Ransomware attacks pose a serious threat to Internet resources due to their far-reaching effects. It’s Zero-day variants are even more hazardous, as less is known about them. In this regard, when used for ransomware attack detection, conventional machine learning approaches may become data-dependent, insensitive to error cost, and thus may not tackle zero-day ransomware attacks. Zero-day ransomware have normally unseen underlying data distribution. This paper presents a Cost-Sensitive Pareto Ensemble strategy, CSPE-R to detect novel Ransomware attacks. Initially, the proposed framework exploits the unsupervised deep Contractive Auto Encoder (CAE) to transform the underlying varying feature space to a more uniform and core semantic feature space. To learn the robust features, the proposed CSPE-R ensemble technique explores different semantic spaces at various levels of detail. Heterogeneous base estimators are then trained over these extracted subspaces to find the core relevance between the various families of the ransomware attacks. Then, a novel Pareto Ensemble-based estimator selection strategy is implemented to achieve a cost-sensitive compromise between false positives and false negatives. Finally, the decision of selected estimators are aggregated to improve the detection against unknown ransomware attacks. The experimental results show that the proposed CSPE-R framework performs well against zero-day ransomware attacks.

The distributed denial of service (DDoS) attacks in cloud computing requires quick absorption of attack data. DDoS attack mitigation is usually achieved by dynamically scaling the cloud resources so as to quickly identify the onslaught features to combat the attack. The resource scaling comes with an additional cost which may prove to be a huge disruptive cost in the cases of longer, sophisticated, and repetitive attacks. In this work, we address an important problem, whether the resource scaling during attack, always result in rapid DDoS mitigation? For this purpose, we conduct real-time DDoS attack experiments to study the attack absorption and attack mitigation for various target services in the presence of dynamic cloud resource scaling. We found that the activities such as attack absorption which provide timely attack data input to attack analytics, are adversely compromised by the heavy resource usage generated by the attack. We show that the operating system level local resource contention, if reduced during attacks, can expedite the overall attack mitigation. The attack mitigation would otherwise not be completed by the dynamic scaling of resources alone. We conceived a novel relation which terms “Resource Utilization Factor” for each incoming request as the major component in forming the resource contention. To overcome these issues, we propose a new “Scale Inside-out” approach which during attacks, reduces the “Resource Utilization Factor” to a minimal value for quick absorption of the attack. The proposed approach sacrifices victim service resources and provides those resources to mitigation service in addition to other co-located services to ensure resource availability during the attack. Experimental evaluation shows up to 95% reduction in total attack downtime of the victim service in addition to considerable improvement in attack detection time, service reporting time, and downtime of co-located services.

Cryptography has been used from time immemorial for preserving the confidentiality of data/information in storage or transit. Thus, cryptography research has also been evolving from the classical Caesar cipher to the modern cryptosystems, based on modular arithmetic to the contemporary cryptosystems based on quantum computing. The emergence of quantum computing poses a major threat to the modern cryptosystems based on modular arithmetic, whereby even the computationally hard problems which constitute the strength of the modular arithmetic ciphers could be solved in polynomial time. This threat triggered post-quantum cryptography research to design and develop post-quantum algorithms that can withstand quantum computing attacks. This paper provides an overview of the various research directions that have been explored in post-quantum cryptography and, specifically, the various code-based cryptography research dimensions that have been explored. Some potential research directions that are yet to be explored in code-based cryptography research from the perspective of codes is a key contribution of this paper.

As the world calls it, data is the new oil. With vast installments of Industrial Internet of Things (IIoT) infrastructure, data is produced at a rate like never before. Similarly, artificial intelligence (AI) and machine learning (ML) solutions are getting integrated to numerous services, making them “smarter.” However, the data remain fragmented in individual organizational silos inhibiting data value extraction to its full potential. Digital marketplaces are emerging to allow data owners to monetize these data. Yet concerns, such as privacy, security, and unfair payment settlement deter adoption of such platforms. In addition, the state-of-the-art platforms are under the control of large multinational corporations with no transparency between the buyer and seller in terms of payment details, listing, data discovery, and storage. In this work, a novel decentralized platform of a digital data marketplace for IoT data has been proposed. The platform leverages a decentralized data streaming network to host IoT data in a reliable and fault-tolerant manner. The platform ensures fair trading, data storage, and delivery in a privacy-preserving manner and trust metric calculation for actors in the network. In order to study the feasibility of the proposed platform, an opensource library is developed using Hyperledger Fabric and data network layer built on VerneMQ, the library is deployed on a real-time Google cloud platform. The library is tested and results are analyzed for throughput, overheads, and scalability.

The Internet of Things is perhaps a concept that the world cannot be imagined without today, having become intertwined in our everyday lives in the domestic, corporate and industrial spheres. However, irrespective of the convenience, ease and connectivity provided by the Internet of Things, the security issues and attacks faced by this technological framework are equally alarming and undeniable. In order to address these various security issues, researchers race against evolving technology, trends and attacker expertise. Though much work has been carried out on network security to date, it is still seen to be lagging in the field of Internet of Things networks. This study surveys the latest trends used in security measures for threat detection, primarily focusing on the machine learning and deep learning techniques applied to Internet of Things datasets. It aims to provide an overview of the IoT datasets available today, trends in machine learning and deep learning usage, and the efficiencies of these algorithms on a variety of relevant datasets. The results of this comprehensive survey can serve as a guide and resource for identifying the various datasets, experiments carried out and future research directions in this field.