Unmanned ground vehicles (UGVs), including autonomous vehicles (AVs), are increasingly deployed across civilian, industrial, and defence environments. Their growing complexity across sensors, controllers, communication modules, and critical functional software also increases their exposure to cyber threats. Although current research on UGV cybersecurity provides valuable insights, it remains fragmented and often focuses on isolated attacks or individual defences without offering a consolidated security perspective. Existing resources, such as MITRE ATT&CK, AUTO-ISAC’s Automotive Threat Matrix, and NIST 800-160, provide useful guidance; however, they are designed for other domains and do not fully capture the operational characteristics and requirements of UGVs. This paper addresses this gap by developing a unified offensive and defensive framework specifically for UGVs. It organises existing literature into UGV-specific tactics, techniques, and asset dependencies, and pairs them with a UGV-focused mitigation database mapped to relevant security controls. Unlike prior efforts, this work integrates these components into a single coherent model, forming the first consolidated cyber offence–defence matrix tailored to UGV operations. To demonstrate the applicability of the framework, we analyse the 2014 Jeep Cherokee remote compromise and map its attack chain into the UGV threat structure. We also evaluate how mitigation strategies align with attack techniques by implementing three representative defences using the CARLASec simulation platform. Finally, we identify remaining gaps in current defences and point to areas requiring further development to improve UGV resilience.

The Internet of Things (IoT) comprises interconnected physical devices, ranging from smartphones to household appliances, that communicate wirelessly over the internet. As IoT networks grow in complexity, new cybersecurity risks continue to emerge, with cybercriminals exploiting unprotected vulnerabilities. While various security solutions have been developed, traditional network intrusion detection systems (NIDS) often struggle to detect novel cyberattacks, limiting their adaptability to evolving cyberattacks. This paper addresses these limitations by proposing a decision framework that integrates three models to classify IoT traffic as benign, a known attack and type, or a novel attack. The framework consists of: 1) a binary neural network that distinguishes benign traffic from any attack, 2) a multi-class neural network that classifies traffic as benign or one of known attack types, and 3) a k -Nearest Neighbors (KNN) model that assesses packet similarity to known attack patterns. By combining these models through ensemble voting and leveraging distance metrics, the proposed framework effectively identifies known and novel attacks. Using two benchmark datasets, the framework demonstrated considerable detection rates for novel attacks, which conventional supervised baseline models failed to achieve, while retaining strong performance in known attack detection and categorization. These findings offer valuable insights for both academia and industry, contributing to the development of more adaptive IoT security solutions.

Network Intrusion Detection Systems (NIDS) are crucial for safeguarding networks against evolving cyber threats. However, evaluations of NIDS often assume offline training, supervised learning, or static concepts, which do not accurately capture the complexities of real-world scenarios. In this paper, we critically assess the performance of current NIDS and outlier detectors under a realistic threat model that uses online training, unsupervised learning, and is concept drift-prone. We find that conventional feature extractors struggle in diverse real-life environments. To overcome this, we propose LAGER, a novel feature extractor utilizing graph neural networks to extract representative features from a layer-wise graph representation of the network. Our results demonstrate that LAGER enhances the detection accuracy and adaptability to concept drifts for a wide range of NIDS and lays a solid foundation for robust network feature extraction.

The combination of computer vision and artificial intelligence is fundamentally transforming a broad spectrum of industries by enabling machines to interpret and act upon visual data with high levels of accuracy. As the biggest and by far the most popular open-source computer vision library, OpenCV library provides an extensive suite of programming functions supporting real-time computer vision. Bugs in the OpenCV library can affect the downstream computer vision applications, and it is critical to ensure the reliability of the OpenCV library. This paper introduces VistaFuzz, a novel technique for harnessing large language models (LLMs) for document-guided fuzzing of the OpenCV library. Vistafuzz utilizes LLMs to parse API documentation and obtain standardized API information. Based on this standardized information, Vista Fuzz extracts constraints on individual input parameters and dependencies between these. Using these constraints and dependencies, VistaFuzz then generates new input values to systematically test each target API. We evaluate the effectiveness of Vistafuzz in testing 330 APIs in the OpenCV library, and the results show that Vistafuzz detected 17 new bugs, where 10 bugs have been confirmed, and 5 of these have been fixed.

Deep learning powers critical applications such as autonomous driving, healthcare, and finance, where the correctness of underlying libraries is essential. Bugs in widely used deep learning APIs can propagate to downstream systems, causing serious consequences. While existing fuzzing techniques detect bugs through intra-framework testing across hardware backends (CPU vs. GPU), they may miss bugs that manifest identically across backends and thus escape detection under these strategies. To address this problem, we propose XAMT, a cross-framework fuzzing method that tests deep learning libraries by matching and comparing functionally equivalent APIs across different frameworks. XAMT matches APIs using similarity-based rules based on names, descriptions, and parameter structures. It then aligns inputs and applies variance-guided differential testing to detect bugs. We evaluated XAMT on five popular frameworks, including PyTorch, TensorFlow, Keras, Chainer, and JAX. XAMT matched 839 APIs and identified 238 matched API groups, and detected 17 bugs, 12 of which have been confirmed. Our results show that XAMT uncovers bugs undetectable by intraframework testing, especially those that manifest consistently across backends. XAMT offers a complementary approach to existing methods and offers a new perspective on the testing of deep learning libraries.

The growing reliance on digital technologies onboard vessels has significantly increased their attack surface. As a result, both IT and OT systems are now vulnerable to a range of cyberattacks. However, existing methods used to assess vulnerability and threats often rely on outdated threat or vulnerability information, limiting their effectiveness. Consequently, a more proactive approach to assessing the security of vessel systems is needed. Threat hunting offers a proactive way of gathering the latest threat and vulnerability data from operational maritime vessels, which can be used for comprehensive security assessments. However, there is a lack of systems specifically designed to perform both threat-hunting and security assessment operations. In this paper, we propose a threat-hunting and security assessment framework that collects and processes real-time data from vessels and conducts security analysis using a graphical security model designed for vessel systems. Our approach demonstrates how the collected information can be used to evaluate a ship’s security posture by simulating potential attack scenarios and understanding how an adversary might attempt to compromise the vessel’s network. It also provides a foundation for more informed, data-driven cybersecurity strategies for the unique systems found onboard maritime vessels.

The robustness of YOLOv5-based camera perception for autonomous driving was systematically evaluated under diverse visual perturbations and spectral configurations using the CARLA simulation environment. An agent-camera framework decoupled perception from vehicle control, enabling consistent testing across 20 configurations and 200 trials (104,231 frames) covering four color modes (RGB, red, green, blue) and five perturbation types (salt-and-pepper noise, Gaussian noise, blur, contrast enhancement, baseline). Results revealed counterintuitive robustness patterns: contrast enhancement and green-channel filtering each improved detection by 37%, while salt-and-pepper noise caused an 83% degradation. The optimal combination-red filtering with contrast enhancement-yielded a 42% gain, demonstrating synergistic effects between spectral and photometric factors. However, confidence scores remained nearly constant (0.55-0.65 range) despite large accuracy fluctuations, indicating that confidence-based monitoring fails to reflect true perception reliability. These findings highlight that lightweight spectral filtering and contrast optimization can enhance perception robustness, while safe deployment requires complementary reliability modeling and sensor redundancy to ensure dependable autonomous vision.

Since the inception of Mirai in 2016, a proliferation of advanced botnets targeting Internet of Things (IoT) devices has occurred, resulting in a notable increase in large-scale cyber attacks against online services. The continual emergence of novel strategies characterises the evolving landscape of IoT botnets. Despite this, a comprehensive understanding of this evolving threat remains elusive, impeding the development of robust defence mechanisms. This paper investigated 55 instances of IoT botnets spanning from 2008 to 2021 to elucidate their evolutionary patterns based on prevalent tactics and techniques. A novel taxonomy of IoT botnets is proposed and formulated with attack tactics, techniques, types, and procedures. We augment our existing simulation framework, IoTSecSim, with enhanced functionalities to simulate novel cyber-attack scenarios incorporating diverse network configurations, evolving attack tactics, and defence strategies. Through comprehensive simulations via the extended IoTSecSim, we assessed the impact of these evolving IoT attack tactics and gauged the efficacy of traditional defence mechanisms using various security metrics.

Cybersecurity risk assessment has become a critical task in systems development and the operation of complex networked systems. However, current state-of-the-art approaches for detecting vulnerabilities, such as automated security testing or penetration testing, often result in late detection. Thus, there is a growing need for security by design, which involves conducting security-related analyses as early as possible in the system development life cycle. This paper proposes an integrated approach that combines static and dynamic hierarchical model-based security risk assessment. The approach enables early identification of security risks during system design, utilizing various models based on the Unified Modeling Language (UML), with lightweight extensions using profiles and stereotypes to capture security attributes like vulnerabilities and asset values. These security attributes are then used to compute relevant properties, including threat space, possible attack paths, and selected network-based security metrics. To facilitate dynamic security analysis, the UML model is subsequently translated into a deterministic and stochastic Petri net (DSPN). This translation allows for the dynamic analysis and simulation of the system’s state and behavior during an attack, capturing temporal aspects and probabilistic transitions. By representing system components and their interactions as modular Petri nets, the DSPN framework facilitates comprehensive simulation and analysis of possible attack scenarios. This also allows us to estimate time-based security metrics such as the duration required for an attacker to compromise system components. Consequently, this combined approach effectively addresses both static security analysis and dynamic state behavior, providing an integrated understanding of the system’s resilience against cyber threats. A real-world industrial case study illustrates the effectiveness of this approach. The underlying data originates from security assessments performed by Keen Security Labs, which were independently verified by BMW (Cai et al., 2019). Specifically, we present an infotainment system network model as implemented in multiple car models along with corresponding attack and defense models. We then demonstrate how the approach assesses the cybersecurity risk of such in-vehicle networks.

Autonomous Vehicles (AVs) integrate numerous control units , network components, and protocols to operate effectively and interact with their surroundings, such as pedestrians and other vehicles. While these technologies enhance vehicle capabilities and enrich the driving experience, they also introduce new attack surfaces, making AVs vulnerable to cyber-attacks. Such cyber-attacks can lead to severe consequences, including traffic disruption and even threats to human life. Security modelling is crucial to safeguarding AVs as it enables the simulation and analysis of an AV’s security before any potential attacks. However, the existing research on AV security modelling methods for analysing security risks and evaluating the effectiveness of security measures remains limited. In this work, we introduce a novel graphical security model and metrics to assess the security of AV systems. The proposed model utilizes initial network information to build attack graphs and attack trees at different layers of network depth. From this, various metrics are automatically calculated to analyse the security and safety of the AV network. The proposed model is designed to identify potential attack paths, analyse security and safety with precise metrics, and evaluate various defence strategies. We demonstrate the effectiveness of our framework by applying it to two AV networks and distinct AV attack scenarios, showcasing its capability to enhance the security of AVs.

Network Intrusion Detection Systems (NIDSes) are increasingly incorporating Machine Learning (ML) and Deep Learning (DL) algorithms for detecting network intrusions. However, ML/DL algorithms are susceptible to adversarial examples, which can lead to the misclassification of input data. This vulnerability poses a significant threat to the reliability of NIDSes in security-sensitive domains. To address this concern, we propose a novel defense framework called Moving Target Defence as Adversarial Defence (MTD-AD) to protect anomaly-based NIDS models from adversarial attacks by stochastically altering the decision boundary of NIDS. Our approach capitalizes on the observation that adversarial examples reside in close proximity to the decision boundary of the model and exhibit sensitivity to slight perturbations of that boundary. We demonstrate the effectiveness of MTD-AD against practical adversarial attacks and evaluate its resilience against adaptive adversaries using an IoT intrusion detection dataset.

The proliferation of the Internet of Things (IoT) devices has provided attackers with tremendous opportunities to launch various cyber-attacks. It has been challenging to analyse the impact of cyber-attacks and evaluate the effectiveness of defences in real IoT environments due to the scale and heterogeneity of IoT networks. In this work, we propose a novel simulation framework and a software tool, IoT Security Simulator (IoTSecSim). IoTSecSim is operated based on a framework we propose for modelling and simulating cyber-attacks and various defences in IoT networks. IoTSecSim is not only able to support the creation of an IoT network with flexible settings of IoT devices and topology information but also models the attack behaviours, node-level, and network-level defences. Moreover, a systematic security evaluation can be performed by comparing the results based on the calculation of security metrics. We perform simulations with case studies on Mirai malware and its variants to model cyber-attack behaviours on IoT networks and evaluate the impact of these attacks and the effectiveness of defence techniques via IoTSecSim. Then, we carry out a sensitivity analysis to justify that the simulation results produced by IoTSecSim are accurate and feasible when compared with related works. We also perform a comparative performance analysis with four combinations of cyber-attack behaviours and show that these behaviours can influence IoT malware propagation in different situations. We consider multiple attacker models and deploy conventional defence techniques (including firewall, intrusion detection, and vulnerability patching) to investigate the effectiveness of defence techniques. IoTSecSim provides a generalised and extensible simulation framework that enables users to model emerging cyber-attacks against IoT networks and evaluate the effectiveness of defences against these attacks. This helps users to focus on the design and performance evaluation of new defences before the actual implementation and deployment of the defences are required.

Network Intrusion Detection Systems (NIDSes) are crucial for securing various networks from malicious attacks. Recent developments in Deep Neural Networks (DNNs) have encouraged researchers to incorporate DNNs as the underlying detection engine for NIDS. However, DNNs are susceptible to adversarial attacks, where subtle modifications to input data result in misclassification, posing a significant threat to security-sensitive domains such as NIDS. Existing efforts in adversarial defenses predominantly focus on supervised classification tasks in Computer Vision, differing substantially from the unsupervised outlier detection tasks in NIDS. To bridge this gap, we introduce a novel method of generalized adversarial robustness and present NIDS-Vis, an innovative black-box algorithm that traverses the decision boundary of DNN-based NIDSes near given inputs. Through NIDS-Vis, we can visualize the geometry of the decision boundaries and examine their impact on performance and adversarial robustness. Our experiment uncovers a tradeoff between performance and robustness, and we propose two novel training techniques, feature space partition and distributional loss function, to enhance the generalized adversarial robustness of DNN-based NIDSes without significantly compromising performance.

Moving Target Defenses (MTD) are proactive security countermeasures that change the attack surface in a system in ways that make it harder for attackers to succeed. These techniques have been shown to be effective, and their application in software-defined networking (SDN) against simple automated attacks is growing in popularity. However, with the increased knowledge of and ease of access to Artificial Intelligence (AI) techniques, AI is starting to be used to enhance cyber attacks, which are becoming increasingly complex. Hence, the evaluation of MTDs against simple automated attacks is no longer enough to demonstrate their effectiveness in increasing system security.

Although machine learning models achieve high classification accuracy against benign examples, they are vulnerable to adversarial machine learning (AML) attacks which generate adversarial examples by adding well-crafted perturbations to the benign examples. The perturbations can be increased to enhance the attack success rate, however, if the perturbations are added without considering the semantic or perceptual similarity between the benign and adversarial examples, the attack can be easily perceived/detected. As such, there exists a trade-off between the attack success rate and the perceptual similarity. In this paper, we propose a novel Semantic-Preserving Adversarial Transformation (SPAT) framework which facilitates an advantageous trade-off between the two metrics. SPAT modifies the optimisation objective of an AML attack to include the goal of increasing the attack success rate as well as the goal of maintaining the perceptual similarity between benign and adversarial examples. Our experiments on a variety of datasets including CIFAR-10, GTSRB, and MNIST demonstrate that SPAT-transformed AML attacks achieve better perceptual similarity while maintaining the attack success rates as the conventional AML attacks.

The satisfaction of a software requirement is commonly stated as a Boolean value, that is, a security requirement is either satisfied (true) or not (false). However, a discrete Boolean value to measure the satisfaction level of a security requirement by deployed mechanisms is not very useful. Rather, it would be more effective if we could quantify the level of satisfaction of security requirements on a continuous scale. We propose an approach to achieve this for cloud software systems based on relationships between defense strength, exploitability of vulnerabilities, and attack severity. We extend the concept of entailment relationship from the field of requirements engineering with the satisfiability aspects of security requirements. The proposed approach enables us to systematically structure security concepts into three sets of related descriptions to quantify the satisfaction level of security requirements with the deployed security solutions. To demonstrate the feasibility of the proposed approach, we evaluate the approach in a case study. As a result, security administrators are able to deploy more effective and appropriate security solutions based on their assessment.

Deep learning-based cybersecurity applications should be able to continually accumulate threat knowledge for new types of threats over time while maintaining the knowledge of threats already exposed to the application. This paper proposes episodic memory management for continual learning with network intrusion datasets. For new attacks, the number of samples may not be sufficiently large for training, and thus the memory management algorithm should retain as many samples as possible instead of random sampling in the episodic memory for continual learning. The experiment results indicated that the proposed algorithm outperforms offline learning in terms of average per-class accuracy in a continual scenario with a network intrusion dataset.

Widely used deep learning models are found to have poor robustness. Little noises can fool state-of-the-art models into making incorrect predictions. While there is a great deal of high-performance attack generation methods, most of them directly add perturbations to original data and measure them using L_p norms; this can break the major structure of data, thus, creating invalid attacks. In this paper, we propose a black-box attack, which, instead of modifying original data, modifies latent features of data extracted by an autoencoder; then, we measure noises in semantic space to protect the semantics of data. We trained autoencoders on MNIST and CIFAR-10 datasets and found optimal adversarial perturbations using a genetic algorithm. Our approach achieved a 100% attack success rate on the first 100 data of MNIST and CIFAR-10 datasets with less perturbation than FGSM.

As cyberattacks continuously threaten conventional defense techniques, Moving Target Defense (MTD) has emerged as a promising countermeasure to defend a system against them by dynamically changing attack surfaces of the system. MTD provides the system a state-of-art security mechanism that increases the attack cost or complexity of the system aiming for reducing vulnerabilities exposed to potential attackers. However, the notion of the proactive and dynamic systems adopting MTD services causes a substantial trade-off between system performance and security effectiveness, compared to conventional defense strategies. The MTD tactics accordingly result in performance degradation (e.g., interruptions of service availability) as one of the drawbacks caused by continuous mutations of the system configuration. Therefore, it is crucial to validate not only the security benefits against system threats but also quality-of-service (QoS) for clients when an MTD-enabled system proactively continues to mutate attack surfaces. This paper contributes to (i) developing new security metrics; (ii) measuring both the performance degradation and security effectiveness against potential real attacks (i.e., scanning, HTTP flood, dictionary, and SQL injection attack); and (iii) comparing the proposed job management strategies (i.e., drop and switch-over) from a performance and security perspective in a physical SDN testbed.

The use of traditional defense mechanisms or intrusion detection systems presents a disadvantage for defenders against attackers since these mechanisms are essentially reactive. Moving target defense (MTD) has emerged as a proactive defense mechanism to reduce this disadvantage by randomly and continuously changing the attack surface of a system to confuse attackers. Although significant progress has been made recently in analyzing the security effectiveness of MTD mechanisms, critical gaps still exist, especially in maximizing security levels and estimating network reconfiguration speed for given attack power. In this paper, we propose a set of Petri Net models and use them to perform a comprehensive evaluation regarding key security metrics of Software-Defined Network (SDNs) based systems adopting a time-based MTD mechanism. We evaluate two use-case scenarios considering two different types of attacks to demonstrate the feasibility and applicability of our models. Our analyses showed that a time-based MTD mechanism could reduce the attackers' speed by at least 78% compared to a system without MTD. Also, in the best-case scenario, it can reduce the attack success probability by about ten times.

As cyberattacks are rising, Moving Target Defense (MTD) can be a countermeasure to proactively protect a networked system against cyber-attacks. Despite the fact that MTD systems demonstrate security effectiveness against the reconnaissance of Cyber Kill Chain (CKC), a time-based MTD has a limitation when it comes to protecting a system against the next phases of CKC. In this work, we propose a novel hybrid MTD technique, its implementation and evaluation. Our hybrid MTD system is designed on a real SDN testbed and it uses an intrusion detection system (IDS) to provide an additional MTD triggering condition. This in itself presents an extra layer of system protection. Our hybrid MTD technique can enhance security in the response to multi-phased cyber-attacks. The use of the reactive MTD triggering from intrusion detection alert shows that it is effective to thwart the further phase of detected cyber-attacks. We also investigate the performance degradation due to more frequent MTD triggers.This work contributes to (1) proposing an ML-based rule classification model for predicting identified attacks which helps a decision-making process for security enhancement; (2) developing a hybrid-based MTD integrated with a Network Intrusion Detection System (NIDS) with the consideration of performance and security; and (3) assessment of the performance degradation and security effectiveness against potential real attacks (i.e., scanning, dictionary, and SQL injection attack) in a physical testbed.

