Assessing the Value of Network Security Technologies

Proper configuration of security technologies is critical to balance the access and protection requirements of information. The common practice of using a layered security architecture that has multiple technologies amplifies the need for proper configuration because the configuration decision about one security technology has ramifications for the configuration decisions about others. We study the impact of configuration on the value obtained from a firewall and an Intrusion Detection System (IDS). We also study how a firewall and an IDS interact with each other in terms of value contribution. We show that the firm may be worse off when it deploys a technology if the technology (either the firewall or the IDS) is improperly configured. A more serious consequence for the firm is that even if each of these (improperly configured) technologies offers a positive value when deployed alone, deploying both may be detrimental to the firm. Configuring the IDS and the firewall optimally eliminates the conflict between them, resulting in a non-negative value to the firm. When optimally configured, we find that these technologies may complement or substitute each other. Further, we find that while the optimal configuration of an IDS is the same whether it is deployed alone or together with a firewall, the optimal configuration of a firewall has a lower detection rate (i.e., allow more access) when it is deployed with an IDS than when deployed alone. Our results highlight the complex interactions between firewall and IDS technologies when they are used together in a security architecture, and, hence, the need for proper configuration in order to benefit from these technologies

A Structured Approach to Effective Access Control List Tuning

Access control lists (ACLs) are rule sets that govern the passing of data packets through network devices such as routers and firewalls. In order to maximize data throughput and minimize security risks, they must be adjusted. The tuning process involves the reconciliation of changed access requirements with the existing rule set, identification of vulnerabilities or performance-degrading rules, and implementation of changes. Informal approaches to this complex task often involve multitasking, a strategy that leads to an increased rate of misconfiguration. To mitigate the impact of perceived task complexity, this research proposes a structured approach to the ACL refinement process. The formalized approach is meant to reduce cognitive overload among information security analysts by sequentially ordering the steps through which an access control list is modified. This work-in-progress also describes an experiment for evaluating the artifact. If supported, it will help IT professionals better secure their infrastructure

Assessing the Value of Network Security Technologies

Proper configuration of security technologies is critical to balance the access and protection requirements of information. The common practice of using a layered security architecture that has multiple technologies amplifies the need for proper configuration because the configuration decision about one security technology has ramifications for the configuration decisions about others. We study the impact of configuration on the value obtained from a firewall and an Intrusion Detection System (IDS). We also study how a firewall and an IDS interact with each other in terms of value contribution. We show that the firm may be worse off when it deploys a technology if the technology (either the firewall or the IDS) is improperly configured. A more serious consequence for the firm is that even if each of these (improperly configured) technologies offers a positive value when deployed alone, deploying both may be detrimental to the firm. Configuring the IDS and the firewall optimally eliminates the conflict between them, resulting in a non-negative value to the firm. When optimally configured, we find that these technologies may complement or substitute each other. Further, we find that while the optimal configuration of an IDS is the same whether it is deployed alone or together with a firewall, the optimal configuration of a firewall has a lower detection rate (i.e., allow more access) when it is deployed with an IDS than when deployed alone. Our results highlight the complex interactions between firewall and IDS technologies when they are used together in a security architecture, and, hence, the need for proper configuration in order to benefit from these technologies

Cyber Defense Remediation in Energy Delivery Systems

The integration of Information Technology (IT) and Operational Technology (OT) in Cyber-Physical Systems (CPS) has resulted in increased efficiency and facilitated real-time information acquisition, processing, and decision making. However, the increase in automation technology and the use of the internet for connecting, remote controlling, and supervising systems and facilities has also increased the likelihood of cybersecurity threats that can impact safety of humans and property. There is a need to assess cybersecurity risks in the power grid, nuclear plants, chemical factories, etc. to gain insight into the likelihood of safety hazards. Quantitative cybersecurity risk assessment will lead to informed cyber defense remediation and will ensure the presence of a mitigation plan to prevent safety hazards. In this dissertation, using Energy Delivery Systems (EDS) as a use case to contextualize a CPS, we address key research challenges in managing cyber risk for cyber defense remediation. First, we developed a platform for modeling and analyzing the effect of cyber threats and random system faults on EDS\u27s safety that could lead to catastrophic damages. We developed a data-driven attack graph and fault graph-based model to characterize the exploitability and impact of threats in EDS. We created an operational impact assessment to quantify the damages. Finally, we developed a strategic response decision capability that presents optimal mitigation actions and policies that balance the tradeoff between operational resilience (tactical risk) and strategic risk. Next, we addressed the challenge of management of tactical risk based on a prioritized cyber defense remediation plan. A prioritized cyber defense remediation plan is critical for effective risk management in EDS. Due to EDS\u27s complexity in terms of the heterogeneous nature of blending IT and OT and Industrial Control System (ICS), scale, and critical processes tasks, prioritized remediation should be applied gradually to protect critical assets. We proposed a methodology for prioritizing cyber risk remediation plans by detecting and evaluating critical EDS nodes\u27 paths. We conducted evaluation of critical nodes characteristics based on nodes\u27 architectural positions, measure of centrality based on nodes\u27 connectivity and frequency of network traffic, as well as the controlled amount of electrical power. The model also examines the relationship between cost models of budget allocation for removing vulnerabilities on critical nodes and their impact on gradual readiness. The proposed cost models were empirically validated in an existing network ICS test-bed computing nodes criticality. Two cost models were examined, and although varied, we concluded the lack of correlation between types of cost models to most damageable attack path and critical nodes readiness. Finally, we proposed a time-varying dynamical model for the cyber defense remediation in EDS. We utilize the stochastic evolutionary game model to simulate the dynamic adversary of cyber-attack-defense. We leveraged the Logit Quantal Response Dynamics (LQRD) model to quantify real-world players\u27 cognitive differences. We proposed the optimal decision making approach by calculating the stable evolutionary equilibrium and balancing defense costs and benefits. Case studies on EDS indicate that the proposed method can help the defender predict possible attack action, select the related optimal defense strategy over time, and gain the maximum defense payoffs. We also leveraged software-defined networking (SDN) in EDS for dynamical cyber defense remediation. We presented an approach to aid the selection security controls dynamically in an SDN-enabled EDS and achieve tradeoffs between providing security and Quality of Service (QoS). We modeled the security costs based on end-to-end packet delay and throughput. We proposed a non-dominated sorting based multi-objective optimization framework which can be implemented within an SDN controller to address the joint problem of optimizing between security and QoS parameters by alleviating time complexity at O(MN2). The M is the number of objective functions, and N is the population for each generation, respectively. We presented simulation results that illustrate how data availability and data integrity can be achieved while maintaining QoS constraints

Facilitating Data Driven Research Through a Hardware- and Software-Based Cyberinfrastructure Architecture

Cyberinfrastructure is the backbone of research and modern industry. As such, to have an environment conducive to research advancements, cyberinfrastructure must be well maintained and accessible by all researchers. Presented in this thesis is a method of centralizing aspects of cyberinfrastructure to allow for ease of collaboration and data management by researchers without requiring these researchers to manage the involved systems themselves. This centralized architecture includes dedicated machines for data transfers, a cluster designed to run microservices surrounding the method, a dashboard for performance and health monitoring, and network telemetry collection. As system administrators are responsible for maintaining the systems in place, a user study was conducted to assess the functionality of the dashboard they would utilize to receive alerts from and utilize to quickly gauge the status of involved hardware. This thesis aims to provide a template for deploying centralized data transfer cyberinfrastructure and a manual for utilizing these systems to support data driven research

Methods and Techniques for Dynamic Deployability of Software-Defined Security Services

With the recent trend of “network softwarisation”, enabled by emerging technologies such as Software-Defined Networking and Network Function Virtualisation, system administrators of data centres and enterprise networks have started replacing dedicated hardware-based middleboxes with virtualised network functions running on servers and end hosts. This radical change has facilitated the provisioning of advanced and flexible network services, ultimately helping system administrators and network operators to cope with the rapid changes in service requirements and networking workloads. This thesis investigates the challenges of provisioning network security services in “softwarised” networks, where the security of residential and business users can be provided by means of sets of software-based network functions running on high performance servers or on commodity devices. The study is approached from the perspective of the telecom operator, whose goal is to protect the customers from network threats and, at the same time, maximize the number of provisioned services, and thereby revenue. Specifically, the overall aim of the research presented in this thesis is proposing novel techniques for optimising the resource usage of software-based security services, hence for increasing the chances for the operator to accommodate more service requests while respecting the desired level of network security of its customers. In this direction, the contributions of this thesis are the following: (i) a solution for the dynamic provisioning of security services that minimises the utilisation of computing and network resources, and (ii) novel methods based on Deep Learning and Linux kernel technologies for reducing the CPU usage of software-based security network functions, with specific focus on the defence against Distributed Denial of Service (DDoS) attacks. The experimental results reported in this thesis demonstrate that the proposed solutions for service provisioning and DDoS defence require fewer computing resources, compared to similar approaches available in the scientific literature or adopted in production networks

Big data analytics: a predictive analysis applied to cybersecurity in a financial organization

Project Work presented as partial requirement for obtaining the Master’s degree in Information Management, with a specialization in Knowledge Management and Business IntelligenceWith the generalization of the internet access, cyber attacks have registered an alarming growth in frequency and severity of damages, along with the awareness of organizations with heavy investments in cybersecurity, such as in the financial sector. This work is focused on an organization’s financial service that operates on the international markets in the payment systems industry. The objective was to develop a predictive framework solution responsible for threat detection to support the security team to open investigations on intrusive server requests, over the exponentially growing log events collected by the SIEM from the Apache Web Servers for the financial service. A Big Data framework, using Hadoop and Spark, was developed to perform classification tasks over the financial service requests, using Neural Networks, Logistic Regression, SVM, and Random Forests algorithms, while handling the training of the imbalance dataset through BEV. The main conclusions over the analysis conducted, registered the best scoring performances for the Random Forests classifier using all the preprocessed features available. Using the all the available worker nodes with a balanced configuration of the Spark executors, the most performant elapsed times for loading and preprocessing of the data were achieved using the column-oriented ORC with native format, while the row-oriented CSV format performed the best for the training of the classifiers.Com a generalização do acesso à internet, os ciberataques registaram um crescimento alarmante em frequência e severidade de danos causados, a par da consciencialização das organizações, com elevados investimentos em cibersegurança, como no setor financeiro. Este trabalho focou-se no serviço financeiro de uma organização que opera nos mercados internacionais da indústria de sistemas de pagamento. O objetivo consistiu no desenvolvimento uma solução preditiva responsável pela detecção de ameaças, por forma a dar suporte à equipa de segurança na abertura de investigações sobre pedidos intrusivos no servidor, relativamente aos exponencialmente crescentes eventos de log coletados pelo SIEM, referentes aos Apache Web Servers, para o serviço financeiro. Uma solução de Big Data, usando Hadoop e Spark, foi desenvolvida com o objectivo de executar tarefas de classificação sobre os pedidos do serviço financeiros, usando os algoritmos Neural Networks, Logistic Regression, SVM e Random Forests, solucionando os problemas associados ao treino de um dataset desequilibrado através de BEV. As principais conclusões sobre as análises realizadas registaram os melhores resultados de classificação usando o algoritmo Random Forests com todas as variáveis pré-processadas disponíveis. Usando todos os nós do cluster e uma configuração balanceada dos executores do Spark, os melhores tempos para carregar e pré-processar os dados foram obtidos usando o formato colunar ORC nativo, enquanto o formato CSV, orientado a linhas, apresentou os melhores tempos para o treino dos classificadores