Modelling and Analysis of Network Security Policies

Nowadays, computers and network communications have a pervasive presence in all our daily activities. Their correct configuration in terms of security is becoming more and more complex due to the growing number and variety of services present in a network. Generally, the security configuration of a computer network is dictated by specifying the policies of the security controls (e.g. firewall, VPN gateway) in the network. This implies that the specification of the network security policies is a crucial step to avoid errors in network configuration (e.g., blocking legitimate traffic, permitting unwanted traffic or sending insecure data). In the literature, an anomaly is an incorrect policy specification that an administrator may introduce in the network. In this thesis, we indicate as policy anomaly any conflict (e.g. two triggered policy rules enforcing contradictory actions), error (e.g. a policy cannot be enforced because it requires a cryptographic algorithm not supported by the security controls) or sub-optimization (e.g. redundant policies) that may arise in the policy specification phase. Security administrators, thus, have to face the hard job of correctly specifying the policies, which requires a high level of competence. Several studies have confirmed, in fact, that many security breaches and breakdowns are attributable to administrators’ responsibilities. Several approaches have been proposed to analyze the presence of anomalies among policy rules, in order to enforce a correct security configuration. However, we have identified two limitations of such approaches. On one hand, current literature identifies only the anomalies among policies of a single security technology (i.e., IPsec, TLS), while a network is generally configured with many technologies. On the other hand, existing approaches work on a single policy type, also named domain (i.e., filtering, communication protection). Unfortunately, the complexity of real systems is not self-contained and each network security control may affect the behavior of other controls in the same network. The objective of this PhD work was to investigate novel approaches for modelling security policies and their anomalies, and formal techniques of anomaly analysis. We present in this dissertation our contributions to the current policy analysis state of the art and the achieved results. A first contribution was the definition of a new class of policy anomalies, i.e. the inter-technology anomalies, which arises in a set of policies of multiple security technologies. We provided also a formal model able to detect these new types of anomalies. One of the results achieved by applying the inter-technology analysis to the communication protection policies was to categorize twelve new types of anomalies. The second result of this activity was derived from an empirical assessment that proved the practical significance of detecting such new anomalies. The second contribution of this thesis was the definition of a newly-defined type of policy analysis, named inter-domain analysis, which identifies any anomaly that may arise among different policy domains. We improved the state of the art by proposing a possible model to detect the inter-domain anomalies, which is a generalization of the aforementioned inter-technology model. In particular, we defined the Unified Model for Policy Analysis (UMPA) to perform the inter-domain analysis by extending the analysis model applied for a single policy domain to comprehensive analysis of anomalies among many policy domains. The result of this last part of our dissertation was to improve the effectiveness of the analysis process. Thanks to the inter-domain analysis, indeed, administrators can detect in a simple and customizable way a greater set of anomalies than the sets they could detect by running individually any other model

