Jonathan Chase | Singapore Management University (original) (raw)

Papers by Jonathan Chase

IEEE Transactions on Dependable and Secure Computing, 2017

As computing services are increasingly cloud-based, corporations are investing in cloud-based sec... more As computing services are increasingly cloud-based, corporations are investing in cloud-based security measures. The Security-asa-Service (SECaaS) paradigm allows customers to outsource security to the cloud, through the payment of a subscription fee. However, no security system is bulletproof, and even one successful attack can result in the loss of data and revenue worth millions of dollars. To guard against this eventuality, customers may also purchase cyber insurance to receive recompense in the case of loss. To achieve cost effectiveness, it is necessary to balance provisioning of security and insurance, even when future costs and risks are uncertain. To this end, we introduce a stochastic optimization model to optimally provision security and insurance services in the cloud. Since the model we design is a mixed integer problem, we also introduce a partial Lagrange multiplier algorithm that takes advantage of the total unimodularity property to find the solution in polynomial time. We also apply sensitivity analysis to find the exact tolerance of decision variables to parameter changes. We show the effectiveness of these techniques using numerical results based on real attack data to demonstrate a realistic testing environment, and find that security and insurance are interdependent.

Proceedings of the International Conference on Automated Planning and Scheduling, Jun 13, 2022

In the quest to achieve better response times in dense urban environments, law enforcement agenci... more In the quest to achieve better response times in dense urban environments, law enforcement agencies are seeking AIdriven planning systems to inform their patrol strategies. In this paper, we present a framework, OFFICERS, for deployment planning that learns from historical data to generate deployment schedules on a daily basis. We accurately predict incidents using ST-ResNet, a deep learning technique that captures wide-ranging spatio-temporal dependencies, and solve a large-scale optimization problem to schedule deployment, significantly improving its scalability through a simulated annealing solver. Methodologically, our approach outperforms our previous works where prediction was done using Generative Adversarial Networks, and optimization was performed with the CPLEX solver. Furthermore, we show that our proposed framework is designed to be readily transferable between use cases, handling a wide range of both criminal and non-criminal incidents, with the use of deep learning and a general-purpose efficient solver, reducing dependence on context-specific details. We demonstrate the value of our approach on a police patrol case study, and discuss both the ethical considerations, and operational requirements, for deployment of a lightweight and responsive planning system.

Proceedings of the International Conference on Automated Planning and Scheduling

Law enforcement agencies in dense urban environments, faced with a wide range of incidents to han... more Law enforcement agencies in dense urban environments, faced with a wide range of incidents to handle and limited manpower, are turning to data-driven AI to inform their policing strategy. In this paper we present a patrol scheduling system called GRAND-VISION: Ground Response Allocation and Deployment - Visualization, Simulation, and Optimization. The system employs deep learning to generate incident sets that are used to train a patrol schedule that can accommodate varying manpower, break times, manual pre-allocations, and a variety of spatio-temporal demand features. The complexity of the scenario results in a system with real world applicability, which we demonstrate through simulation on historical data obtained from a large urban law enforcement agency.

Urban law enforcement agencies are under great pressure to respond to emergency incidents effecti... more Urban law enforcement agencies are under great pressure to respond to emergency incidents effectively while operating within restricted budgets. Minutes saved on emergency response times can save lives and catch criminals, and a responsive police force can deter crime and bring peace of mind to citizens. To efficiently minimize the response times of a law enforcement agency operating in a dense urban environment with limited manpower, we consider in this paper the problem of optimizing the spatial and temporal deployment of law enforcement agents to predefined patrol regions in a real-world scenario informed by machine learning. To this end, we develop a mixed integer linear optimization formulation (MIP) to minimize the risk of failing response time targets. Given the stochasticity of the environment in terms of incident numbers, location, timing, and duration, we use Sample Average Approximation (SAA) to find a robust deployment plan. To overcome the sparsity of real data, samples...

2015 IEEE Global Communications Conference (GLOBECOM), 2014

The increasing popularity of using mobile devices in a work context, has led to the need to be ab... more The increasing popularity of using mobile devices in a work context, has led to the need to be able to support more powerful computation. Users no longer remain in an office or at home to conduct their activities, preferring libraries and cafes. In this paper, we consider a mobile cloud computing scenario in which users bring their own mobile devices and are offered a variety of equipment, e.g., desktop computer, smart-TV, or projector, to migrate their applications to, so as to save battery life, improve usability and performance. We formulate a stochastic optimization problem to optimize the allocation of user applications to equipment despite future uncertainties. Furthermore, we extend the scenario to consider multiple locations and geographic migration. The performance evaluation shows the superior benefit, e.g., higher profit, compared with a baseline algorithm.

IEEE Transactions on Services Computing, 2015

2014 IEEE International Conference on Communications (ICC), 2014

ABSTRACT Cloud computing provides users with great flexibility when provisioning resources, with ... more ABSTRACT Cloud computing provides users with great flexibility when provisioning resources, with cloud providers offering a choice of reservation and on-demand purchasing options. Reservation plans offer cheaper prices, but must be chosen in advance, and therefore must be appropriate to users' requirements. If demand is uncertain, the reservation plan may not be sufficient and on-demand resources have to be provisioned. Previous work focused on optimally placing virtual machines with cloud providers to minimize total cost. However, many applications require large amounts of network bandwidth. Therefore, considering only virtual machines offers an incomplete view of the system. Exploiting recent developments in software defined networking (SDN), we propose a unified approach that integrates virtual machine and network bandwidth provisioning. We solve a stochastic integer programming problem to obtain an optimal provisioning of both virtual machines and network bandwidth, when demand is uncertain. Numerical results clearly show that our proposed solution minimizes users' costs and provides superior performance to alternative methods. We believe that this integrated approach is the way forward for cloud computing to support network intensive applications.

