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Anomaly-based attack detection methods that rely on learning the benign profile of operation are commonly used for identifying data falsification attacks and faults in cyber-physical systems. However, most works do not assume the presence of attacks while training the anomaly detectors- and their impact on eventual anomaly detection performance during the test set. Some robust learning methods overcompensate mitigation which leads to increased false positives in the absence of attacks/threats during training. To achieve this balance, this paper proposes a framework to enhance the robustness of previous anomaly detection frameworks in smart living applications, by introducing three profound design changes for threshold learning of time series anomaly detectors:(1) Tukey bi-weight loss function instead of square loss function (2) adding quantile weights to regression errors of Tukey (3) modifying the definition of empirical cost function from MSE to the harmonic mean of quantile weighted Tukey losses. We show that these changes mitigate performance degradation in anomaly detectors caused by untargeted poisoning attacks during training- while is simultaneously able to prevent false alarms in the absence of such training set attacks. We evaluate our work using a proof of concept that uses state-of-the-art anomaly detection in smart living CPS that detects false data injection in smart metering. 

Modern smart vehicles have a Controller Area Network (CAN) that supports intra-vehicle communication between intelligent Electronic Control Units (ECUs). The CAN is known to be vulnerable to various cyber attacks. In this paper, we propose a unified framework that can detect multiple types of cyber attacks (viz., Denial of Service, Fuzzy, Impersonation) affecting the CAN. Specifically, we construct a feature by observing the timing information of CAN packets exchanged over the CAN bus network over partitioned time windows to construct a low dimensional representation of the entire CAN network as a time series latent space. Then, we apply a two tier anomaly based intrusion detection model that keeps track of short term and long term memory of deviations in the initial time series latent space, to create a 'stateful latent space'. Then, we learn the boundaries of the benign stateful latent space that specify the attack detection criterion. To find hyper-parameters of our proposed model, we formulate a preference based multi-objective optimization problem that optimizes security objectives tailored for a network-wide time series anomaly based intrusion detector by balancing trade-offs between false alarm count, time to detection, and missed detection rate. We use real benign and attack datasets collected from a Kia Soul vehicle to validate our framework and show how our performance outperforms existing works. 

Residential smart water meters (SWMs) collect real-time water consumption data, enabling automated billing and peak period forecasting. The presence of unsafe events is typically detected via deviations from the benign profile of water usage. However, profiling the benign behavior is non-trivial for large-scale SWM networks because once deployed, the collected data already contain those events, biasing the benign profile. To address this challenge, we propose a real-time data-driven unsafe event detection framework for city-scale SWM networks that automatically learns the profile of benign behavior of water usage. Specifically, we first propose an optimal clustering of SWMs based on the recognition of residential similarity water usage to divide the SWM network infrastructure into clusters. Then we propose a mathematical invariant based on the absolute difference between two generalized means – one with positive and the other with negative order. Next, we propose a robust threshold learning approach utilizing a modified Hampel loss function that learns the robust detection thresholds even in the presence of unsafe events. Finally, we validated our proposed framework using a dataset of 1,099 SWMs over 2.5 years. Results show that our model detects unsafe events in the test set, even while learning from the training data with unlabeled unsafe events. 

In this paper, we propose a lightweight explainable machine learning approach that is device and attack-type agnostic and can detect IoT devices that are victims of low-intensity direct and reflective volumetric DDoS attacks launched in an ON-OFF manner. Specifically, our approach is based on a parameterized bio-inspired information-theoretic model that can capture small and subtle volumetric differences between attack versus benign byte volumes exchanged between IoT devices and the rest of the internet. Our approach has four main phases: (1) Feature Engineering involving a simple compression to achieve a universally reduced feature space for volumetric attacks; (2) Model Parameterization: identify appropriate parameters of a bio-inspired information-theoretic model and their appropriate pruned search spaces. (3) Parameter Learning: take a supervised approach for learning the optimal parameters of the explainable model using a local search. (4) Testing: We apply the learned parameters in the test set. For validation, we use real datasets from 4 different types of IoT devices containing seven different kinds of attacks and varying DDoS attack volumes. Furthermore, we employ strategies to counter the inherent biases in attacked datasets to ensure unbiased evaluation. 