Distributed Security Policy Analysis

Computer networks have become an important part of modern society, and computer network security is crucial for their correct and continuous operation. The security aspects of computer networks are defined by network security policies. The term policy, in general, is defined as ``a definite goal, course or method of action to guide and determine present and future decisions''. In the context of computer networks, a policy is ``a set of rules to administer, manage, and control access to network resources''. Network security policies are enforced by special network appliances, so called security controls.Different types of security policies are enforced by different types of security controls. Network security policies are hard to manage, and errors are quite common. The problem exists because network administrators do not have a good overview of the network, the defined policies and the interaction between them. Researchers have proposed different techniques for network security policy analysis, which aim to identify errors within policies so that administrators can correct them. There are three different solution approaches: anomaly analysis, reachability analysis and policy comparison. Anomaly analysis searches for potential semantic errors within policy rules, and can also be used to identify possible policy optimizations. Reachability analysis evaluates allowed communication within a computer network and can determine if a certain host can reach a service or a set of services. Policy comparison compares two or more network security policies and represents the differences between them in an intuitive way. Although research in this field has been carried out for over a decade, there is still no clear answer on how to reduce policy errors. The different analysis techniques have their pros and cons, but none of them is a sufficient solution. More precisely, they are mainly complements to each other, as one analysis technique finds policy errors which remain unknown to another. Therefore, to be able to have a complete analysis of the computer network, multiple models must be instantiated. An analysis model that can perform all types of analysis techniques is desirable and has three main advantages. Firstly, the model can cover the greatest number of possible policy errors. Secondly, the computational overhead of instantiating the model is required only once. Thirdly, research effort is reduced because improvements and extensions to the model are applied to all three analysis types at the same time. Fourthly, new algorithms can be evaluated by comparing their performance directly to each other. This work proposes a new analysis model which is capable of performing all three analysis techniques. Security policies and the network topology are represented by the so-called Geometric-Model. The Geometric-Model is a formal model based on the set theory and geometric interpretation of policy rules. Policy rules are defined according to the condition-action format: if the condition holds then the action is applied. A security policy is expressed as a set of rules, a resolution strategy which selects the action when more than one rule applies, external data used by the resolution strategy and a default action in case no rule applies. This work also introduces the concept of Equivalent-Policy, which is calculated on the network topology and the policies involved. All analysis techniques are performed on it with a much higher performance. A precomputation phase is required for two reasons. Firstly, security policies which modify the traffic must be transformed to gain linear behaviour. Secondly, there are much fewer rules required to represent the global behaviour of a set of policies than the sum of the rules in the involved policies. The analysis model can handle the most common security policies and is designed to be extensible for future security policy types. As already mentioned the Geometric-Model can represent all types of security policies, but the calculation of the Equivalent-Policy has some small dependencies on the details of different policy types. Therefore, the computation of the Equivalent-Policy must be tweaked to support new types. Since the model and the computation of the Equivalent-Policy was designed to be extendible, the effort required to introduce a new security policy type is minimal. The anomaly analysis can be performed on computer networks containing different security policies. The policy comparison can perform an Implementation-Verification among high-level security requirements and an entire computer network containing different security policies. The policy comparison can perform a ChangeImpact-Analysis of an entire network containing different security policies. The proposed model is implemented in a working prototype, and a performance evaluation has been performed. The performance of the implementation is more than sufficient for real scenarios. Although the calculation of the Equivalent-Policy requires a significant amount of time, it is still manageable and is required only once. The execution of the different analysis techniques is fast, and generally the results are calculated in real time. The implementation also exposes an API for future integration in different frameworks or software packages. Based on the API, a complete tool was implemented, with a graphical user interface and additional features

An Artificial Immune System-Inspired Multiobjective Evolutionary Algorithm with Application to the Detection of Distributed Computer Network Intrusions

Today\u27s predominantly-employed signature-based intrusion detection systems are reactive in nature and storage-limited. Their operation depends upon catching an instance of an intrusion or virus after a potentially successful attack, performing post-mortem analysis on that instance and encoding it into a signature that is stored in its anomaly database. The time required to perform these tasks provides a window of vulnerability to DoD computer systems. Further, because of the current maximum size of an Internet Protocol-based message, the database would have to be able to maintain 25665535 possible signature combinations. In order to tighten this response cycle within storage constraints, this thesis presents an Artificial Immune System-inspired Multiobjective Evolutionary Algorithm intended to measure the vector of trade-off solutions among detectors with regard to two independent objectives: best classification fitness and optimal hypervolume size. Modeled in the spirit of the human biological immune system and intended to augment DoD network defense systems, our algorithm generates network traffic detectors that are dispersed throughout the network. These detectors promiscuously monitor network traffic for exact and variant abnormal system events, based on only the detector\u27s own data structure and the ID domain truth set, and respond heuristically. The application domain employed for testing was the MIT-DARPA 1999 intrusion detection data set, composed of 7.2 million packets of notional Air Force Base network traffic. Results show our proof-of-concept algorithm correctly classifies at best 86.48% of the normal and 99.9% of the abnormal events, attributed to a detector affinity threshold typically between 39-44%. Further, four of the 16 intrusion sequences were classified with a 0% false positive rate

Increasing service visibility for future, softwarised air traffic management data networks

