Biblio

Microservice architectures adoption is growing expeditiously in market size and adoption, including in business-critical systems. This is due to agility in development and deployment further increased by containers and their characteristics. Ensuring security is still a major concern due to challenges faced such as resource separation and isolation, as improper access to one service might compromise complete systems. This doctoral work intends to advance the security of microservice systems through research and improvement of methodologies for detection, tolerance and mitigation of security intrusions, while overcoming challenges related to multi-tenancy, heterogeneity, dynamicity of systems and environments. Our preliminary research shows that host-based IDSes are applicable in container environments. This will be extended to dynamic scenarios, serving as a steppingstone to research intrusion tolerance techniques suited to these environments. These methodologies will be demonstrated in realistic microservice systems: complex, dynamic, scalable and elastic.

Multi-agent architectures have been successful in attaining considerable attention among computer security researchers. This is so, because of their demonstrated capabilities such as autonomy, embedded intelligence, learning and self-growing knowledge-base, high scalability, fault tolerance, and automatic parallelism. These characteristics have made this technology a de facto standard for developing ambient security systems to meet the open and dynamic nature of today's online communities. Although multi-agent architectures are increasingly studied in the area of computer security, there is still not enough empirical evidence on their performance in intrusions and attacks detection. The aim of this paper is to report the systematic literature review conducted in the context of specific research questions, to investigate multi-agent IDS architectures to highlight the issues that affect their performance in terms of detection accuracy and response time. We used pertinent keywords and terms to search and retrieve the most recent research studies, on multi-agent IDS architectures, from the major research databases and digital libraries such as SCOPUS, Springer, and IEEE Explore. The search processes resulted in a number of studies; among them, there were journal articles, book chapters, conference papers, dissertations, and theses. The obtained studies were assessed and filtered out, and finally, there were over 71 studies chosen to answer the research questions. The results of this study have shown that multi-agent architectures include several advantages that can help in the development of ambient IDS. However, it has been found that there are several issues in the current multi-agent IDS architectures that may degrade the accuracy and response time of intrusions and attacks detection. Based on our findings, the issues of multi-agent IDS architectures include limitations in the techniques, mechanisms, and schemes used for multi-agent IDS adaptation and learning, load balancing, scalability, fault-tolerance, and high communication overhead. It has also been found that new measurement metrics are required for evaluating multi-agent IDS architectures.

Many organizations are still reluctant to move sensitive data to the cloud. Moreover, data protection regulations have established considerable punishments for violations of privacy and security requirements. Privacy, however, is a concept that is difficult to measure and to demonstrate. While many privacy design strategies, tactics and patterns have been proposed for privacy-preserving system design, it is difficult to evaluate an existing system with regards to whether these strategies have or have not appropriately been implemented. In this paper we propose indicators for a system's non-compliance with privacy design strategies, called privacy smells. To that end we first identify concrete metrics that measure certain aspects of existing privacy design strategies. We then define smells based on these metrics and discuss their limitations and usefulness. We identify these indicators on two levels of a cloud system: the data flow level and the access control level. Using a cloud system built in Microsoft Azure we show how the metrics can be measured technically and discuss the differences to other cloud providers, namely Amazon Web Services and Google Cloud Platform. We argue that while it is difficult to evaluate the privacy-awareness in a cloud system overall, certain privacy aspects in cloud systems can be mapped to useful metrics that can indicate underlying privacy problems. With this approach we aim at enabling cloud users and auditors to detect deep-rooted privacy problems in cloud systems.

Internet of Medical Things (IoMT) are getting popular in the smart healthcare domain. These devices are resource-constrained and are vulnerable to attack. As the IoMTs are connected to the healthcare network infrastructure, it becomes the primary target of the adversary due to weak security and privacy measures. In this regard, this paper proposes a security architecture for smart healthcare network infrastructures. The architecture uses various security components or services that are developed and deployed as virtual network functions. This makes the security architecture ready for future network frameworks such as OpenMANO. Besides, in this security architecture, only authenticated and trusted IoMTs serve the patients along with an encryption-based communication protocol, thus creating a secure, privacy-preserving and trusted healthcare network infrastructure.

With the widespread of Artificial Intelligence (AI)-enabled security applications, there is a need for collecting heterogeneous and scalable data sources for effectively evaluating the performances of security applications. This paper presents the description of new datasets, named ToNİoT datasets that include distributed data sources collected from Telemetry datasets of Internet of Things (IoT) services, Operating systems datasets of Windows and Linux, and datasets of Network traffic. The paper aims to describe the new testbed architecture used to collect Linux datasets from audit traces of hard disk, memory and process. The architecture was designed in three distributed layers of edge, fog, and cloud. The edge layer comprises IoT and network systems, the fog layer includes virtual machines and gateways, and the cloud layer includes data analytics and visualization tools connected with the other two layers. The layers were programmatically controlled using Software-Defined Network (SDN) and Network-Function Virtualization (NFV) using the VMware NSX and vCloud NFV platform. The Linux ToNİoT datasets would be used to train and validate various new federated and distributed AI-enabled security solutions such as intrusion detection, threat intelligence, privacy preservation and digital forensics. Various Data analytical and machine learning methods are employed to determine the fidelity of the datasets in terms of examining feature engineering, statistics of legitimate and security events, and reliability of security events. The datasets can be publicly accessed from [1].

An Internet of Things (IoT) architecture comprised of cloud, fog and resource constrained IoT end devices. The exponential development of IoT has increased the processing and footprint overhead in IoT end devices. All the components of IoT end devices that establish Chain of Trust (CoT) to ensure security are termed as Trusted Computing Base (TCB). The increased overhead in the IoT end device has increased the demand to increase the size of TCB surface area hence increases complexity of TCB surface area and also the increased the visibility of TCB surface area to the external world made the IoT end devices architecture over-architectured and unsecured. The TCB surface area minimization that has been remained unfocused reduces the complexity of TCB surface area and visibility of TCB components to the external un-trusted world hence ensures security in terms of confidentiality, integrity, authenticity (CIA) at the IoT end devices. The TCB minimization thus will convert the over-architectured IoT end device into lightweight and secured architecture highly desired for resource constrained IoT end devices. In this paper we review the IoT end device architectures proposed in the recent past and concluded that these architectures of resource constrained IoT end devices are over-architectured due to larger TCB and ignored bugs and vulnerabilities in TCB hence un-secured. We propose the Novel levelled architecture with TCB minimization by replacing oversized hypervisor with lightweight Micro(μ)-hypervisor i.e. μ-visor and transferring μ-hypervisor based virtualization over fog node for light weight and secured IoT End device architecture. The bug free TCB components confirm stable CoT for guaranteed CIA resulting into robust Trusted Execution Environment (TEE) hence secured IoT end device architecture. Thus the proposed resulting architecture is secured with minimized SRAM and flash memory combined footprint 39.05% of the total available memory per device. In this paper we review the IoT end device architectures proposed in the recent past and concluded that these architectures of resource constrained IoT end devices are over-architectured due to larger TCB and ignored bugs and vulnerabilities in TCB hence un-secured. We propose the Novel levelled architecture with TCB minimization by replacing oversized hypervisor with lightweight Micro(μ)-hypervisor i.e. μ-visor and transferring μ-hypervisor based virtualization over fog node for light weight and secured IoT End device architecture. The bug free TCB components confirm stable CoT for guaranteed CIA resulting into robust Trusted Execution Environment (TEE) hence secured IoT end device architecture. Thus the proposed resulting architecture is secured with minimized SRAM and flash memory combined footprint 39.05% of the total available memory per device.

In the Ubiquitous Electric Internet of Things (UEIoT), the terminals are very easy to be accessed and attacked by attackers due to the lack of effective monitoring and safe isolation methods. Therefore, in the implementation of UEIoT, the security protection of terminals is particularly important. Therefore, this paper proposes a dual-system design scheme for terminal active immunity based on trusted computing. In this scheme, the terminal node in UEIoT is composed of two parts: computing part and trusted protection part. The computing component and the trusted protection component are logically independent of each other, forming a trusted computing active immune dual-system structure with both computing and protection functions. The Trusted Network Connection extends the trusted state of the terminal to the network, thus providing a solution for terminal secure access in the UEIoT.

The recent advances in mobile devices and wireless communication sector transformed Mobile Augmented Reality (MAR) from science fiction to reality. Among the other MAR use cases, the incorporation of this MAR technology in the healthcare sector can elevate the quality of diagnosis and treatment for the patients. However, due to the highly sensitive nature of the data available in this process, it is also highly vulnerable to all types of security threats. In this paper, an edge-based secure architecture is presented for a MAR healthcare application. Based on the ESSMAR architecture, a secure key management scheme is proposed for both the registration and authentication phases. Then the security of the proposed scheme is validated using formal and informal verification methods.

The increasing vehicles have brought convenience to people as well as many traffic problems. The Internet of Vehicles (IoV) is an extension of the intelligent transportation system based on the Internet of Things (IoT), which is the omnibearing network connection among “Vehicles, Loads, Clouds”. However, IoV also faces threats from various known and unknown security vulnerabilities. Traditional security defense methods can only deal with known attacks, while there is no effective way to deal with unknown attacks. In this paper, we show an IoV system deployed on a Mimic Common Operating Environment (MCOE). At the sensing layer, we introduce a lightweight cryptographic algorithm, LBlock, to encrypt the data collected by the hardware. Thus, we can prevent malicious tampering of information such as vehicle conditions. At the application layer, we firstly put the IoV system platform into MCOE to make it dynamic, heterogeneous and redundant. Extensive experiments prove that the sensing layer can encrypt data reliably and energy-efficiently. And we prove the feasibility and security of the Internet of Vehicles system platform on MCOE.

With the development of business, management companies are currently facing a series of problems and challenges in terms of resource allocation and task management. In terms of the technical route, this research will use cloud services to implement the public honesty system, and carry out secondary development and interface development on this basis, the architecture design and the formulation of the process are realized for various types, relying on the support of the knowledge base and case library, through the system intelligent configuration corresponding work instructions, safety work instructions, case references and other reference information to the existing work plan to provide managers Reference; managers can configure and adjust the work content by themselves through specific requirements to efficiently and flexibly adapt to the work content.

Fog Computing is one of the edge computing paradigms that envisages being the proximate processing and storage infrastructure for a multitude of IoT appliances. With its dynamic deployability as a medium level cloud service, fog nodes are enabling heterogeneous service provisioning infrastructure that features scalability, interoperability, and adaptability. Out of the various 5G based services possible with the fog computing platforms, security services are imperative but minimally investigated direct live. Thus, in this research, we are focused on launching security services in a fog node with an architecture capable of provisioning on-demand service requests. As the fog nodes are constrained on resources, our intention is to integrate light-weight virtualization technology such as Docker for forming the service provisioning infrastructure. We managed to launch multiple security instances configured to be Intrusion Detection and Prevention Systems (IDPSs) on the fog infrastructure emulated via a Raspberry Pi-4 device. This environment was tested with multiple network flows to validate its feasibility. In our proposed architecture, orchestration strategies performed by the security orchestrator were stated as guidelines for achieving pragmatic, dynamic orchestration with fog in IoT deployments. The results of this research guarantee the possibility of developing an ambient security service model that facilitates IoT devices with enhanced security.

The Fog is an emergent computing architecture that will support the mobility and geographic distribution of Internet of Things (IoT) nodes and deliver context-aware applications with low latency to end-users. It forms an intermediate layer between IoT devices and the Cloud. However, Fog computing brings many requirements that increase the cost of security management. It inherits the security and trust issues of Cloud and acquires some of the vulnerable features of IoT that threaten data and application confidentiality, integrity, and availability. Several existing solutions address some of the security challenges following adequate adaptation, but others require new and innovative mechanisms. These reflect the need for a Fog architecture that provides secure access, efficient authentication, reliable and secure communication, and trust establishment among IoT devices and Fog nodes. The Fog might be more convenient to deploy decentralized authentication solutions for IoT than the Cloud if appropriately designed. In this short survey, we highlight the Fog security challenges related to IoT security requirements and architectural design. We conduct a comparative study of existing Fog architectures then perform a critical analysis of different authentication schemes in Fog computing, which confirms some of the fundamental requirements for effective authentication of IoT devices based on the Fog, such as decentralization, less resource consumption, and low latency.

Budding technologies like IoT requires minimum latency for performing real-time applications. The IoT devices collect a huge amount of big data and stores in the cloud environment, because of its on-demand services and scalability. But processing the needed information of the IoT devices from the cloud computing environment is found to be time-sensitive one. To eradicate this issue fog computing environment was created which acts an intermediate between the IoT devices and cloud computing environment. The fog computing performs intermediate computation and storage which is needed by IoT devices and it eliminates the drawbacks of latency and bandwidth limitation faced by directly using cloud computing for storage and accessing. The fog computing even though more advantageous it is more exposed to security issues by its architecture. This paper concentrates more on the security issues met by fog computing and the present methods used by the researchers to secure fog with their pros and cons.

Fog computing or edge computing or fogging extends cloud computing to the edge of the network. It operates on the computing, storage and networking services between user-end devices and cloud computing data centres. However, in the process of caring out these operations, fog computing is faced with several security issues. These issues may be inherited from cloud computing systems or may arise due to fog computing systems alone. Some of the major gaps in providing a secure platform for the fog computing process arise from interim operational steps like authentication or identification, which often expands to large scale performance issues in fog computing. Thus, these issues and their implications on fog computing databases, and the possible available solutions are researched and provided for a better scope of future use and growth of fog computing systems by bridging the gaps of security issues in it.

With the growing numbers of security attacks causing more and more serious damages in software systems, security cannot be added as an afterthought in software development. It has to be built in from the early development phases such as requirement and design. The role responsible for designing a software system is termed an “architect”, knowledgeable about the system architecture design, but not always well-trained in security. Moreover, involving other security experts into the system design is not always possible due to time-to-market and budget constraints. To address these challenges, we propose to define an asset-based security assistance in this paper, to help architects design secure systems even if these architects have limited knowledge in security. This assistance helps alert threats, and integrate the security controls over vulnerable parts of system into the architecture model. The central concept enabling this assistance is that of asset. We apply our proposal on a telemonitoring case study to show that automating such an assistance is feasible.

As the increasing demands of social services, unmanned aerial vehicles (UAVs)-assisted networks promote the promising prospect for implementing high-rate information transmission and applications. The sensing data can be collected by UAVs, a large number of applications based on UAVs have been realized in the 5G networks. However, the malicious UAVs may provide false information and destroy the services. The 5G UAV communication systems face the security threats. Therefore, this paper develops a novel trust-based security scheme for 5G UAV communication systems. Firstly, the architecture of the 5G UAV communication system is presented to improve the communication performance. Secondly, the trust evaluation scheme for UAVs is developed to evaluate the reliability of UAVs. By introducing the trust threshold, the malicious UAVs will be filtered out from the systems to protect the security of systems. Finally, the simulation results have been demonstrated the effectiveness of the proposed scheme.

Differentially private location trace synthesis (DPLTS) has recently emerged as a solution to protect mobile users' privacy while enabling the analysis and sharing of their location traces. A key challenge in DPLTS is to best preserve the utility in location trace datasets, which is non-trivial considering the high dimensionality, complexity and heterogeneity of datasets, as well as the diverse types and notions of utility. In this paper, we present OptaTrace: a utility-optimized and targeted approach to DPLTS. Given a real trace dataset D, the differential privacy parameter ε controlling the strength of privacy protection, and the utility/error metric Err of interest; OptaTrace uses Bayesian optimization to optimize DPLTS such that the output error (measured in terms of given metric Err) is minimized while ε-differential privacy is satisfied. In addition, OptaTrace introduces a utility module that contains several built-in error metrics for utility benchmarking and for choosing Err, as well as a front-end web interface for accessible and interactive DPLTS service. Experiments show that OptaTrace's optimized output can yield substantial utility improvement and error reduction compared to previous work.

Space-air-ground integrated network (SAGIN) is emerged as a versatile computing and traffic architecture in recent years. Though SAGIN brings many significant benefits for modern communication and computing services, there are many unprecedented challenges in SAGIN. The one critical challenge in SAGIN is the data security. In SAGIN, because the data will be stored in cleartext on cloud, the sensitive data may suffer from the illegal access by the unauthorized users even the untrusted cloud servers (CSs). Ciphertext-policy attribute-based encryption (CP-ABE), which is a type of attribute-based encryption (ABE), has been regarded as a promising solution to the critical challenge of the data security on cloud. But there are two main blemishes in traditional CP-ABE. The first one is that there is only one attribute authority (AA) in CP-ABE. If the single AA crashs down, the whole system will be shut down. The second one is that the AA cannot effectively manage the life cycle of the users’ private keys. If a user on longer has one attribute, the AA cannot revoke the user’s private key of this attribute. This means the user can still decrypt some ciphertexts using this invalid attribute. In this paper, to solve the two flaws mentioned above, we propose a multi-authority CP-ABE (MA-CP-ABE) scheme with the dynamical key revocation (DKR). Our key revocation supports both user revocation and attribute revocation. And the our revocation is time friendly. What’s more, by using our dynamically tag-based revocation algorithm, AAs can dynamically and directly re-enable or revoke the invalid attributes to users. Finally, by evaluating and implementing our scheme, we can observe that our scheme is more comprehensive and practical for cloud applications in SAGIN.

This paper presents Foggy, an anonymous communication system focusing on providing users with anonymous web browsing. Foggy provides a microservice-based proxy for web browsing and other low-latency network activities without exposing users' metadata and browsed content to adversaries. It is designed with decentralized information management, web caching, and configurable service selection. Although Foggy seems to be more centralized compared with Tor, it gains an advantage in manageability while retaining anonymity. Foggy can be deployed by several agencies to become more decentralized. We prototype Foggy and test its performance. Our experiments show Foggy's low latency and deployability, demonstrating its potential to be a commercial solution for real-world deployment.

The new generation of digital services are natively conceived as an ordered set of Virtual Network Functions, deployed across boundaries and organizations. In this context, security threats, variable network conditions, computational and memory capabilities and software vulnerabilities may significantly weaken the whole service chain, thus making very difficult to combat the newest kinds of attacks. It is thus extremely important to conceive a flexible (and standard-compliant) framework able to attest the trustworthiness and the reliability of each single function of a Service Function Chain. At the time of this writing, and to the best of authors knowledge, the scientific literature addressed all of these problems almost separately. To bridge this gap, this paper proposes a novel methodology, properly tailored within the ETSI-NFV framework. From one side, Software-Defined Controllers continuously monitor the properties and the performance indicators taken from networking domains of each single Virtual Network Function available in the architecture. From another side, a high-level orchestrator combines, on demand, the suitable Virtual Network Functions into a Service Function Chain, based on the user requests, targeted security requirements, and measured reliability levels. The paper concludes by further explaining the functionalities of the proposed architecture through a use case.

Recent technological advancements have enabled proliferated use of small embedded and IoT devices for collecting, processing, and transferring the security-critical information and user data. This exponential use has acted as a catalyst in the recent growth of sophisticated attacks such as the replay, man-in-the-middle, and malicious code modification to slink, leak, tweak or exploit the security-critical information in malevolent activities. Therefore, secure communication and software state assurance (at run-time and boot-time) of the device has emerged as open security problems. Furthermore, these devices need to have an appropriate recovery mechanism to bring them back to the known-good operational state. Previous researchers have demonstrated independent methods for attack detection and safeguard. However, the majority of them lack in providing onboard system recovery and secure communication techniques. To bridge this gap, this manuscript proposes SRACARE - a framework that utilizes the custom lightweight, secure communication protocol that performs remote/local attestation, and secure boot with an onboard resilience recovery mechanism to protect the devices from the above-mentioned attacks. The prototype employs an efficient lightweight, low-power 32-bit RISC-V processor, secure communication protocol, code authentication, and resilience engine running on the Artix 7 Field Programmable Gate Array (FPGA) board. This work presents the performance evaluation and state-of-the-art comparison results, which shows promising resilience to attacks and demonstrate the novel protection mechanism with onboard recovery. The framework achieves these with only 8% performance overhead and a very small increase in hardware-software footprint.

Remote Attestation (RA) is a security service that detects malware presence on remote IoT devices by verifying their software integrity by a trusted party (verifier). There are three main types of RA: software (SW)-, hardware (HW)-, and hybrid (SW/HW)-based. Hybrid techniques obtain secure RA with minimal hardware requirements imposed on the architectures of existing microcontrollers units (MCUs). In recent years, considerable attention has been devoted to hybrid techniques since prior software-based ones lack concrete security guarantees in a remote setting, while hardware-based approaches are too costly for low-end MCUs. However, one key problem is that many already deployed IoT devices neither satisfy minimal hardware requirements nor support hardware modifications, needed for hybrid RA. This paper bridges the gap between software-based and hybrid RA by proposing a novel RA scheme based on software virtualization. In particular, it proposes a new scheme, called SIMPLE, which meets the minimal hardware requirements needed for secure RA via reliable software. SIMPLE depends on a formally-verified software-based memory isolation technique, called Security MicroVisor (Sμ V). Its reliability is achieved by extending the formally-verified safety and correctness properties to cover the entire software architecture of SIMPLE. Furthermore, SIMPLE is used to construct SIMPLE+, an efficient swarm attestation scheme for static and dynamic heterogeneous IoT networks. We implement and evaluate SIMPLE and SIMPLE+ on Atmel AVR architecture, a common MCU platform.

We propose a novel attestation architecture for the Internet of Things (IoT). Our distributed attestation network (DAN) utilizes blockchain technology to store and share device information. We present the design of this new attestation architecture as well as a prototype system chosen to emulate an IoT deployment with a network of Raspberry Pi, Infineon TPMs, and a Hyperledger Fabric blockchain.

In recent years, we have seen an advent in software attestation defenses targeting embedded systems which aim to detect tampering with a device's running program. With a persistent threat of an increasingly powerful attacker with physical access to the device, attestation approaches have become more rooted into the device's hardware with some approaches even changing the underlying microarchitecture. These drastic changes to the hardware make the proposed defenses hard to apply to new systems. In this paper, we present and evaluate LAHEL as the means to study the implementation and pitfalls of a hardware-based attestation mechanism. We limit LAHEL to utilize existing technologies without demanding any hardware changes. We implement LAHEL as a hardware IP core which interfaces with the CoreSight Debug Architecture available in modern ARM cores. We show how LAHEL can be integrated to system on chip designs allowing for microcontroller vendors to easily add our defense into their products. We present and test our prototype on a Zynq-7000 SoC, evaluating the security of LAHEL against powerful time-of-check-time-of-use (TOCTOU) attacks, while demonstrating improved performance over existing attestation schemes.

Security has always been one of the main concerns in the field of computer architecture and cloud computing. Cache-based side-channel attacks pose a threat to almost all existing architectures and cloud computing. Especially in the public cloud, the cache is shared among multiple tenants, and cache attacks can make good use of this to extract information. Cache side-channel attacks are a problem to be solved for security, in which how to accurately detect cache side-channel attacks has been a research hotspot. Because the cache side-channel attack does not require the attacker to physically contact the target device and does not need additional devices to obtain the side channel information, the cache-side channel attack is efficient and hidden, which poses a great threat to the security of cryptographic algorithms. Based on the AES algorithm, this paper uses hardware performance counters to obtain the features of different cache events under Flush + Reload, Prime + Probe, and Flush + Flush attacks. Firstly, the random forest algorithm is used to filter the cache features, and then the support vector machine algorithm is used to model the system. Finally, high detection accuracy is achieved under different system loads. The detection accuracy of the system is 99.92% when there is no load, the detection accuracy is 99.85% under the average load, and the detection accuracy under full load is 96.57%.