This article proposes a unified threat landscape for Participatory Crowd Sensing (P-CS) systems. Specifically, it focuses on attacks from organized malicious actors that may use the knowledge of P-CS platform's operations and exploit algorithmic weaknesses in AI-based methods of event trust, user reputation, decision-making or recommendation models deployed to preserve information integrity in P-CS. We emphasize on intent driven malicious behaviors by advanced adversaries and how attacks are crafted to achieve those attack impacts. Three directions of the threat model are introduced, such as attack goals, types, and strategies. We expand on how various strategies are linked with different attack types and goals, underscoring formal definition, their relevance and impact on the P-CS platform. 

Smart water metering (SWM) infrastructure collects real-time water usage data that is useful for automated billing, leak detection, and forecasting of peak periods. Cyber/physical attacks can lead to data falsification on water usage data. This paper proposes a learning approach that converts smart water meter data into a Pythagorean mean-based invariant that is highly stable under normal conditions but deviates under attacks. We show how adversaries can launch deductive or camouflage attacks in the SWM infrastructure to gain benefits and impact the water distribution utility. Then, we apply a two-tier approach of stateless and stateful detection, reducing false alarms without significantly sacrificing the attack detection rate. We validate our approach using real-world water usage data of 92 households in Alicante, Spain for varying attack scales and strengths and prove that our method limits the impact of undetected attacks and expected time between consecutive false alarms. Our results show that even for low-strength, low-scale deductive attacks, the model limits the impact of an undetected attack to only 0.2199375 pounds and for high-strength, low-scale camouflage attack, the impact of an undetected attack was limited to 1.434375 pounds. 

Route Planning Systems (RPS) are a core component of autonomous personal transport systems essential for safe and efficient navigation of dynamic urban environments with the support of edge-based smart city infrastructure, but they also raise concerns about user route privacy in the context of both privately-owned and commercial vehicles. Numerous high profile data breaches in recent years have fortunately motivated research on privacy-preserving RPS, but most of them are rendered impractical by greatly increased communication and processing overhead. We address this by proposing an approach called Hierarchical Privacy-Preserving Route Planning (HPRoP) which divides and distributes the route planning task across multiple levels, and protects locations along the entire route. This is done by combining Inertial Flow partitioning, Private Information Retrieval (PIR), and Edge Computing techniques with our novel route planning heuristic algorithm. Normalized metrics were also formulated to quantify the privacy of the source/destination points (endpoint location privacy) and the route itself (route privacy). Evaluation on a simulated road network showed that HPRoP reliably produces routes differing only by ≤20% in length from optimal shortest paths, with completion times within ∼ 25 seconds which is reasonable for a PIR-based approach. On top of this, more than half of the produced routes achieved near-optimal endpoint location privacy (∼ 1.0) and good route privacy (≥ 0.8). 

Modern smart cities need smart transportation solutions to quickly detect various traffic emergencies and incidents in the city to avoid cascading traffic disruptions. To materialize this, roadside units and ambient transportation sensors are being deployed to collect speed data that enables the monitoring of traffic conditions on each road segment. In this paper, we first propose a scalable data-driven anomaly-based traffic incident detection framework for a city-scale smart transportation system. Specifically, we propose an incremental region growing approximation algorithm for optimal Spatio-temporal clustering of road segments and their data; such that road segments are strategically divided into highly correlated clusters. The highly correlated clusters enable identifying a Pythagorean Mean-based invariant as an anomaly detection metric that is highly stable under no incidents but shows a deviation in the presence of incidents. We learn the bounds of the invariants in a robust manner such that anomaly detection can generalize to unseen events, even when learning from real noisy data. Second, using cluster-level detection, we propose a folded Gaussian classifier to pinpoint the particular segment in a cluster where the incident happened in an automated manner. We perform extensive experimental validation using mobility data collected from four cities in Tennessee, compare with the state-of-the-art ML methods, to prove that our method can detect incidents within each cluster in real-time and outperforms known ML methods.