In the face of an increasing attack landscape, it is necessary to cater for efficient mechanisms to verify software and device integrity for detecting run-time modifications in next-generation systems-of-systems. In this context, remote attestation is a promising defense mechanism that allows a third party, the verifier, to ensure a remote device’s configuration integrity and behavioural execution correctness. However, most of the existing families of attestation solutions suffer from the lack of software-based mechanisms for the efficient extraction of rigid control-flow information. This limits their applicability to only those cyber-physical systems equipped with additional hardware support. This paper proposes a multi-level execution tracing framework capitalizing on recent software features, namely the extended Berkeley Packet Filter and Intel Processor Trace technologies, that can efficiently capture the entire platform configuration and control-flow stacks, thus, enabling wide attestation coverage capabilities that can be applied on both resource-constrained devices and cloud services. Our goal is to enhance run-time software integrity and trustworthiness with a scalable tracing solution eliminating the need for federated infrastructure trust.

Embedded systems that make up the Internet of Things (IoT), Supervisory Control and Data Acquisition (SCADA) networks, and Smart Grid applications are coming under increasing scrutiny in the security field. Remote Attestation (RA) is a security mechanism that allows a trusted device, the verifier, to determine the trustworthiness of an untrusted device, the prover. RA has become an area of high interest in academia and industry and many research works on RA have been published in recent years. This paper reviewed the published RA research works from 2003-2020. Our contributions are fourfold. First, we have re-framed the problem of RA into 5 smaller problems: root of trust, evidence type, evidence gathering, packaging and verification, and scalability. We have provided a holistic review of RA by discussing the relationships between these problems and the various solutions that exist in modern RA research. Second, we have presented an enhanced threat model that allows for a greater understanding of the security benefits of a given RA scheme. Third, we have proposed a taxonomy to classify and analyze RA research works and use it to categorize 58 RA schemes reported in literature. Fourth, we have provided cost benefit analysis details of each RA scheme surveyed such that security professionals may perform a cost benefit analysis in the context of their own challenges. Our classification and analysis has revealed areas of future research that have not been addressed by researchers rigorously.

Remote Attestation (RA) is a security service by which a Verifier (Vrf) can verify the platform state of a remote Prover (Prv). However, in most existing RA schemes, the Prv might be vulnerable to denial of service (DoS) attacks due to the interactive challenge-response methodology while there is no authentication about the challenge. Worse, many schemes cannot effectively detect mobile malware that can be inactive during the on-demand attestation launched by the Vrf. In this paper, we propose a self-measurement RA for SGX-based platforms, which can effectively mitigate DoS attacks and defend against mobile malware. To this end, a two-way identity authentication is first enforced between the Prv and Vrf with the help of a blockchain system, in which a shared session key is also generated. Secondly, trigger conditions of measurements on the Prv’s side are time points generated by the Prv self instead of Vrf’s requests. The Vrf can retrieve multiple selfmeasurement results during one execution of the protocol to monitor the Prv’s platform over a period of time continuously, which can detect mobile malware effectively. Our scheme utilizes SGX to provide the runtime protection for sensitive information such as session key, self-measurement code, time points of self-measurements, and self-measurement results, making a higher security guarantee. In addition, the session key, time points of self-measurements, and self-measurement code can be changed or upgraded, making our scheme more flexible and scalable. The simulation implementation and results show that our scheme is feasible and practical.

Trusted data transmission is the foundation of the Internet of Things (IoT) security, so in the process of data transmission, the trust of IoT nodes needs to be confirmed in real time, and the real-time tracking of node trust is also expected. Yet, modern IoT devices provide limited security capabilities, forming a new attack focus. Remote attestation is a kind of technology to detect network threats by remotely checking the internal situation of terminal devices by a trusted entity. Multidevice attestation is rarely studied although the ongoing single device attestation techniques lack scalability in the application of IoT. In this article, we present a lightweight attestation protocol based on an IoT system under an ideal physical unclonable functions environment. Our protocol can resilient against any strong adversary who physically accesses IoT devices. Simulation results show that our protocol is scalable and can be applied to dynamic networks.