Reinforcement learning (RL) is a promising approach for intelligent agents to protect a given system under highly hostile environments. RL allows the agent to adaptively make sequential defense decisions based on the perceived current state of system security aiming to achieve the maximum defense performance in terms of fast, efficient, and automated detection, threat analysis, and response to the threat. In this paper, we propose a deep reinforcement learning (DRL)-based adaptive traffic inspection and moving target defense countermeasure framework, called ‘DIVERGENCE,’ for building a secure networked system. The DIVERGENCE provides two main security services: (1) a DRL-based network traffic inspection mechanism to achieve scalable and intensive network traffic visibility for rapid threat detection; and (2) an address shuffling-based moving target defense (MTD) technique to defend against threats as a proactive intrusion prevention mechanism. Through extensive simulations and experiments, we demonstrate that the DIVERGENCE successfully caught malicious traffic flows while significantly reducing the vulnerability of the network through MTD.

Resource constrained Internet-of-Things (IoT) devices are highly likely to be compromised by attackers, because strong security protections may not be suitable to be deployed. This requires an alternative approach to protect vulnerable components in IoT networks. In this article, we propose an integrated defense technique to achieve intrusion prevention by leveraging cyberdeception (i.e., a decoy system) and moving target defense (i.e., network topology shuffling). We evaluate the effectiveness and efficiency of our proposed technique analytically based on a graphical security model in a software-defined networking (SDN)-based IoT network. We develop four strategies (i.e., fixed/random and adaptive/hybrid) to address “when” to perform network topology shuffling and three strategies (i.e., genetic algorithm/decoy attack path-based optimization/random) to address “how” to perform network topology shuffling on a decoy-populated IoT network, and we analyze which strategy can best achieve a system goal, such as prolonging the system lifetime, maximizing deception effectiveness, maximizing service availability, or minimizing defense cost. We demonstrated that a software-defined IoT network running our intrusion prevention technique at the optimal parameter setting prolongs system lifetime, increases attack complexity of compromising critical nodes, and maintains superior service availability compared with a counterpart IoT network without running our intrusion prevention technique. Further, when given a single goal or a multi-objective goal (e.g., maximizing the system lifetime and service availability while minimizing the defense cost) as input, the best combination of “when” and “how” strategies is identified for executing our proposed technique under which the specified goal can be best achieved.

Modern networks are dynamic with configuration changes that introduces a set of challenge to the network administrator in terms of security and availability. Here, the major challenge faced by the administrator is the increasing number of vulnerabilities with the uncertainties related to defense deployment options and how these options affect the network availability over time. This work proposes a hierarchical model-based approach to evaluate the availability of dynamic networks considering the deployment of different hardening options. In particular, this work adopts reliability block diagrams and Petri nets to represent and analyze dynamic network environments and evaluate their availability. A case study is presented to demonstrate the feasibility, usefulness, and scalability of the proposed approach for computing the availability of dynamic networks considering different hardening options. The proposed approach can be helpful for network administrators who are in charge of choosing the best hardening options taking into account the impacts on availability.

Existing security risk evaluation approaches (e.g., asset-based) do not consider specific security requirements of individual cloud computing clients in the security risk evaluation. In this paper, we propose a threat-specific risk evaluation approach that uses various security attributes of the cloud (e.g., vulnerability information, the probability of an attack, and the impact of each attack associated with the identified threat(s)) as well as the client-specific security requirements in the cloud. Our approach allows a security administrator of the cloud provider to make fine-grained decisions for selecting mitigation strategies in order to protect the outsourced computing assets of individual clients based on their specific security needs against specific threats. This is different from the existing asset-based approaches where they do not have the functionalities to provide the security evaluation of the cloud with respect to specific threats. On the other hand, the proposed approach enables security administrators to compute a range of more effective client-specific countermeasures with respect to the importance of security requirements and threats. The experimental evaluation results demonstrate that effective security solutions vary due to specific threats prioritized by different clients for an application in the cloud. Further, the proposed approach is not limited to only the cloud-based systems, but can easily be adopted to other networked systems. We have also developed a software tool to support the proposed approach.

Due to the constrained resource and computational limitation of many Internet of Things (IoT) devices, conventional security protections, which require high computational overhead are not suitable to be deployed. Thus, vulnerable IoT devices could be easily exploited by attackers to break into networks. In this paper, we employ cyber deception and moving target defense (MTD) techniques to proactively change the network topology with both real and decoy nodes with the support of software-defined networking (SDN) technology and investigate the impact of single-target and multi-target attacks on the effectiveness of the integrated mechanism via a hierarchical graphical security model with security metrics. We also implement a web-based visualization interface to show topology changes with highlighted attack paths. Finally, the qualitative security analysis is performed for a small-scale and SDN-supported IoT network with different combinations of decoy types and levels of attack intelligence. Simulation results show the integrated defense mechanism can introduce longer mean-time-to-security-failure and larger attack impact under the multi-target attack, compared with the single-target attack model. In addition, adaptive shuffling has better performance than fixed interval shuffling in terms of a higher proportion of decoy paths, longer mean-time-to-security-failure and largely reduced defense cost.

Cloud computing offers a model where resources (storage, applications, etc.) are abstracted and provided “as-aservice” in a remotely accessible manner. Although there are numerous claimed benefits of the Cloud to ensure confidentiality, integrity, and availability of the stored data, the number of security breaches is still on the rise. The lack of security assurance and transparency prevented customers/enterprises from trusting the Cloud Service Providers (CSPs). Unless the customer's security requirements are identified and documented by the CSPs, customers can not be assured that the CSPs will satisfy their requirements. Furthermore, the customer's compensation upon a violation is a manual time intensive process.In this paper we address the aforementioned challenges by proposing a decentralized customer-based monitoring approach running over Ethereum blockchain. The proposed approach allows the customer(s) to validate the compliance of CSP(s) to the contracted services in the Service Level Agreements (SLAs) and “autonomsly” compensate customers in case of security breaches. At the same time, the proposed approach prevents customers from misreporting for financial gain. The approach builds upon the Ethereum blockchain infrastructure in order to securely store monitoring logs and incorporate SLAs as smart contracts. The compliance validation framework is implemented and its functionality is evaluated on Amazon EC2 and Ethereum Blockchain.

Software defined networking (SDN) has been adopted in many application domains as it provides functionalities to dynamically control the network flow more robust and more economical compared to the traditional networks. In order to strengthen the security of the SDN against cyber attacks, many security solutions have been proposed. However, those solutions need to be compared in order to optimize the security of the SDN. To assess and evaluate the security of the SDN systematically, one can use graphical security models (e.g., attack graphs and attack trees). However, it is difficult to provide defense against an attack in real time due to their high computational complexity. In this paper, we propose a real-time intrusion response in SDN using precomputation to estimate the likelihood of future attack paths from an ongoing attack. We also take into account various SDN components to conduct a security assessment, which were not available when addressing only the components of an existing network. Our experimental analysis shows that we are able to estimate possible attack paths of an ongoing attack to mitigate it in real time, as well as showing the security metrics that depend on the flow table, including the SDN component. Hence, the proposed approach can be used to provide effective real-time mitigation solutions for securing SDN.

Moving target defense (MTD) has emerged as a proactive defense mechanism aiming to thwart a potential attacker. The key underlying idea of MTD is to increase uncertainty and confusion for attackers by changing the attack surface (i.e., system or network configurations) that can invalidate the intelligence collected by the attackers and interrupt attack execution; ultimately leading to attack failure. Recently, the significant advance of software-defined networking (SDN) technology has enabled several complex system operations to be highly flexible and robust; particularly in terms of programmability and controllability with the help of SDN controllers. Accordingly, many security operations have utilized this capability to be optimally deployed in a complex network using the SDN functionalities. In this paper, by leveraging the advanced SDN technology, we developed an attack graph-based MTD technique that shuffles a host’s network configurations (e.g., MAC/IP/port addresses) based on its criticality, which is highly exploitable by attackers when the host is on the attack path(s). To this end, we developed a hierarchical attack graph model that provides a network’s vulnerability and network topology, which can be utilized for the MTD shuffling decisions in selecting highly exploitable hosts in a given network, and determining the frequency of shuffling the hosts’ network configurations. The MTD shuffling with a high priority on more exploitable, critical hosts contributes to providing adaptive, proactive, and affordable defense services aiming to minimize attack success probability with minimum MTD cost. We validated the out performance of the proposed MTD in attack success probability and MTD cost via both simulation and real SDN testbed experiments.

Moving target defense (MTD) is a proactive defense mechanism of changing the attack surface to increase an attacker's confusion and/or uncertainty, which invalidates its intelligence gained through reconnaissance and/or network scanning attacks. In this work, we propose software-defined networking (SDN)-based MTD technique using the shuffling of IP addresses and port numbers aiming to obfuscate both network and transport layers' real identities of the host and the service for defending against the network reconnaissance and scanning attacks. We call our proposed MTD technique Random Host and Service Multiplexing, namely RHSM. RHSM allows each host to use random, multiple virtual IP addresses to be dynamically and periodically shuffled. In addition, it uses short-lived, multiple virtual port numbers for an active service running on the host. Our proposed RHSM is novel in that we employ multiplexing (or de-multiplexing) to dynamically change and remap from all the virtual IPs of the host to the real IP or the virtual ports of the services to the real port, respectively. Via extensive simulation experiments, we prove how effectively and efficiently RHSM outperforms a baseline counterpart (i.e., a static network without RHSM) in terms of the attack success probability and defense cost.

Hardening the dynamic networks is a very challenging task due to their complexity and dynamicity. Moreover, there may be multi-objectives to satisfy, while containing the solutions within the constraints (e.g., fixed budget, availability of countermeasures, performance degradation, non-patchable vulnerabilities, etc). In this paper, we propose a systematic approach to optimise the selection of the security hardening options for the dynamic networks given multiple constraints and objectives. To do so, we evaluate potential attack scenarios for a given time period, and then use a multi-objective optimisation based on Non-dominated Sorting Genetic Algorithm to find the optimal set of security hardening options. We measure the effectiveness of the options using various security metrics, which is demonstrated through experimental analysis. The results show that our approach can be applied to select the optimal set of security hardening options to be deployed for the dynamic networks given multiple objectives and constraints.

Systems unavailability may produce severe consequences for modern business such as data loss, customer dissatisfaction, and subsequent revenue loss. Disaster recovery (DR) solutions have been adopted by many organizations as an attempt to prevent data loss and ensure business continuity. With the cloud computing expansion, different cloud providers have been offering low-cost solutions for DR purposes such as the Backup-as-a-service (BaaS) for consumers. Therefore, in this paper, we present an integrated model-experiment approach to evaluate a BaaS environment for DR purposes. We use analytic models and fault-injection experiments to evaluate DR keymetrics such as availability, downtime, Recovery Time Objective (RTO), and Recovery Point Objective (RPO) in a real-world BaaS environment. The results revealed that the environment availability can vary according to the amount of data to backed up and restored. Besides, a sensitivity analysis shows that the RTO and RPO are mainly influenced by the the mean time to recover from a disaster and the backup interval, respectively.

As connected and autonomous vehicle technology evolves, the design of in-vehicle network architecture, which connects multiple electronic control units (ECUs) and internal sensors and supports connectivity to the outside of the vehicle, has increased significantly to meet security needs. However, the heterogeneous structure of the in-vehicle network and lack of security consideration has introduced a lack of scalability and security concerns. In this work, we propose a shuffling-based moving target defense (MTD) technique aiming to disturb network reconnaissance attacks and deployed it in the proposed software-defined networking (SDN)-based in-vehicle network architecture. To validate the proposed MTD, we compare the service availability of our proposed MTD and non-MTD counterpart in the presence of the reconnaissance-based false message injection attacks.

Advances in the Internet of Things (IoT) have changed Smart Home technology from a local control system to one where many devices with high computational ability are network-enabled. These devices exhibit known and unknown attack surfaces by attackers either compromising the Smart Home itself or exploiting these devices to launch DDoS attacks. However, average home users often lack the technical skills to properly protect their networks and devices from those attacks. And manufacturers tend to design their systems for ease of use rather than ensuring security. Even for skilled home users, secure manual configuration and connection are time-consuming and error-prone. Therefore, it is important to develop a technique which is both simple to manage and secure against cyber-attacks. We present a secure auto-configurable gateway architecture (SAGA) that allows IoT devices to be automatically discovered, registered and configured when they connect to a Smart Home network. SAGA can (1) automatically register IoT devices securely, (2) reject malicious attempts by rogue devices to connect to the network, (3) identify attempts to capture devices by a rogue gateway and (4) provide services such as firewalls, address translation, device software updates, and host home control applications. Security analysis for the proposed architecture and details was performed. A prototype for SAGA was implemented and performance was evaluated in terms of execution/processing time.

The Internet of Things (IoT) is a network comprised of heterogeneous devices that can exchange data without requiring human-to-human or human-to-computer interactions. However, there are various vulnerabilities found due to the heterogeneity of the IoT network. Moreover, the mobility of IoT devices causes potential dynamic changes to the attack surfaces of IoT networks. As a result, static network security analysis approaches cannot capture these changes. In order to address this problem, we present an IoT security assessment approach by modelling different movement patterns of mobile IoT devices. Graphical security models are used in conjunction to evaluate the security of the IoT networks taking into account the mobility of the IoT devices. Further, we use various security metrics to analyze the security of the network to show the changing security posture when mobility is taken into account. The feasibility of the proposed approach is demonstrated by analyzing the security of an example mobile IoT network using three existing synthetic mobility models: Random Waypoint, Gauss-Markov and Reference Point Group. The experimental analysis shows the changing attack surface of the IoT networks when mobile devices are considered.

Conventional security risk assessment approaches for cloud infrastructures do not explicitly consider risk with respect to specific threats. This is a challenge for a cloud provider because it may apply the same risk assessment approach in assessing the risk of all of its clients. In practice, the threats faced by each client may vary depending on their security requirements. The cloud provider may also apply generic mitigation strategies that are not guaranteed to be effective in thwarting specific threats for different clients. This paper proposes a threat-specific risk assessment framework which evaluates the risk with respect to specific threats by considering only those threats that are relevant to a particular cloud client. The risk assessment process is divided into three phases which have inter-related activities arranged in a spiral. Application of the framework to a cloud deployment case study shows that considering risk with respect to specific threats leads to a more accurate quantification of security risk. Although our framework is motivated by risk assessment challenges in the cloud it can be applied in any network environment.