IEEE Transactions on Dependable and Secure Computing, 2017

As computing services are increasingly cloud-based, corporations are investing in cloud-based sec... more As computing services are increasingly cloud-based, corporations are investing in cloud-based security measures. The Security-asa-Service (SECaaS) paradigm allows customers to outsource security to the cloud, through the payment of a subscription fee. However, no security system is bulletproof, and even one successful attack can result in the loss of data and revenue worth millions of dollars. To guard against this eventuality, customers may also purchase cyber insurance to receive recompense in the case of loss. To achieve cost effectiveness, it is necessary to balance provisioning of security and insurance, even when future costs and risks are uncertain. To this end, we introduce a stochastic optimization model to optimally provision security and insurance services in the cloud. Since the model we design is a mixed integer problem, we also introduce a partial Lagrange multiplier algorithm that takes advantage of the total unimodularity property to find the solution in polynomial time. We also apply sensitivity analysis to find the exact tolerance of decision variables to parameter changes. We show the effectiveness of these techniques using numerical results based on real attack data to demonstrate a realistic testing environment, and find that security and insurance are interdependent.

Proceedings of the International Conference on Automated Planning and Scheduling, Jun 13, 2022

In the quest to achieve better response times in dense urban environments, law enforcement agenci... more In the quest to achieve better response times in dense urban environments, law enforcement agencies are seeking AIdriven planning systems to inform their patrol strategies. In this paper, we present a framework, OFFICERS, for deployment planning that learns from historical data to generate deployment schedules on a daily basis. We accurately predict incidents using ST-ResNet, a deep learning technique that captures wide-ranging spatio-temporal dependencies, and solve a large-scale optimization problem to schedule deployment, significantly improving its scalability through a simulated annealing solver. Methodologically, our approach outperforms our previous works where prediction was done using Generative Adversarial Networks, and optimization was performed with the CPLEX solver. Furthermore, we show that our proposed framework is designed to be readily transferable between use cases, handling a wide range of both criminal and non-criminal incidents, with the use of deep learning and a general-purpose efficient solver, reducing dependence on context-specific details. We demonstrate the value of our approach on a police patrol case study, and discuss both the ethical considerations, and operational requirements, for deployment of a lightweight and responsive planning system.

Proceedings of the International Conference on Automated Planning and Scheduling

Law enforcement agencies in dense urban environments, faced with a wide range of incidents to han... more Law enforcement agencies in dense urban environments, faced with a wide range of incidents to handle and limited manpower, are turning to data-driven AI to inform their policing strategy. In this paper we present a patrol scheduling system called GRAND-VISION: Ground Response Allocation and Deployment - Visualization, Simulation, and Optimization. The system employs deep learning to generate incident sets that are used to train a patrol schedule that can accommodate varying manpower, break times, manual pre-allocations, and a variety of spatio-temporal demand features. The complexity of the scenario results in a system with real world applicability, which we demonstrate through simulation on historical data obtained from a large urban law enforcement agency.

Urban law enforcement agencies are under great pressure to respond to emergency incidents effecti... more Urban law enforcement agencies are under great pressure to respond to emergency incidents effectively while operating within restricted budgets. Minutes saved on emergency response times can save lives and catch criminals, and a responsive police force can deter crime and bring peace of mind to citizens. To efficiently minimize the response times of a law enforcement agency operating in a dense urban environment with limited manpower, we consider in this paper the problem of optimizing the spatial and temporal deployment of law enforcement agents to predefined patrol regions in a real-world scenario informed by machine learning. To this end, we develop a mixed integer linear optimization formulation (MIP) to minimize the risk of failing response time targets. Given the stochasticity of the environment in terms of incident numbers, location, timing, and duration, we use Sample Average Approximation (SAA) to find a robust deployment plan. To overcome the sparsity of real data, samples...

2015 IEEE Global Communications Conference (GLOBECOM), 2014

The increasing popularity of using mobile devices in a work context, has led to the need to be ab... more The increasing popularity of using mobile devices in a work context, has led to the need to be able to support more powerful computation. Users no longer remain in an office or at home to conduct their activities, preferring libraries and cafes. In this paper, we consider a mobile cloud computing scenario in which users bring their own mobile devices and are offered a variety of equipment, e.g., desktop computer, smart-TV, or projector, to migrate their applications to, so as to save battery life, improve usability and performance. We formulate a stochastic optimization problem to optimize the allocation of user applications to equipment despite future uncertainties. Furthermore, we extend the scenario to consider multiple locations and geographic migration. The performance evaluation shows the superior benefit, e.g., higher profit, compared with a baseline algorithm.

IEEE Transactions on Services Computing, 2015

2014 IEEE International Conference on Communications (ICC), 2014

ABSTRACT Cloud computing provides users with great flexibility when provisioning resources, with ... more ABSTRACT Cloud computing provides users with great flexibility when provisioning resources, with cloud providers offering a choice of reservation and on-demand purchasing options. Reservation plans offer cheaper prices, but must be chosen in advance, and therefore must be appropriate to users' requirements. If demand is uncertain, the reservation plan may not be sufficient and on-demand resources have to be provisioned. Previous work focused on optimally placing virtual machines with cloud providers to minimize total cost. However, many applications require large amounts of network bandwidth. Therefore, considering only virtual machines offers an incomplete view of the system. Exploiting recent developments in software defined networking (SDN), we propose a unified approach that integrates virtual machine and network bandwidth provisioning. We solve a stochastic integer programming problem to obtain an optimal provisioning of both virtual machines and network bandwidth, when demand is uncertain. Numerical results clearly show that our proposed solution minimizes users' costs and provides superior performance to alternative methods. We believe that this integrated approach is the way forward for cloud computing to support network intensive applications.