Due to the concern on cloud security, digital encryption is applied before outsourcing data to the cloud for utilization. This introduces a challenge about how to efficiently perform queries over ciphertexts. Crypto-based solutions currently suffer from limited operation support, high computational complexity, weak generality, and poor verifiability. An alternative method that utilizes hardware-assisted Trusted Execution Environment (TEE), i.e., Intel SGX, has emerged to offer high computational efficiency, generality and flexibility. However, SGX-based solutions lack support on multi-user query control and suffer from security compromises caused by untrustworthy TEE function invocation, e.g., key revocation failure, incorrect query results, and sensitive information leakage. In this article, we leverage SGX and propose a secure and efficient SQL-style query framework named QShield. Notably, we propose a novel lightweight secret sharing scheme in QShield to enable multi-user query control; it effectively circumvents key revocation and avoids cumbersome remote attestation for authentication. We further embed a trust-proof mechanism into QShield to guarantee the trustworthiness of TEE function invocation; it ensures the correctness of query results and alleviates side-channel attacks. Through formal security analysis, proof-of-concept implementation and performance evaluation, we show that QShield can securely query over outsourced data with high efficiency and scalable multi-user support.

One of the important characteristics envisioned for 6G is security function virtualization (SFV). Similar to network function virtualization (NFV) in 5G networks, SFV provides new opportunities for improving security while reducing the security overhead. In particular, it provides an attractive way of solving compatibility issues related to security. Malware in Internet of Things (IoT) systems is gaining popularity among cyber-criminals because of the expected number of IoT devices in 5G and 6G networks. To solve this issue, this article proposes a security framework that exploits softwarization of security functions via SFV to improve trust in IoT systems and contain the propagation of malware. IoT devices are categorized into trusted, vulnerable, and compromised levels using remote attestation. To isolate the devices in the three distinct categories, NFV is used to create separate networks for each category, and a distributed ledger is used to store the state of each device. Virtualized remote attestation routines are employed to avoid any compatibility issues among heterogeneous IoT devices and effectively contain malware propagation. The results show that the proposed framework can reduce the number of infected devices by 66 percent in only 10 seconds.

Advances in wireless networking, such as 5G, continue to enable the vision of the Internet of Things (IoT), where everything is connected, and much data is collected by IoT devices and made available to interested parties (i.e., application servers). However, events such as botnet attacks (e.g., [1]) demonstrate that there are important challenges in this evolution.

The releases of Intel SGX and AMD SEV mark the transition of hardware-based enclaves from research prototypes to mainstream products. These two paradigms of secure enclaves are attractive to both the cloud providers and tenants, since security is one of the key pillars of cloud computing. However, it is found that current hardware-defined enclaves are not flexible and efficient enough for the cloud. For example, although SGX can provide strong memory protection with both confidentiality and integrity, the size of secure memory is tightly restricted. On the contrary, SEV enables enclaves to use more memory but has critical security flaws due to no memory integrity protection. Meanwhile, both types of enclaves have relatively long booting latency, which makes them not suitable for short-term tasks like serverless workloads. After an in-depth analysis, we find that there are some intrinsic tradeoffs between security and performance due to the limitation of architectural designs. In this article, we investigate a novel hardware-software co-design of enclaves to meet the requirements of cloud by placing a part of the logic of the enclave mechanism into a lightweight software layer, named Enclavisor, to achieve a balance between security, performance, and flexibility. Specifically, our implementation is based on AMD’s SEV and, Enclavisor is placed in the guest kernel mode of SEV’s secure virtual machines. Enclavisor inherently supports memory encryption with no memory limitation and also achieves efficient booting, multiple enclave granularities, and post-launch remote attestation. Meanwhile, we also propose hardware/ software solutions to mitigate the security flaws caused by the lack of memory integrity. We implement a prototype of Enclavisor on an AMD SEV server. The experiments on both micro-benchmarks and application benchmarks show that enclaves on Enclavisor can have close-to-native performance.

In the face of an increasing attack landscape, it is necessary to cater for efficient mechanisms to verify software and device integrity for detecting run-time modifications in next-generation systems-of-systems. In this context, remote attestation is a promising defense mechanism that allows a third party, the verifier, to ensure a remote device’s configuration integrity and behavioural execution correctness. However, most of the existing families of attestation solutions suffer from the lack of software-based mechanisms for the efficient extraction of rigid control-flow information. This limits their applicability to only those cyber-physical systems equipped with additional hardware support. This paper proposes a multi-level execution tracing framework capitalizing on recent software features, namely the extended Berkeley Packet Filter and Intel Processor Trace technologies, that can efficiently capture the entire platform configuration and control-flow stacks, thus, enabling wide attestation coverage capabilities that can be applied on both resource-constrained devices and cloud services. Our goal is to enhance run-time software integrity and trustworthiness with a scalable tracing solution eliminating the need for federated infrastructure trust.

Embedded systems that make up the Internet of Things (IoT), Supervisory Control and Data Acquisition (SCADA) networks, and Smart Grid applications are coming under increasing scrutiny in the security field. Remote Attestation (RA) is a security mechanism that allows a trusted device, the verifier, to determine the trustworthiness of an untrusted device, the prover. RA has become an area of high interest in academia and industry and many research works on RA have been published in recent years. This paper reviewed the published RA research works from 2003-2020. Our contributions are fourfold. First, we have re-framed the problem of RA into 5 smaller problems: root of trust, evidence type, evidence gathering, packaging and verification, and scalability. We have provided a holistic review of RA by discussing the relationships between these problems and the various solutions that exist in modern RA research. Second, we have presented an enhanced threat model that allows for a greater understanding of the security benefits of a given RA scheme. Third, we have proposed a taxonomy to classify and analyze RA research works and use it to categorize 58 RA schemes reported in literature. Fourth, we have provided cost benefit analysis details of each RA scheme surveyed such that security professionals may perform a cost benefit analysis in the context of their own challenges. Our classification and analysis has revealed areas of future research that have not been addressed by researchers rigorously.

Remote Attestation (RA) is a security service by which a Verifier (Vrf) can verify the platform state of a remote Prover (Prv). However, in most existing RA schemes, the Prv might be vulnerable to denial of service (DoS) attacks due to the interactive challenge-response methodology while there is no authentication about the challenge. Worse, many schemes cannot effectively detect mobile malware that can be inactive during the on-demand attestation launched by the Vrf. In this paper, we propose a self-measurement RA for SGX-based platforms, which can effectively mitigate DoS attacks and defend against mobile malware. To this end, a two-way identity authentication is first enforced between the Prv and Vrf with the help of a blockchain system, in which a shared session key is also generated. Secondly, trigger conditions of measurements on the Prv’s side are time points generated by the Prv self instead of Vrf’s requests. The Vrf can retrieve multiple selfmeasurement results during one execution of the protocol to monitor the Prv’s platform over a period of time continuously, which can detect mobile malware effectively. Our scheme utilizes SGX to provide the runtime protection for sensitive information such as session key, self-measurement code, time points of self-measurements, and self-measurement results, making a higher security guarantee. In addition, the session key, time points of self-measurements, and self-measurement code can be changed or upgraded, making our scheme more flexible and scalable. The simulation implementation and results show that our scheme is feasible and practical.

Trusted data transmission is the foundation of the Internet of Things (IoT) security, so in the process of data transmission, the trust of IoT nodes needs to be confirmed in real time, and the real-time tracking of node trust is also expected. Yet, modern IoT devices provide limited security capabilities, forming a new attack focus. Remote attestation is a kind of technology to detect network threats by remotely checking the internal situation of terminal devices by a trusted entity. Multidevice attestation is rarely studied although the ongoing single device attestation techniques lack scalability in the application of IoT. In this article, we present a lightweight attestation protocol based on an IoT system under an ideal physical unclonable functions environment. Our protocol can resilient against any strong adversary who physically accesses IoT devices. Simulation results show that our protocol is scalable and can be applied to dynamic networks.

Due to recent notorious security threats, like Miraibotnet, it is challenging to perform efficient data communication and routing in low power and lossy networks (LLNs) such as Internet of Things (IoT), in which huge data collection and processing are predictable. The Routing Protocol for low power and Lossy networks (RPL) is recently standardized as a routing protocol for LLNs. However, the lack of scalability and the vulnerabilities towards various security threats still pose a significant challenge in the broader adoption of RPL in LLNs.

The perception of security when consumers use the m-fintech payment application impacts satisfaction and continuance intention. However, data security threats and legal breaches have made consumers skeptical about the continuance of m-fintech payments. Therefore, this study aims to analyze the perceived security factor as a form of consumer satisfaction and the desire to continue using it with the support of confirmation behavior. This study uses a quantitative method by surveying 357 m-fintech payment users in Jabodetabek. All collected data has been processed, cleaned, and analyzed utilizing variance-based Structural Equation Modeling statistics. The research finding has proven that all hypotheses are accepted. Perceived security significantly affects confirmation, satisfaction, and continuance intention. A confirmation significantly affects satisfaction, and satisfaction significantly affects the continuance intention of mfintech payment. The originality of this research measures perceived security formatively. The conclusions of this analysis serve as information for the digital central currency bank (CDBC) development plan based on the security level.

The growth of Electric Vehicles (EVs), coupled with the deployment of large-scale extreme fast charging stations (XFCSs), has increased the attack surface for EV ecosystems. To secure such critical cyber-physical systems (CPSs), it is imperative for the system defenders to perform an in-depth analysis of potential attack vectors, evaluate possible countermeasures, and analyze attack-defense scenarios quantitatively to implement a defense strategy that will provide maximum utilization of their limited resources. Therefore, a systematic framework is essential, relying on modeling tools that security experts are familiar with. In this paper, we propose a comprehensive methodology for enabling the defender to perform a high-level attack surface analysis of an XFCS and determine the defense strategy with the highest utility value. We apply STRIDE threat modeling and attack defense tree (ADT) to enumerate realizable attack paths and identify possible defense measures. We then employ analytic hierarchy process (AHP) as a multi-criteria decisionmaking algorithm to obtain the highest utility strategy for the defender to adopt. The proposed methodology is validated by demonstrating its real-world feasibility through a case study, using sample attack paths for an XFCS.

Online loan is viewed as an alternative to banking but easier and provide direct connection between public and loan offerer. However, online security threats and scam are undermining the quality of online loan. This study aims to determine how the public views their privacy while using online loan applications, perceived risk, perceived security, and qualities on intention to apply online loan. In order to examine the intention, a quantitative survey method was adopted and survey questionnaire was sent to the public who had experienced and apply for online loan applications. 153 responses were received and analysed using IBM SPSS version 28 for demographic analysis and SmartPLS 4 for model and structural measurements. Results show that perceived security, service quality and system quality were not critical to the respondents when choosing online loan applications while perceived risk, information sharing, and privacy concern were critical. This study shows that general public believed that security and quality are part of the package when organization offered a product or service. Interestingly, while privacy, risk, and information are important, public felt that it is the duty of organization to take care of their interests. Future research should look into behavioural aspects of public risk, information sharing, and privacy concern to understand in-depth.

The escalating visibility of secure direct object reference (IDOR) vulnerabilities in API security, as indicated in ⁠ the compilation of OWASP Top 10 API Security Risks, highlights a noteworthy peril to sensitive data. This study explores IDOR vulnerabilities found within Android APIs, intending to clarify their inception while evaluating their implications for application security. This study combined the qualitative and quantitative approaches. Insights were obtained from an actual penetration test on an Android app into the ⁠ primary reasons for IDOR vulnerabilities, underscoring insufficient input validation and weak authorization methods. We stress the frequent occurrence of IDOR vulnerabilities in the OWASP Top 10 API ⁠ vulnerability list, highlighting the necessity to prioritize them in security evaluations. There are mitigation recommendations available for developers, which recognize its limitations involving a possibly small and homogeneous selection ⁠ of tested Android applications, the testing environment that could cause some inaccuracies, and the impact of time constraints. Additionally, the study noted insufficient threat modeling and root ⁠ cause analysis, affecting its generalizability and real-world relevance. However, comprehending and controlling IDOR dangers can enhance Android API ⁠ security, protect user data, and bolster application resilience.

Risk assessors and managers face many difficult challenges related to the new network system. These challenges include the continuous changes in the nature of network systems caused by technological progress, their distribution in the fields of physics, information and social cognition, and the complex network structure that usually includes thousands of nodes. Here, we review the probability and risk-based decision technology applied to network systems, and conclude that the existing methods can not solve all the components of the risk assessment triad (threat, vulnerability, consequence), and lack the ability to integrate across multiple areas of network systems, thus providing guidance for enhancing network security. We propose a cloud native security chain architecture and network topology reconstruction technology link based on the full link of microservices. The network security performance is quantified by multi-layer filtering mechanism and setting different fitness index functions. The method proposed in this paper solves the problems of packet loss, load balancing and distributed delay of network security mechanism in the global network to a certain extent.

Package registries host reusable code assets, allowing developers to share and reuse packages easily, thus accelerating the software development process. Current software registry ecosystems involve multiple independent stakeholders for package management. Unfortunately, abnormal behavior and information inconsistency inevitably exist, enabling adversaries to conduct malicious activities with minimal effort covertly. In this paper, we investigate potential security vulnerabilities in six popular software registry ecosystems. Through a systematic analysis of the official registries, corresponding registry mirrors and registry clients, we identify twelve potential attack vectors, with six of them disclosed for the first time, that can be exploited to distribute malicious code stealthily. Based on these security issues, we build an analysis framework, RScouter, to continuously monitor and uncover vulnerabilities in registry ecosystems. We then utilize RScouter to conduct a measurement study spanning one year over six registries and seventeen popular mirrors, scrutinizing over 4 million packages across 53 million package versions. Our quantitative analysis demonstrates that multiple threats exist in every ecosystem, and some have been exploited by attackers. We have duly reported the identified vulnerabilities to related stakeholders and received positive responses.

Security system designers favor worst-case security metrics, such as those derived from differential privacy (DP), due to the strong guarantees they provide. On the downside, these guarantees result in a high penalty on the system’s performance. In this paper, we study Bayes security, a security metric inspired by the cryptographic advantage. Similarly to DP, Bayes security i) is independent of an adversary’s prior knowledge, ii) it captures the worst-case scenario for the two most vulnerable secrets (e.g., data records); and iii) it is easy to compose, facilitating security analyses. Additionally, Bayes security iv) can be consistently estimated in a black-box manner, contrary to DP, which is useful when a formal analysis is not feasible; and v) provides a better utility-security trade-off in high-security regimes because it quantifies the risk for a specific threat model as opposed to threat-agnostic metrics such as DP.

This research aimed to examine the relationship between digital citizenship and information security achievements levels. For this purpose, the research was designed in the relational survey model within the scope of quantitative research. The sample of the research consists of teacher candidates studying at the Faculty of Education of Fırat University in the 2022-2023 academic year. To collect the research data, the Digital Citizenship Questionnaire and the Information Security Achievements Scale were used. At the end of the study, it was revealed that the digital citizenship levels of the teacher candidates were high, and the information security attainment levels related to threats and taking precautions were moderate. According to the gender variable, the digital citizenship levels of teacher candidates were found to be significantly higher in favor of females. Information security achievement levels differ significantly in favor of males according to the gender variable. It has been observed that as the information security achievements of the teacher candidates increase, the correct usage, health and social responsibility levels of digital citizenship tend to increase as well.

In the digital era, web applications have become a prevalent tool for businesses. As the number of web applications continues to grow, they become enticing targets for malicious actors seeking to exploit potential security vulnerabilities. Organizations face constant risks associated with vulnerabilities in their web-based software systems, which can result in data breaches, service disruptions, and a loss of trust. Consequently, organizations require an effective and efficient approach to assess and analyze the security of acquired web-based software, ensuring sufficient confidence in its utilization. This research aims to enhance the quantitative evaluation and analysis of web application security through a model-based approach. We focus on integrating the Open Web Application Security Project s (OWASP) Application Security Verification Standard (ASVS) into a structured and analyzable metamodel. This model aims to effectively assess the security levels of web applications while offering valuable insights into their strengths and weaknesses. By combining the ASVS with a comprehensive framework, we aim to provide a robust methodology for evaluating and analyzing web application security.

Security still remains an afterthought in modern Electronic Design Automation (EDA) tools, which solely focus on enhancing performance and reducing the chip size. Typically, the security analysis is conducted by hand, leading to vulnerabilities in the design remaining unnoticed. Security-aware EDA tools assist the designer in the identification and removal of security threats while keeping performance and area in mind. Stateof-the-art approaches utilize information flow analysis to spot unintended information leakages in design structures. However, the classification of such threats is binary, resulting in negligible leakages being listed as well. A novel quantitative analysis allows the application of a metric to determine a numeric value for a leakage. Nonetheless, current approximations to quantify the leakage are still prone to overlooking leakages. The mathematical model 2D-QModel introduced in this work aims to overcome this shortcoming. Additionally, as previous work only includes a limited threat model, multiple threat models can be applied using the provided approach. Open-source benchmarks are used to show the capabilities of 2D-QModel to identify hardware Trojans in the design while ignoring insignificant leakages.

The edge computing-based Internet of Things (IoT) offers benefits in terms of efficiency, low latency, security, and privacy. However, programming models and platforms for this edge-based IoT are still an open problem, particularly regarding security and privacy. This paper proposes concrete and realizable ideas for building a secure programming platform called Secure Swarm Programming Platform (SSPP) to ensure platform-level security for the edge-based IoT while utilizing existing systemlevel security mechanisms. SSPP’s easy-to-use software components can enable static and dynamic security analysis of IoT applications, preventing vulnerabilities and detecting intrusions. Software deployed through SSPP can be remotely attested by a verifier on the edge, ensuring it remains untampered with. This paper also plans out future research and evaluation of SSPP’s programmability, security, and remote attestation.

In the context of cloud environments, data providers entrust their data to data consumers in order to allow further computing on their own IT infrastructure. Usage control measures allow the data provider to restrict the usage of its data even on the data consumer’s system. Two of these restrictions can be the geographic location and time limitations. Current solutions that could be used to enforce such constraints can be easily manipulated. These include solutions based on the system time, organizational agreements, GPS-based techniques or simple delay measurements to derive the distance to known reference servers.