A Software Defined Network (SDN) provides functionalities for modifying network configurations. To enhance security, Moving Target Defense (MTD) techniques are deployed in the networks to continuously change the attack surface. In this paper, we realize an MTD system by exploiting the SDN functionality to optimally reconfigure the network topology. We introduce a novel problem Shuffle Assignment Problem (SAP), the reconfiguration of a network topology for enhanced security, and we show how to compute the optimal solution for small-sized networks and the near-optimal solution for large-sized networks using a heuristic method. In addition, we propose a shuffle-based online MTD mechanism, which periodically reconfigures the network topology to continuously change the attack surface. This mechanism also selects an optimal countermeasure using our proposed topological distance metric in real-time when an attack is detected. We demonstrate the feasibility and the effectiveness of our proposed solutions through experimental analysis on an SDN testbed and simulations.

Although most companies operate a firewall to protect their information assets, they have difficulties in identifying the control conditions of firewalls. This study proposes an analysis tool to visualize segment-based firewall rules to facilitate verification of the current control conditions. The proposed visualization tool analyzes the current control conditions of packets automatically, thereby eliminating the need for manual inspection as before, and displays the conditions with a visualization model to allow them to be easily verified. This enables managers to perform fast and accurate verification to assess whether packets are allowed or denied. This present study involved implementing the proposed visualization tool, and simulations were conducted to verify that the proposed approach was achievable. The present study also included conducting interviews with firewall experts whose feedback was positive. A video of the proposed visualization tool can be found on the following web site: https://youtu.be/q4HMnBvXbk.

Cyber crime is a developing concern, where criminals are targeting valuable assets and critical infrastructures within networked systems, causing a severe socio-economic impact on enterprises and individuals. Adopting moving target defense (MTD) helps thwart cyber attacks by continuously changing the attack surface. There are numerous MTD techniques proposed in various domains (e.g., virtualized network, wireless sensor network), but there is still a lack of methods to assess and compare the effectiveness of them. Security models, such as an attack graph (AG), provide a formal method of analyzing the security, but incorporating MTD techniques in those security models has not been studied. In this paper, we incorporate MTD techniques into a security model, namely a hierarchical attack representation model (HARM), to assess the effectiveness of them. In addition, we use importance measures (IMs) for deploying MTD techniques to enhance the scalability. Finally, we compare the scalability of AG and HARM when deploying MTD techniques, as well as changes in performance and security in our experiments.

Software failures are still a major concern in mission- and enterprise-critical contexts, despite significant efforts spent in software testing. In fact, while software testing is effective against easily-reproducible bugs (Bohrbugs), it is considerably less suitable for dealing with bugs that lead to hard-to-reproduce failures (Mandelbugs). On the positive side, the elusive nature of Mandelbugs provides opportunities for failure recovery, which are investigated in this paper. Based on real cases of Mandelbugs in eleven Information Technology (IT) systems running in production, the paper proposes a model that describes the recovery processes in IT systems. It then presents closed-form expressions, and a numerical analysis, of the mean time to recovery, and the software (un)availability. This analysis allows the designer to compare recovery strategies, as well as to determine the parameters having a high influence on the efficacy of recovery from failures caused by Mandelbugs.

Since security is increasingly the principal concern in the conception and implementation of software systems, it is very important that the security mechanisms are designed so as to protect the computer systems against cyber attacks. An Intrusion Tolerance Systems play a crucial role in maintaining the service continuity and enhancing the security compared with the traditional security. In this paper, we propose to combine a preventive maintenance with existing intrusion tolerance system to improve the system security. We use a semi-Markov process to model the system behavior. We quantitatively analyze the system security using the measures such as system availability, Mean Time To Security Failure and cost. The numerical analysis is presented to show the feasibility of the proposed approach.

Security assessment and mitigation have gained considerable attention over the recent years according to the information technology evolution and its broad adoption. Organizations are more aware of their data security, and they also have become more exigent in terms of extensibility and flexibility of their Information Technology infrastructures. Cloud computing is introduced as an evolution of information technology that offers major solutions and techniques to meet the evolving requirements of both tenants and clients. Therefore, extensibility and dynamic adjustment, which are among the most essential Cloud advantages, can make the security analysis a very hard task. There have been many approaches to analyze automatically the cyber security of traditional IT infrastructures without taking into account the new Cloud features, such as the nested virtualization. Until now, there is a few work to assess the security of Cloud computing. In this paper, we propose a novel approach to design and develop Model-based Automated Security Assessment Tool named MASAT.

Internet of Things (IoT) is enabling innovative applications in various domains. Due to its heterogeneous and wide scale structure, it introduces many new security issues. To address the security problem, we propose a framework for security modeling and assessment of the IoT. The framework helps to construct graphical security models for the IoT. Generally, the framework involves five steps to find attack scenarios, analyze the security of the IoT through well-defined security metrics, and assess the effectiveness of defense strategies. The benefits of the framework are presented via a study of two example IoT networks. Through the analysis results, we show the capabilities of the proposed framework on mitigating impacts of potential attacks and evaluating the security of large-scale networks.

Software Defined Network (SDN) is an emerging paradigm for flexible network design and implementation. Availability metric of SDNs is critically demanding further studies. This paper aims to propose hierarchical models to assess the availability of SDNs. We incorporate various failure modes and recovery behaviors in the SDN including (i) link failures at network level, and (ii) software and hardware failures at network device level. We use hierarchical models in which a Reliability Graph (RG) is used to represent the reachability of hosts (and switches) in the SDN at the upper level and Stochastic Reward Net (SRN)s are used to represent the detailed failure and recovery of network devices at the lower level, respectively. We incorporate the programmable capability of the SDN at the upper level (i.e., the RG). We perform numerical analysis to assess the availability of the SDN in terms of steady state availability and downtime in minutes per year, and we also show the sensitivity analysis.

With the rise in popularity of using websites to distribute content to users, content creators needed to gain revenue from page views. To achieve this, they use third-party networks to distribute advertisements and perform analytics on users to customize their advertisements. These networks build up a profile of their users (e.g., navigation patterns, visit frequency, personal interest), which poses a significant privacy risk as this information can be sold to others or used in targeted advertising. In order for users to protect themselves, they need to install add-ons for their web browser which removes third-party content. This paper surveys some of the existing add-ons to block third-party content, and determine which add-ons are required to provide adequate protection. Further analytics are conducted to evaluate the effectiveness of those add-ons through the experiments.

Moving Target Defense (MTD) changes the attack surface of a system that confuses intruders to thwart attacks. Various MTD techniques are developed to enhance the security of a networked system, but the effectiveness of these techniques is not well assessed. Security models (e.g., Attack Graphs (AGs)) provide formal methods of assessing security, but modeling the MTD techniques in security models has not been studied. In this paper, we incorporate the MTD techniques in security modeling and analysis using a scalable security model, namely Hierarchical Attack Representation Models (HARMs), to assess the effectiveness of the MTD techniques. In addition, we use importance measures (IMs) for scalable security analysis and deploying the MTD techniques in an effective manner. The performance comparison between the HARM and the AG is given. Also, we compare the performance of using the IMs and the exhaustive search method in simulations.

