Network intrusion detection systems (NIDSs) play a crucial role in defending computer networks. However, there are concerns regarding the feasibility and sustainability of current approaches when faced with the demands of modern networks. More specifically, these concerns relate to the increasing levels of required human interaction and the decreasing levels of detection accuracy. This paper presents a novel deep learning technique for intrusion detection, which addresses these concerns. We detail our proposed nonsymmetric deep autoencoder (NDAE) for unsupervised feature learning. Furthermore, we also propose our novel deep learning classification model constructed using stacked NDAEs. Our proposed classifier has been implemented in graphics processing unit (GPU)-enabled TensorFlow and evaluated using the benchmark KDD Cup '99 and NSL-KDD datasets. Promising results have been obtained from our model thus far, demonstrating improvements over existing approaches and the strong potential for use in modern NIDSs.

The Internet of Things has facilitated access to a large volume of sensitive information on each participating object in an ecosystem. This imposes many threats ranging from the risks of data management to the potential discrimination enabled by data analytics over delicate information such as locations, interests, and activities. To address these issues, the concept of trust is introduced as an important role in supporting both humans and services to overcome the perception of uncertainty and risks before making any decisions. However, establishing trust in a cyber world is a challenging task due to the volume of diversified influential factors from cyber-physical-systems. Hence, it is essential to have an intelligent trust computation model that is capable of generating accurate and intuitive trust values for prospective actors. Therefore, in this paper, a quantifiable trust assessment model is proposed. Built on this model, individual trust attributes are calculated numerically. Moreover, a novel algorithm based on machine learning principles is devised to classify the extracted trust features and combine them to produce a final trust value to be used for decision making. Finally, our model's effectiveness is verified through a simulation. The results show that our method has advantages over other aggregation methods.

Security through trust presents a viable solution for threat management in the Internet of Things (IoT). Currently, a well-defined trust management framework for collaborative applications on the IoT platform does not exist. In order to estimate reliably the trust values of nodes within a system, the trust should be measured by suitable parameters that are based on the nodes' functional properties in the application context. Existing models do not clearly outline the parametrization of trust. Also, trust decay is inadequately modeled in most current models. In addition, trust recommendations are usually inaccurately weighted with respect to previous trust, thereby increasing the effect of bad recommendations. A new model, CTRUST, is proposed to resolve these shortcomings. In CTRUST, trust is accurately parametrized while recommendations are evaluated through belief functions. The effects of trust decay and maturity on the trust evaluation process were studied. Each trust component is neatly modeled by appropriate mathematical functions. CTRUST was implemented in a collaborative download application and its performance was evaluated based on the utility derived and its trust accuracy, convergence, and resiliency. The results indicate that IoT collaborative applications based on CTRUST gain a significant improvement in performance, in terms of efficiency and security.

In a world where organisations are embracing new IT working models such as Bring Your Own Device (BYOD) and remote working, the traditional mindset of defending the network perimeter is no longer sufficient. Zero Trust Architecture (ZTA) has recently emerged as a new security model in which the breach mindset dominates the threat model. By default, the ZTA considers any endpoint (i.e., device), user, or application to be untrusted until proven otherwise. Nonetheless, once proven by the endpoint, using Advanced Persistent Threats (APT), attackers can still take over an authenticated and authorised session via that endpoint. Therefore, they can perform several user/device centric malicious activities in addition to lateral movement rendering the endpoint as the Achilles heel of ZTA. To effectively deter APT attack capabilities on the endpoints, this work proposes a Blockchain-enabled Intrusion Detection and Prevention System (BIDPS) that augments ZTA onto endpoints. The BIDPS aims to achieve two core outcomes: first, detect and prevent attackers’ techniques and tactics as per MITRE’s ATT&CK enterprise matrix earlier than the lateral movement stage, and secondly, strip trust out of the endpoint itself and place it on-chain, thus creating an immutable system of explicit trust. To evaluate the effectiveness of the BIDPS, a testbed was built where techniques of over ten APTs attacks were launched against the endpoint. BIDPS has proven a high rate of success defending against the launched attacks owing to its Blockchain’s immutability, fortifying the detection/prevention processes.

Recently, using advanced cryptographic techniques to process, store, and share data securely in an untrusted cloud environment has drawn widespread attention from academic researchers. In particular, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is a promising, advanced type of encryption technique that resolves an open challenge to regulate fine-grained access control of sensitive data according to attributes, particularly for Internet of Things (IoT) applications. However, although this technique provides several critical functions such as data confidentiality and expressiveness, it faces some hurdles including revocation issues and lack of managing a wide range of attributes. These two issues have been highlighted by many existing studies due to their complexity which is hard to address without high computational cost affecting the resource-limited IoT devices. In this paper, unlike other survey papers, existing single and multiauthority CP-ABE schemes are reviewed with the main focus on their ability to address the revocation issues, the techniques used to manage the revocation, and comparisons among them according to a number of secure cloud storage criteria. Therefore, this is the first review paper analysing the major issues of CP-ABE in the IoT paradigm and explaining the existing approaches to addressing these issues.

To ensure seamless communications between mobile Electric Vehicles (EVs) and EV power supply equipment, support for ubiquitous and transparent mobile IP communications is essential in Vehicle-to-Grid (V2G) networks. However, it initiates a range of privacy-related challenges as it is possible to track connected EVs through their mobile IP addresses. Recent works are mostly dedicated to solving authentication and privacy issues in V2G networks in general. Yet, they do not tackle the security and privacy challenges resulting from enabling mobile IP communications. To address these challenges, this paper proposes an Efficient, Secure and Privacy-preserving Proxy Mobile IPv6 (ESP-PMIPv6) protocol for the protection of mobile IP communications in V2G networks. ESP-PMIPv6 enables authorised EVs to acquire a mobile IPv6 address and access the V2G network in a secure and privacy-preserving manner. While ESP-PMIPv6 offers mutual authentication, identity anonymity and location unlinkability for the mobile EVs, it also achieves authorised traceability of misbehaving EVs through a novel collaborative tracking scheme. Formal and informal security analyses are conducted to prove that ESP-PMIPv6 meets these security and privacy goals. In addition, via a simulated assessment, the ESP-PMIPv6 is proven to achieve low authentication latency, low handover delay, and low packet loss rate in comparison with the PMIPv6 protocol.