Air Traffic Management (ATM) is at an exciting frontier. The volume of air traffic is reaching the safe limits of current infrastructure. Yet, demand for more air traffic continues. To meet capacity demands, ATM data networks are increasing in complexity with: greater infrastructure integration, higher availability and precision of services; and the introduction of unmanned systems. Official recommendations into previous disruptive outages have high-lighted the need for operators to have richer monitoring capabilities and operational systems visibility, on-demand, in response to challenges. The work presented in this thesis, helps ATM operators better understand and increase visibility into the behaviour of their services and infrastructure, with the primary aim to inform decision-making to reduce service disruption. This is achieved by combining a container-based NFV framework with Software- Defined Networking (SDN). The application of SDN+NFV in this work allows lightweight, chain-able monitoring and anomaly detection functions to be deployed on-demand, and the appropriate (sub)set of network traffic routed through these virtual network functions to provide timely, context-specific information. This container-based function deployment architecture, allows for punctual in-network processing through the instantiation of custom functionality, at appropriate locations. When accidents do occur, such as the crash of a UAV, the lessons learnt should be integrated into future systems. For one such incident, the accident investigation identified a telemetry precursor an hour prior. The function deployment architecture allows operators to extend and adapt their network infrastructure, to incorporate the latest monitoring recommendations. Furthermore, this work has examined relationships in application-level information and network layer data representing individual examples of a wide range of generalisable cases including: between the cyber and physical components of surveillance data, the rate of change in telemetry to determine abnormal aircraft surface movements, and the emerging behaviour of network flooding. Each of these examples provide valuable context-specific benefits to operators and a generalised basis from which further tools can be developed to enhance their understanding of their networks

Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles

The damaging effects of cyberattacks to an industry like the Cooperative Connected and Automated Mobility (CCAM) can be tremendous. From the least important to the worst ones, one can mention for example the damage in the reputation of vehicle manufacturers, the increased denial of customers to adopt CCAM, the loss of working hours (having direct impact on the European GDP), material damages, increased environmental pollution due e.g., to traffic jams or malicious modifications in sensors’ firmware, and ultimately, the great danger for human lives, either they are drivers, passengers or pedestrians. Connected vehicles will soon become a reality on our roads, bringing along new services and capabilities, but also technical challenges and security threats. To overcome these risks, the CARAMEL project has developed several anti-hacking solutions for the new generation of vehicles. CARAMEL (Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles), a research project co-funded by the European Union under the Horizon 2020 framework programme, is a project consortium with 15 organizations from 8 European countries together with 3 Korean partners. The project applies a proactive approach based on Artificial Intelligence and Machine Learning techniques to detect and prevent potential cybersecurity threats to autonomous and connected vehicles. This approach has been addressed based on four fundamental pillars, namely: Autonomous Mobility, Connected Mobility, Electromobility, and Remote Control Vehicle. This book presents theory and results from each of these technical directions

Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles

The damaging effects of cyberattacks to an industry like the Cooperative Connected and Automated Mobility (CCAM) can be tremendous. From the least important to the worst ones, one can mention for example the damage in the reputation of vehicle manufacturers, the increased denial of customers to adopt CCAM, the loss of working hours (having direct impact on the European GDP), material damages, increased environmental pollution due e.g., to traffic jams or malicious modifications in sensors’ firmware, and ultimately, the great danger for human lives, either they are drivers, passengers or pedestrians. Connected vehicles will soon become a reality on our roads, bringing along new services and capabilities, but also technical challenges and security threats. To overcome these risks, the CARAMEL project has developed several anti-hacking solutions for the new generation of vehicles. CARAMEL (Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles), a research project co-funded by the European Union under the Horizon 2020 framework programme, is a project consortium with 15 organizations from 8 European countries together with 3 Korean partners. The project applies a proactive approach based on Artificial Intelligence and Machine Learning techniques to detect and prevent potential cybersecurity threats to autonomous and connected vehicles. This approach has been addressed based on four fundamental pillars, namely: Autonomous Mobility, Connected Mobility, Electromobility, and Remote Control Vehicle. This book presents theory and results from each of these technical directions

Spectrum Sharing, Latency, and Security in 5G Networks with Application to IoT and Smart Grid