Computing a prioritized set of vulnerabilities to patch is important for system administrators to determine the order of vulnerabilities to be patched that are more critical to the network security. One way to assess and analyze security to find vulnerabilities to be patched is to use attack representation models (ARMs). However, security solutions using ARMs are optimized for only the current state of the networked system. Therefore, the ARM must reanalyze the network security, causing multiple iterations of the same task to obtain the prioritized set of vulnerabilities to patch. To address this problem, we propose to use importance measures to rank network hosts and vulnerabilities, then combine these measures to prioritize the order of vulnerabilities to be patched. We show that nearly equivalent prioritized set of vulnerabilities can be computed in comparison to an exhaustive search method in various network scenarios, while the performance of computing the set is dramatically improved, while equivalent solutions are computed in various network scenarios.

Constraints such as limited security investment cost precludes a security decision maker from implementing all possible countermeasures in a system. Existing analytical model-based security optimization strategies do not prevail for the following reasons: (i) none of these model-based methods offer a way to find optimal security solution in the absence of probability assignments to the model, (ii) methods scale badly as size of the system to model increases and (iii) some methods suffer as they use attack trees (AT) whose structure does not allow for the inclusion of countermeasures while others translate the non-state-space model (e.g., attack response tree) into a state-space model hence causing state-space explosion. In this paper, we use a novel AT paradigm called attack countermeasure tree (ACT) whose structure takes into account attacks as well as countermeasures (in the form of detection and mitigation events). We use greedy and branch and bound techniques to study several objective functions with goals such as minimizing the number of countermeasures, security investment cost in the ACT and maximizing the benefit from implementing a certain countermeasure set in the ACT under different constraints. We cast each optimization problem into an integer programming problem which also allows us to find optimal solution even in the absence of probability assignments to the model. Our method scales well for large ACTs and we compare its efficiency with other approaches.

ABSTRACT One of the main problems of existing approaches in anomaly detection in intrusion detection system (IDS) is that IDSs provide only binary detection result: intrusion (attack) or normal. If some attack data or normal data is belonged to boundary, they may be classified wrongly. That is a main cause of high false rates and inaccurate detection rates in IDS. We propose a new approach named Quantitative Intrusion Intensity Assessment (QIIA) that exploits proximity metrics computation so that it provides intrusion (or normal) quantitative intensity value. It is capable of representing how an instance of audit data is proximal to intrusion or normal in a numerical value. This can identify unknown intrusion and normal pattern more accurately. Prior to applying QIIA to audit data, we perform feature selection and parameter optimization of detection models to decrease the overheads to process audit data and to enhance detection rates. Random Forests is used to generate proximity metrics that represent the intrusion intensity (and normal instance intensity) in a numerical way. The numerical value is used to determine whether unknown audit data are intrusion or normal. We carry out several experiments on KDD 1999 dataset and the experimental results show the feasibility of our approach. Copyright © 2012 John Wiley & Sons, Ltd.

ABSTRACT Attack tree (AT) is one of the widely used non‐state‐space models for security analysis. The basic formalism of AT does not take into account defense mechanisms. Defense trees (DTs) have been developed to investigate the effect of defense mechanisms using measures such as attack cost, security investment cost, return on attack (ROA), and return on investment (ROI). DT, however, places defense mechanisms only at the leaf nodes and the corresponding ROI/ROA analysis does not incorporate the probabilities of attack. In attack response tree (ART), attack and response are both captured but ART suffers from the problem of state‐space explosion, since solution of ART is obtained by means of a state‐space model. In this paper, we present a novel attack tree paradigm called attack countermeasure tree (ACT) which avoids the generation and solution of a state‐space model and takes into account attacks as well as countermeasures (in the form of detection and mitigation events). In ACT, detection and mitigation are allowed not just at the leaf node but also at the intermediate nodes while at the same time the state‐space explosion problem is avoided in its analysis. We study the consequences of incorporating countermeasures in the ACT using three case studies (ACT for BGP attack, ACT for a SCADA attack and ACT for malicious insider attacks). Copyright © 2011 John Wiley & Sons, Ltd.

Server virtualization is a technology used in many enterprise systems to reduce operation and acquisition costs, and increase the availability of their critical services. Virtualized systems may be even more complex than traditional nonvirtualized systems; thus, the quantitative assessment of system availability is even more difficult. In this paper, we propose a sensitivity analysis approach to find the parameters that deserve more attention for improving the availability of systems. Our analysis is based on Markov reward models, and suggests that host failure rate is the most important parameter when the measure of interest is the system mean time to failure. For capacity oriented availability, the failure rate of applications was found to be another major concern. The results of both analyses were cross-validated by varying each parameter in isolation, and checking the corresponding change in the measure of interest. A cost-based optimization method helps to highlight the parameter that should have higher priority in system enhancement.

High-availability assurance of server systems is becoming an important issue, since many mission-critical applications are implemented on server systems. To achieve high-availability, software rejuvenation is a practical technique to reduce unexpected downtime caused by software aging in software applications running on server systems. Although analytic models of software rejuvenation are well-studied, such analysis is not used in server system administration due to the complexity of modeling. In this paper, we present an availability modeling method for server system with software rejuvenation based on SysML that is used to describe system configurations and maintenance operations semi-formally. The proposed approach allows system administrators, who do not have expertise in availability modeling, to design and study the effects of different rejuvenation policies deployed in server systems. To show the applicability of the proposed modeling and evaluation process, a case study of a web application server is presented. We show the correctness of our modeling method by comparing the conventional models for condition-based and time-based software rejuvenation.

Several studies have been carried out on software bugs analysis and classification for life and mission critical systems, which include reproducible bugs called Bohrbugs, and hard to reproduce bugs called Mandelbugs. Although software reliability in IT systems has been studied for years, there are only a few formal analytic models for recovery from Mandelbugs. This paper discusses in detail several real cases of Mandelbugs and presents a simple flowchart which describes the recovery processes implemented in IT systems for a large variety of Mandelbugs. The flowchart is based on more than 10 IT systems that are running in production. The paper then presents a closed-form expression of the mean time to recovery from these bugs. Measures of interest including mean time to recovery and system unavailability are computed. A numerical and parametric sensitivity analysis of the model parameters are carried out. This analysis allows the designer to find out important parameter(s) for the recovery from failures due to Mandelbugs.

High-availability assurance of cloud service is a critical and challenging issue for cloud service providers. To quantify the availability of cloud services from both architectural and operational points of views, availability modeling and evaluation are essential. This paper presents a component-based availability modeling framework, named Candy, which constructs a comprehensive availability model semi-automatically from system specifications described by Systems Modeling Language (SysML). SysML diagrams are translated into components of availability model and the components are assembled together to form the entire availability model in Stochastic Reward Nets (SRNs). In order to incorporate the maintenance operations of cloud services in availability models, Candy defines the translation rules from Activity diagram to SRN and synchronizes the related SRNs according to SysML allocation notations. The feasibility of the proposed modeling and availability evaluation process is studied by an illustrative example of a web application service hosted on a cloud infrastructure having multiple failure isolation zones and automatic scale-up function.

Spam is no more garbage but risk since it recently includes virus attachments and spyware agents which make the recipients’ system ruined, therefore, there is an emerging need for spam detection. Many spam detection techniques based on machine learning algorithms have been proposed. As the amount of spam has been increased tremendously using bulk mailing tools, spam detection techniques should deal with it. For spam detection, parameters optimization and feature selection have been proposed to reduce processing overheads with guaranteeing high detection rates. However, the previous approaches have not taken into account variable importance and optimal number of features and there are no approaches using both of them together so far. In this paper, we propose an optimal spam detection model based on Random Forests (RF) which enables parameters optimization and feature selection. We optimize two parameters of RF to maximize the detection rates. We provide the variable importance of each feature so that it is easy to eliminate the irrelevant features. Furthermore, we decide an optimal number of selected features using two methods; (i) only one parameters optimization during overall feature selection, (ii) parameters optimization in every feature elimination phase. We carry out experiments on the Spambase dataset and show the feasibility of our approach.

Handling diverse client demands and managing unexpected failures without degrading performance are two key promises of a cloud delivered service. However, evaluation of a cloud service quality becomes difficult as the scale and complexity of a cloud system increases. In a cloud environment, service request from a user goes through a variety of provider specific processing steps from the instant it is submitted until the service is fully delivered. Measurement-based evaluation of cloud service quality is expensive especially if many configurations, workload scenarios, and management methods are to be analyzed. To overcome these difficulties, in this paper we propose a general analytic model based approach for an end-to-end perform ability analysis of a cloud service. We illustrate our approach using Infrastructure-as-a-Service (IaaS) cloud, where service availability and provisioning response delays are two key QoS metrics. A novelty of our approach is in reducing the complexity of analysis by dividing the overall model into sub-models and then obtaining the overall solution by iteration over individual sub-model solutions. In contrast to a single one-level monolithic model, our approach yields a high fidelity model that is tractable and scalable. Our approach and underlying models can be readily extended to other types of cloud services and are applicable to public, private and hybrid clouds.

Prior to field deployment, mission critical sensor networks should be analyzed for high reliability assurance. Past research only focused on reliability models for sensor node or network in isolation. This paper presents a comprehensive approach for reliability analysis of a cluster-based sensor network. We use a three-level hierarchical model for sensor networks using fault trees and use Markov chains at the bottom level to model the reliability of individual sensor nodes. We summarize the developed models, showcase the initial numerical results and outline the future avenues of research in the following sections.

The term resilience is used differently by different communities. In general engineering systems, fast recovery from a degraded system state is often termed as resilience. Computer networking community defines it as the combination of trustworthiness (dependability, security, performability) and tolerance (survivability, disruption tolerance, and traffic tolerance). Dependable computing community defined resilience as the persistence of service delivery that can justifiably be trusted, when facing changes. In this paper, resilience definitions of systems and networks will be presented. Metrics for resilience will be compared with dependability metrics such as availability, performance, performability. Simple examples will be used to show quantification of resilience via probabilistic analytic models.

This paper develops an availability model of a virtualized system. We construct non-virtualized and virtualized two hosts system models using a two-level hierarchical approach in which fault trees are used in the upper level and homogeneous continuous time Markov chains (CTMC) are used to represent sub-models in lower level. In the models, we incorporate not only hardware failures (e.g., CPU, memory, power, etc) but also software failures including Virtual Machine Monitor (VMM), Virtual Machine (VM), and application failures. We also incorporate high availability (HA) service and VM live migration in the virtualized system. Metrics we use are system steady state availability, downtime in minutes per year and capacity oriented availability.

The problem of previous approaches in anomaly detection in Intrusion Detection System (IDS) is to provide only binary detection result; intrusion or normal. This is a main cause of high false rates and inaccurate detection rates in IDS. In this paper, we propose a new approach named Quantitative Intrusion Intensity Assessment (QIIA). QIIA exploits feature selection and proximity metrics computation so that it provides intrusion (or normal) quantitative intensity value. It is capable of representing how an instance of audit data is proximal to intrusion or normal in the form of a numerical value. Prior to applying QIIA to audit data, we perform feature selection and parameters optimization of detection model in order not only to decrease the overheads to process audit data but also to enhance detection rates. QIIA then is performed using Random Forest (RF) and it generates proximity metrics which represent the intrusion intensity in a numerical way. The numerical values are used to determine whether unknown audit data is intrusion or normal. We carry out several experiments on KDD 1999 dataset and show the evaluation results.

It is important to achieve energy efficient data mining in Wireless Sensor Networks (WSN) while preserving privacy of data. In this paper, we present a privacy preserving data mining based on Support Vector Machines (SVM). We review the previous approach in privacy preserving data mining in distributed system. And we also review energy efficient data mining in WSN. We then propose an energy efficient privacy preserving data mining in WSN. We use SVM because it has been shown best classification accuracy and sparse data presentation using support vectors. We show security analysis and energy estimation of our proposed approach.

In previous approaches, it's generally assumed that all Radio Frequency Identification (RFID) tags belong to a single RFID domain system (we name this as RFID single domain system). To date, most researches in the RFID single domain system have been on authentication protocols against a variety of attacks. This paper considers the security and privacy problems regarding that RFID tags used by different two or more RFID domains (we name this as RFID multi-domain system). We divided the security and privacy mechanisms in RFID multi-domain system into 3 conceptual parts: RFID forehand system security, RFID backend system security, and RFID inter-domain system security. First, we review RFID forehand and backend system security issues. Second, we present a security framework in RFID multi-domain system. Third, we propose and evaluate authentication and authorization for RFID inter-domain system with a case study.

In this paper, we propose adaptation mechanisms for sensor networks. The existing researches on security for sensor networks have mostly concerned with confidentiality, integrity and authentication based on cryptographic mechanisms. These aspects of security are important but not enough to provide survivability of sensor networks. In this paper, we employ the software rejuvenation and reconfiguration methodology to increase the survivability of sensor nodes in sensor networks. The model uses global or local intrusion detection systems and performs 4 strategies to extend the availability of sensor nodes in sensor networks. We propose a general framework and its methodology and the security analysis and evaluation results show the feasibility of our approach.

Wireless sensor network (WSN) should be capable of fulfilling its mission, in a timely manner, in the middle of intrusion, attacks, accidents and failures in hostile environment. However, current security mechanisms for WSN are able to satisfy confidentiality, integrity, and authentication properties using cipher algorithms, key management schemes, and so on, but they are not enough to meet above requirements. Therefore, we propose a framework of survivability model for WSN. Our model adopts software rejuvenation methodology, which is applicable in security field and also less expensive. We model and analyze each cluster of a hierarchical cluster based WSN as a stochastic process based on semi-Markov process (SMP) and discrete-time Markov chain (DTMC). The model analysis indicates the feasibility of our approach.

In this paper, we propose genetic algorithm (GA) to improve support vector machines (SVM) based intrusion detection system (IDS). SVM is relatively a novel classification technique and has shown higher performance than traditional learning methods in many applications. So several security researchers have proposed SVM based IDS. We use fusions of GA and SVM to enhance the overall performance of SVM based IDS. Through fusions of GA and SVM, the "optimal detection model" for SVM classifier can be determined. As the result of this fusion, SVM based IDS not only select "optimal parameters "for SVM but also "optimal feature set" among the whole feature set. We demonstrate the feasibility of our method by performing several experiments on KDD 1999 intrusion detection system competition dataset.