This paper aims to optimize incident-specific cyber insurance design.
problem Complexity in determining optimal risk retention and transfer.
method Economic foundation for incident-specific cyber insurance with Pareto optimality.
result Illustrates feasibility of designing incident-specific indemnities for both parties.
Enhances cyber risk assessment with entity-specific features.
problem Lack of high-quality public cyber incident data.
method Develops an InsurTech framework to enrich cyber incident data with entity-specific attributes and implements machine learning models.
result InsurTech features improve prediction robustness and provide customized risk profiles.
Study shows data breaches cause significant financial losses for firms, especially in health sector.
problem Understanding the economic impact of cyber incidents on listed firms.
method Event study using abnormal returns over 2012-2022, adjusting for event-induced variance and residual cross-correlation.
result Data breaches cause significant financial losses for firms, especially in health sector.
Paper introduces a framework for managing cyber risk with insurance and cybersecurity models.
problem Pervasive challenges in managing cyber risk, especially for capital allocation.
method Combines insurance frequency-severity models with cybersecurity cascade models for comprehensive cyber risk assessment. Facilitates informed capital allocation through a two-pillar framework.
result Demonstrates the necessity of comprehensive cost-benefit analysis for budget-constrained companies.
CyPhERS provides real-time event info for CPSs, avoiding downtime.
problem Real-time event identification in CPSs is challenging due to complex interdependencies and rare events.
method CyPhERS integrates cyber and physical components, generating event signatures for known and unknown events.
result Event signatures provide relevant and inferable information on both known and unknown event types.
Model quantifies cyber-attacks' impact on firms and insurers.
problem Impact of cyber-attacks on firms' revenues and insurers' portfolios.
method Stochastic SIR model coupled with granular firm growth model.
result Predicts insurer needs to compensate up to two days of revenue in a 100-day incident.
Study examines cyber losses across sectors, finds high severity and frequency.
problem Understanding the nature of cyber losses and their variability across sectors.
method Analysis of a leading industry dataset of cyber events, focusing on frequency and severity.
result Cyber risks are heavy-tailed, with high probability of extreme losses.
The paper examines the feasibility of managing aggregate cyber-risk in IoT environments.
problem Determining sustainable conditions for providing aggregate cyber-risk coverage.
method Developed a rigorous general theory and validated it with real data.
result Conditions for sustainable aggregate cyber-risk management under heavy-tailed distributions.
Preventing organizations from Cyber exploits needs timely intelligence about Cyber vulnerabilities and attacks, referred as threats. Cyber threat intelligence can be extracted from various sources including social media platforms where users publish the threat information in real time. Gathering Cyber threat intelligen…
Research shows filtering reduces predictability of cyber-attacks.
problem Predicting cyber-attacks from incomplete data.
method Combining external data with machine learning algorithms to learn indicators of cyber-attacks.
result The process of filtering reduces the predictability of cyber-attacks.
Study on cyber insurance viability using statistical models.
problem Exploring insurability of cyber risk and its factors.
method Regression models (GAMLSS, ordinal regressions) and utility modelling.
result Provides insights into insurability of cyber risk.
Study quantifies model risk in cyber insurance, affecting premium pricing.
problem Model risk and risk sensitivity in cyber insurance pricing.
method Robust estimators for model parameters and dependence analysis.
result Robust estimation improves tail index and joint loss model accuracy.
We present cyber-security problems of high importance. We show that in order to solve these cyber-security problems, one must cope with certain machine learning challenges. We provide novel data sets representing the problems in order to enable the academic community to investigate the problems and suggest methods to c…
The paper uses a simulator and optimisation to defend against cyber threats.
problem Defending against cyber threats in simulated networks.
method Dynamic causal Bayesian optimisation (DCBO) integrated with a cyber security simulator.
result DCBO optimally reduces the cost of intrusions in simulated networks.
Study finds stocks with higher cyber risk scores outperform others, indicating a market-wide cyber risk premium.
problem Identifying and quantifying firms' cyber risks and their impact on stock performance.
method Machine learning algorithm to analyze disclosures and a dedicated cyber corpus.
result High cyber risk stocks significantly outperform others, indicating a market-wide cyber risk premium.
Paper analyzes cyber risk classifications for forecasting performance.
problem Lack of effective out-of-sample forecasting performance in current cyber risk classifications.
method Rolling window analysis using threshold weighted scoring functions.
result Dynamic and impact-based cyber risk classifiers outperform others in forecasting future cyber risk losses.
A novel model combines deep learning and extreme value theory for multivariate cyber risk prediction.
problem High dimensionality and heavy tails in multivariate cyber risk patterns.
method Combines deep learning for point predictions and extreme value theory for quantile predictions.
result The model provides satisfactory high quantile predictions and accurate point predictions.
Motivated by the developments in cyber risk treatment in the finance industry, we propose a general framework of cyber bond, whose main purpose is to insure (compensate) losses of a cyber attack. Based on a database of publicly available cyber events, we determine cyber loss distribution parameters and use them to nume…
Cyber attacks are growing in frequency and severity. Over the past year alone we have witnessed massive data breaches that stole personal information of millions of people and wide-scale ransomware attacks that paralyzed critical infrastructure of several countries. Combating the rising cyber threat calls for a multi-p…
Develops a Bonus-Malus model for cyber risk insurance to incentivize cybersecurity.
problem Lack of effective insurance strategies to incentivize cybersecurity.
method Proposes a Bonus-Malus model and a mathematical model with a numerical algorithm.
result Demonstrates how a Bonus-Malus system resolves moral hazard and benefits the insurer.
The paper evaluates machine learning cyber defenses using log data against adversarial attacks.
problem Evaluating the robustness of machine learning cyber defenses against adversarial attacks.
method Developed a testing framework using deep reinforcement learning and adversarial natural language processing.
result Higher dropout levels increase robustness, with 90% dropout probability showing the highest robustness.
Crowdsourced data helps detect incidents faster, balancing accuracy and practicality.
problem Detecting incidents from crowdsourced data is challenging due to noise and uncertainty.
method CROME (Crowdsourced Multi-objective Event Detection) uses CNN and Pareto optimization.
result The approach outperforms existing methods in incident detection and practicality.
The paper models and prices cyber insurance risks, distinguishing idiosyncratic, systematic, and systemic risks.
problem Modeling and pricing cyber insurance policies, especially for systemic risks.
method Distinguishes three types of cyber risks and proposes methods for their valuation.
result Complex methods are needed for systemic cyber risks, including risk-neutral valuation and monetary risk measures.
The paper shows supply chain features improve cyber risk prediction.
problem Predicting cyber risk from supply chain attributes.
method Machine learning, external supply chain features, AUC improvement.
result Supply chain network features improve AUC by 2.3%.
Study uses vehicle trajectory data to predict traffic incidents on highways.
problem Early detection of traffic incidents to reduce secondary crashes.
method Machine learning algorithms (Logistic Regression, Random Forest, Extreme Gradient Boost, Artificial Neural Network) applied to vehicle trajectory data.
result Random Forest model performs best for incident prediction.
Introduces an artificial cyber lab to test and identify cyber resilience measures.
problem Systemic cyber risks and their control methods.
method Classical contagion models and artificial cyber lab simulations.
result Identified two classes of measures: security- and topology-based interventions.
Study finds high cyber risk stocks generate significant excess returns.
problem Understanding and quantifying cyber risk's impact on stock returns.
method Machine learning algorithm measuring cyber risk proximity to a corpus.
result High cyber risk stocks generate an excess return of 18.72% p.a.
Sparse incidence tensors can represent a variety of structured data. For example, we may represent attributed graphs using their node-node, node-edge, or edge-edge incidence matrices. In higher dimensions, incidence tensors can represent simplicial complexes and polytopes. In this paper, we formalize incidence tensors,…
New approach detects cyber-attacks in real-time.
problem Real-time detection of cyber-attacks for effective mitigation.
method Aggregates unsupervised anomaly detection algorithms and incorporates delayed feedback.
result Improves anomaly detection performance through theoretical guarantees.
The scale of Internet-connected systems has increased considerably, and these systems are being exposed to cyber attacks more than ever. The complexity and dynamics of cyber attacks require protecting mechanisms to be responsive, adaptive, and scalable. Machine learning, or more specifically deep reinforcement learning…
Critical incident stages identification and reasonable prediction of traffic incident duration are essential in traffic incident management. In this paper, we propose a traffic incident duration prediction model that simultaneously predicts the impact of the traffic incidents and identifies the critical groups of tempo…
Recreating cyber-attack alert data with a high level of fidelity is challenging due to the intricate interaction between features, non-homogeneity of alerts, and potential for rare yet critical samples. Generative Adversarial Networks (GANs) have been shown to effectively learn complex data distributions with the inten…
Recent changes to greenhouse gas emission policies are catalyzing the electric vehicle (EV) market making it readily accessible to consumers. While there are challenges that arise with dense deployment of EVs, one of the major future concerns is cyber security threat. In this paper, cyber security threats in the form o…
Predicting traffic incident duration is a major challenge for many traffic centres around the world. Most research studies focus on predicting the incident duration on motorways rather than arterial roads, due to a high network complexity and lack of data. In this paper we propose a bi-level framework for predicting th…
Paper models cloud outages for cyber insurance stress-testing.
problem Cyber insurance portfolios' vulnerability to simultaneous cloud outages.
method Modeling and calibrating cloud-outage scenarios, measuring diversification.
result Cloud-outage diversification can protect against accumulation risk.
This research develops a new model for cyber risk and insurance pricing.
problem Accurate calculation of aggregate losses in cyber insurance pricing.
method A path-based k-generation risk contagion model in a tree-shaped network structure.
result Explicit expressions for mean and variance of local loss on a single path.
Crimes emerge out of complex interactions of human behaviors and situations. Linkages between crime incidents are highly complex. Detecting crime linkage given a set of incidents is a highly challenging task since we only have limited information, including text descriptions, incident times, and locations. In practice,…
This paper discusses adversarial attacks on cyber security systems using machine learning.
problem Adversarial attacks limit the use of machine learning in cyber security.
method Characterizes adversarial attack methods and their applications in cyber security.
result Highlights unique challenges and future research directions for adversarial attacks in cyber security.
CyBeR-0 optimizes federated learning with Byzantine resilience and reduced communication costs.
problem Byzantine attacks and communication inefficiency in federated learning.
method Transformed robust aggregation for zero-order optimization under client heterogeneity.
result CyBeR-0 achieves stable performance with minimal communication costs and reduced memory usage.
Paper tackles cybersecurity attack detection with an ensemble approach.
problem Challenges in multi-class classification for cyber security breaches.
method Designing a multi-node multi-class classification ensemble approach.
result Proposed approach outperforms full-data approach in multi-node data-censoring cases.
Automated suggestions help train technicians diagnose incidents faster.
problem Manual and time-consuming incident diagnosis by train maintenance technicians.
method Developed and deployed a learning machine to suggest diagnostics to technicians.
result The model refines its accuracy through feedback from experts and uses feature engineering.
This paper proposes a real-time signal plan recommendation system for traffic incidents.
problem Limited effectiveness of traffic incident management due to late response and workload.
method Decomposes recommendation task into real-time traffic prediction and plan association, learning from historical data and metric learning.
result Precision score of 96.75% and recall of 87.5% on testing plan, with 22.5 minutes lead time ahead of Waze alerts.
Study analyzes data breach reporting patterns and frequency across U.S. states, finding increasing trends after 2020.
problem Contradictory conclusions in data breach frequency trends due to inconsistent data collection and reporting standards.
method Joint analysis of state Attorneys General's publications on data breaches across eight states with established notification laws.
result Frequency of data breaches is increasing after 2020, with commonalities and heterogeneities across states.
Framework for managing cyber risks in networks.
problem Managing systemic cyber risks in digital networks.
method Three components: acceptable configurations, risk mitigation interventions, and cost function.
result Effective decision-making for network resilience.
Accurate traffic speed prediction is an important and challenging topic for transportation planning. Previous studies on traffic speed prediction predominately used spatio-temporal and context features for prediction. However, they have not made good use of the impact of urban traffic incidents. In this work, we aim to…
Machine learning (ML) started to become widely deployed in cyber security settings for shortening the detection cycle of cyber attacks. To date, most ML-based systems are either proprietary or make specific choices of feature representations and machine learning models. The success of these techniques is difficult to a…
This paper detects anomalies in cellular network traffic using hybrid methods.
problem Detecting anomalies in network traffic for security and analysis.
method Hybrid method combining GARCH, K-means, and Neural Network.
result Anomaly detection in cellular network traffic successfully achieved.
Paper introduces a new model for cyber insurance pricing.
problem Inaccurate pricing of cyber insurance due to multiple, contagious losses.
method Developed a bivariate compound dynamic contagion process.
result Analytical expressions for the compound process and its moments.