The surge of mobile devices, such as smartphones, and tables, demands additional capacity. On the other hand, Internet-of-Things (IoT) and smart grid, which connects numerous sensors, devices, and machines require ubiquitous connectivity and data security. Additionally, some use cases, such as automated manufacturing process, automated transportation, and smart grid, require latency as low as 1 ms, and reliability as high as 99.99\%. To enhance throughput and support massive connectivity, sharing of the unlicensed spectrum (3.5 GHz, 5GHz, and mmWave) is a potential solution. On the other hand, to address the latency, drastic changes in the network architecture is required. The fifth generation (5G) cellular networks will embrace the spectrum sharing and network architecture modifications to address the throughput enhancement, massive connectivity, and low latency. To utilize the unlicensed spectrum, we propose a fixed duty cycle based coexistence of LTE and WiFi, in which the duty cycle of LTE transmission can be adjusted based on the amount of data. In the second approach, a multi-arm bandit learning based coexistence of LTE and WiFi has been developed. The duty cycle of transmission and downlink power are adapted through the exploration and exploitation. This approach improves the aggregated capacity by 33\%, along with cell edge and energy efficiency enhancement. We also investigate the performance of LTE and ZigBee coexistence using smart grid as a scenario. In case of low latency, we summarize the existing works into three domains in the context of 5G networks: core, radio and caching networks. Along with this, fundamental constraints for achieving low latency are identified followed by a general overview of exemplary 5G networks. Besides that, a loop-free, low latency and local-decision based routing protocol is derived in the context of smart grid. This approach ensures low latency and reliable data communication for stationary devices. To address data security in wireless communication, we introduce a geo-location based data encryption, along with node authentication by k-nearest neighbor algorithm. In the second approach, node authentication by the support vector machine, along with public-private key management, is proposed. Both approaches ensure data security without increasing the packet overhead compared to the existing approaches

Two-tier Intrusion Detection System for Mobile Ad Hoc Networks

Nowadays, a commonly used wireless network (i.e. Wi-Fi) operates with the aid of a fixed infrastructure (i.e. an access point) to facilitate communication between nodes when they roam from one location to another. The need for such a fixed supporting infrastructure limits the adaptability of the wireless network, especially in situations where the deployment of such an infrastructure is impractical. In addition, Wi-Fi limits nodes' communication as it only provides facility for mobile nodes to send and receive information, but not reroute the information across the network. Recent advancements in computer network introduced a new wireless network, known as a Mobile Ad Hoc Network (MANET), to overcome these limitations. MANET has a set of unique characteristics that make it different from other kind of wireless networks. Often referred as a peer to peer network, such a network does not have any fixed topology, thus nodes are free to roam anywhere, and could join or leave the network anytime they desire. Its ability to be setup without the need of any infrastructure is very useful, especially in geographically constrained environments such as in a military battlefield or a disaster relief operation. In addition, through its multi hop routing facility, each node could function as a router, thus communication between nodes could be made available without the need of a supporting fixed router or an access point. However, these handy facilities come with big challenges, especially in dealing with the security issues. This research aims to address MANET security issues by proposing a novel intrusion detection system that could be used to complement existing prevention mechanisms that have been proposed to secure such a network. A comprehensive analysis of attacks and the existing security measures proved that there is a need for an Intrusion Detection System (IDS) to protect MANETs against security threats. The analysis also suggested that the existing IDS proposed for MANET are not immune against a colluding blackmail attack due to the nature of such a network that comprises autonomous and anonymous nodes. The IDS architecture as proposed in this study utilises trust relationships between nodes to overcome this nodes' anonymity issue. Through a friendship mechanism, the problems of false accusations and false alarms caused by blackmail attackers in global detection and response mechanisms could be eliminated. The applicability of the friendship concept as well as other proposed mechanisms to solve MANET IDS related issues have been validated through a set of simulation experiments. Several MANET settings, which differ from each other based on the network's density level, the number of initial trusted friends owned by each node, and the duration of the simulation times, have been used to study the effects of such factors towards the overall performance of the proposed IDS framework. The results obtained from the experiments proved that the proposed concepts are capable to at least minimise i f not fully eliminate the problem currently faced in MANET IDS

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts