Openflow Path Fast Failover Fast Convergence Mechanism

Whenever there is a link failure in the network, OpenFlow controller can react by computing a new backup path and provide the information to the affected node or the node can switch to backup path locally using the predefined backup path table provided by the OpenFlow controller. Setting predefined backup paths, results in a faster network rerouting time compared to backup path that establish on demand. However, it may lead to the use of sub-optimal backup path. In this paper, we present a fast and efficient failover mechanism for redirecting traffic flows to more optimal backup path when there is a link failure or congestion problem. We introduce a switch flow entry expiry mechanism to immediately reroute traffic to backup path to reduce the network restoration time. To update the switch with more optimal path information, the controller require a round trip to the network and this can introduce to slow network convergence. We proposed a local pre-calculated path dataset mechanism in Openflow controller to allow fast network convergence

Biometrics for internet‐of‐things security: A review

The large number of Internet‐of‐Things (IoT) devices that need interaction between smart devices and consumers makes security critical to an IoT environment. Biometrics offers an interesting window of opportunity to improve the usability and security of IoT and can play a significant role in securing a wide range of emerging IoT devices to address security challenges. The purpose of this review is to provide a comprehensive survey on the current biometrics research in IoT security, especially focusing on two important aspects, authentication and encryption. Regarding authentication, contemporary biometric‐based authentication systems for IoT are discussed and classified based on different biometric traits and the number of biometric traits employed in the system. As for encryption, biometric‐cryptographic systems, which integrate biometrics with cryptography and take advantage of both to provide enhanced security for IoT, are thoroughly reviewed and discussed. Moreover, challenges arising from applying biometrics to IoT and potential solutions are identified and analyzed. With an insight into the state‐of‐the‐art research in biometrics for IoT security, this review paper helps advance the study in the field and assists researchers in gaining a good understanding of forward‐looking issues and future research directions

IoT threat detection advances, challenges and future directions

It is predicted that, the number of connected Internet of Things (IoT) devices will rise to 38.6 billion by 2025 and an estimated 50 billion by 2030. The increased deployment of IoT devices into diverse areas of our life has provided us with significant benefits such as improved quality of life and task automation. However, each time a new IoT device is deployed, new and unique security threats emerge or are introduced into the environment under which the device must operate. Instantaneous detection and mitigation of every security threat introduced by different IoT devices deployed can be very challenging. This is because many of the IoT devices are manufactured with no consideration of their security implications. In this paper therefore, we review existing literature and present IoT threat detection research advances with a focus on the various IoT security challenges as well as the current developments towards combating cyber security threats in IoT networks. However, this paper also highlights several future research directions in the IoT domain

Leveraging Artificial Intelligence Capabilities for Real-Time Monitoring of Cybersecurity Threats

The number of cybersecurity incidents perpetrated by adversaries using modern complex and emerging technologies is growing exponentially. To curb this threat, advanced countermeasures need to be developed and implemented to mitigate cybersecurity incidents and detect the activities of adversaries. Developing advanced, innovative, and effective countermeasures is a challenge when faced with a daily tide of cybersecurity threats and concomitant vulnerabilities which, in most cases, have significant consequences to organisations. Artificial Intelligence (AI), which has specific, established use-cases in the cybersecurity domain offers promising solutions. This chapter examines how AI can enhance the real-time monitoring of cybersecurity threats in different environments. As a result, different cybersecurity threats, as well as commonly exploited cybersecurity vulnerabilities, are described and discussed in this chapter. Following this, some current real-time cybersecurity monitoring tools are discussed. Finally, the chapter highlights the role of AI in real-time monitoring focusing on how Explainable Artificial Intelligence (XAI) can be used to enhance real-time monitoring of cybersecurity threats which has become a crucial component of modern-day security implementations

A Review of Security Standards and Frameworks for IoT-Based Smart Environments

Assessing the security of IoT-based smart environments such as smart homes and smart cities is becoming fundamentally essential to implementing the correct control measures and effectively reducing security threats and risks brought about by deploying IoT-based smart technologies. The problem, however, is in finding security standards and assessment frameworks that best meets the security requirements as well as comprehensively assesses and exposes the security posture of IoT-based smart environments. To explore this gap, this paper presents a review of existing security standards and assessment frameworks which also includes several NIST special publications on security techniques highlighting their primary areas of focus to uncover those that can potentially address some of the security needs of IoT-based smart environments. Cumulatively a total of 80 ISO/IEC security standards, 32 ETSI standards and 37 different conventional security assessment frameworks which included seven NIST special publications on security techniques were reviewed. To present an all-inclusive and up-to-date state-of-the-art research, the review process considered both published security standards and assessment frameworks as well as those under development. The findings show that most of the conventional security standards and assessment frameworks do not directly address the security needs of IoT-based smart environments but have the potential to be adapted into IoT-based smart environments. With this insight into the state-of-the-art research on security standards and assessment frameworks, this study helps advance the IoT field by opening new research directions as well as opportunities for developing new security standards and assessment frameworks that will address future IoT-based smart environments security concerns. This paper also discusses open problems and challenges related to IoT-based smart environments security issues. As a new contribution, a taxonomy of challenges for IoT-based smart environment security concerns drawn from the extensive literature examined during this study is proposed in this paper which also maps the identified challenges to potential proposed solutions.Validerad;2021;Nivå 2;2021-09-13 (beamah);Forskningsfinansiär: Australian Government’sCooperative Research Centres Programme</p