent years have seen a great increase in the availability of traffic data. As a result deep learning approaches have begun to emerge as models of choice in the short term traffic forecasting domain. Among deep learning approaches Long Short Term Memory (LSTM) and Temporal Convolutional Networks (TCN) have both shown state-of-the-art performance in general forecasting tasks as well as promising results in traffic forecasting. This work has compared both of these approaches in terms of capturing the temporal spatial correlation and scalability. The LSTM showed more ability to capture the temporal spatial correlation while both architectures seemed equally scalable.

The increasing global food demand, accompanied by the decreasing number of expert growers, brings the need for more sustainable and efficient solutions in horticulture. Consultancy company Delphy aims to face this challenge by taking a more data-driven approach, by means of autonomous growing inside the greenhouse. The controlled environment of greenhouses enable data collection and precise control. Delphy's current solutions focus on the needs of the crop, but a grower also needs to consider the economic aspect of taking certain decisions on the greenhouse climate. A potential method for solving this complex problem is Constraint Programming (CP). In this work, the applicability of CP for the greenhouse economic optimal control problem will be studied. The contributions of this work are threefold; First, the greenhouse climate is modelled with Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN) machine learning models. Secondly, an LSTM model is embedded into a CP model. Lastly, the profit of the grower is optimised through this CP decision support system (DSS). The performed experiments show that both types of time-based machine learning models can model greenhouse temperature and humidity deficit with reasonable accuracy, while light and CO2 are harder to predict. The correctness of the LSTM-in-CP embedding is validated. The implemented DSS is not yet practically applicable, as the search space is too large to come to reasonable results for realistic instances. For small instances however, the DSS is able to improve the decisions of the grower, demonstrating the potential of using CP for economic greenhouse decision making.

Research has shown that the Border Gateway Protocol (BGP) is vulnerable to a new attack that exploits the community attribute. These community attacks can influence BGP routing in unintended ways. Currently, there are no effective mitigations against these attacks which do not limit the normal usage of BGP communities or offer cryptographic authorization of community values. In this thesis, we are the first to design and evaluate a new Resource Public Key Infrastructure (RPKI) object that provides these properties, called a Route Community Authorization (RCA). Autonomous Systems (ASes) can sign and upload an RCA that includes which communities are authorized to be used and under which conditions to reduce the attack surface. The approach does not alter BGP itself and ASes that deploy the security solution can verify if route announcements with community values are allowed to use those communities. With the RPKI construction of our solution, cryptographic operations are offloaded from the router to a separate server. This is because most BGP routers deployed on the internet are not capable yet of performing cryptographic operations on every announcement. We simulate attack scenarios and evaluate the developed solution to see if it is a viable option to be deployed on the internet. The results show that the developed solution can block the community attacks, on the condition that ASes have properly configured the RCA and at least the target, or an AS between the attacker and the target, deploys the solution. If ASes configure the RCAs too broad, then there is still room for an attacker to abuse the community values. We show that the performance of the solution using properly configured RCAs can keep up with the rate of announcements seen on the internet. Since experiments were performed in simulations, additional experiments are needed in the future on hardware routers and by ASes on the internet to see if the results are the same. The biggest issue for the developed solution is the initial adoption by ASes since security benefits are low in the beginning, but increase once more ASes participate. Once deployed BGP routers can handle cryptographic operations on every announcement, we suggest using BGPsec and extending it to include the community attribute. Using a BGPsec approach will improve security and provide integrity of the community attribute, which RCA does not provide.

Traffic prediction plays a big role in efficient transport planning capabilities and can reduce traffic congestion. In this study the application of Long Short-Term Memory (LSTM) models for predicting traffic volumes across varying prediction horizons is investigated. The data used is collected by the municipality of The Hague for a single month. The study focuses on comparing the performance of the LSTM across different time horizons up to 10 hours in the future. To evaluate the performance of the LSTM models, two common evaluation measures are employed: Root Mean Square Error (RMSE) and Symmetric Mean Absolute Percentage Error (SMAPE). The baseline for the predictions is set at a 15-minute future forecast. Comparing the 1-hour prediction against the 10-hour predictions relative to the baseline RMSE, the RMSE increased threefold. However, the SMAPE first increases, but surprisingly after 6 hours starts to decrease again.

Amplification Denial of Service (DoS) attacks have been a persistent challenge in network security, with the consequences ranging from causing minor disruptions to substantial financial losses and irreparable damage to reputation. In today's network environment, many infrastructures are not primary targets of amplification attacks but unwittingly aid them by sending large responses generated by spoofed packets to the potential victims. The ever-growing number of servers makes manual detection of vulnerable components impractical, emphasizing the urgent need for automated tools, which are currently lacking. This paper investigates factors that affect amplification DoS attacks on three UDP-based protocols, DNS, NTP, and Memcached. Our analysis indicates that for DNS, factors such as the buffer size, replying to ANY queries, Resource Records (RR), and Name Servers (NS) per domain significantly impact the amplification potential. For Memcached, the key and value lengths substantially affect the amplification factor. Regarding NTP, the magnitude of amplification is influenced by the number of recently contacted clients, with the version being a critical determinant for the likelihood of attack success for both NTP and Memcached. By incorporating these parameters, we propose the development of an automated tool capable of identifying such vulnerable components within network infrastructures.

Amplification Denial of Service (DoS) attacks have been a persistent challenge in network security, with the consequences ranging from causing minor disruptions to substantial financial losses and irreparable damage to reputation. In today's network environment, many infrastructures are not primary targets of amplification attacks but unwittingly aid them by sending large responses generated by spoofed packets to the potential victims. The ever-growing number of servers makes manual detection of vulnerable components impractical, emphasizing the urgent need for automated tools, which are currently lacking. This paper investigates factors that affect amplification DoS attacks on three UDP-based protocols, DNS, NTP, and Memcached. Our analysis indicates that for DNS, factors such as the buffer size, replying to ANY queries, Resource Records (RR), and Name Servers (NS) per domain significantly impact the amplification potential. For Memcached, the key and value lengths substantially affect the amplification factor. Regarding NTP, the magnitude of amplification is influenced by the number of recently contacted clients, with the version being a critical determinant for the likelihood of attack success for both NTP and Memcached. By incorporating these parameters, we propose the development of an automated tool capable of identifying such vulnerable components within network infrastructures.

In recent years, the novel framing of the caching problem as an Online Convex Optimisation (OCO) problem has led to the introduction of several online caching policies. These policies are proven optimal with regard to regret for any arbitrary request pattern, including that of adversarial origin. Nevertheless, their performance is primarily affected by the tuning of their so-called hyperparameters (e.g. learning rate), which is often done under the presumption of adversarial conditions. Consequently, as not all request patterns encountered in practice are adversarial, this suggests potential for further improvements. Unfortunately, the tuning of these hyperparameters to achieve optimal performance across non-adversarial request sequences remains dubious and poses a new challenge. This paper proposes an online meta-learner that combines several instances of the OGA caching policy, each distinct in their learning rate, framing the problem as an expert advice decision problem. The proposed meta-learner dynamically shifts between caching experts and achieves a regret upper bound similar to that of the best-performing caching expert. The penalty introduced for learning the best expert is limited to a logarithmic dependence on the number of experts, which improves upon previous works. Numerical evaluations composed of synthetic request traces demonstrate consistent improvements when the caching experts' relative performance varies over time.

In recent years, the novel framing of the caching problem as an Online Convex Optimisation (OCO) problem has led to the introduction of several online caching policies. These policies are proven optimal with regard to regret for any arbitrary request pattern, including that of adversarial origin. Nevertheless, their performance is primarily affected by the tuning of their so-called hyperparameters (e.g. learning rate), which is often done under the presumption of adversarial conditions. Consequently, as not all request patterns encountered in practice are adversarial, this suggests potential for further improvements. Unfortunately, the tuning of these hyperparameters to achieve optimal performance across non-adversarial request sequences remains dubious and poses a new challenge. This paper proposes an online meta-learner that combines several instances of the OGA caching policy, each distinct in their learning rate, framing the problem as an expert advice decision problem. The proposed meta-learner dynamically shifts between caching experts and achieves a regret upper bound similar to that of the best-performing caching expert. The penalty introduced for learning the best expert is limited to a logarithmic dependence on the number of experts, which improves upon previous works. Numerical evaluations composed of synthetic request traces demonstrate consistent improvements when the caching experts' relative performance varies over time.

Distributed Reflection Denial-of-Service (DRDoS) attacks remain among the most damaging cyber threats, leveraging vulnerable UDP-based protocols to amplify traffic and overwhelm targets. Our measurement study investigates the amplification potential of three commonly exploited protocols: DNS, NTP, and Memcached, within the context of the network infrastructure in Belgium and Luxembourg. By analysing amplification factors through various query strategies, we aim to identify potential vulnerabilities and correlations between factors that influence the weaponisation of these protocols. We also investigated application-layer looping vulnerabilities, also known as “Loopy”. Our findings indicate that despite protocol hardening, significant risks remain, particularly with improperly configured DNS servers and not updated NTP and Memcached versions.