Quantum computing advances threaten the security of today's public key infrastructure, and have led to the pending standardization of alternative, quantum-resistant key encapsulation and digital signature cryptography schemes. Unfortunately, authentication algorithms based on the new post-quantum (PQ) cryptography create significant performance bottlenecks for TLS due to larger certificate chains which introduce additional packets and round-trips. The TLS handshake slowdown will be unacceptable to many applications, and detrimental to the broader adoption of quantum safe cryptography standards. In this paper, we propose a novel framework for Intermediate Certificate Authority (ICA) certificate suppression in TLS that reduces the authentication message size and prevents excessive round-trip delays. Our approach utilizes an approximate membership query (AMQ) data structure (probabilistic filter) to advertise known ICA certs to remote TLS endpoints so that unnecessary ICA certificates are omitted from the TLS handshake exchange. We showcase the extend of the PQ authentication overhead challenge in TLS, and evaluate the feasibility of AMQ filters for ICA suppression in terms of space and computational overhead. Finally, we experimentally evaluate the potential gains form our approach and showcase a 70% reduction in exchanged ICA cert data that translates to 15--50 MB of savings in PQ TLS and for certain Web-based application scenarios.

Distribution and transmission protection systems are considered vital parts of modern smart grid ecosystems due to their ability to isolate faulted segments and preserve the operation of critical loads. Current protection schemes increasingly utilize cognitive methods to proactively modify their actions according to extreme power system changes. However, the effectiveness and robustness of these information-driven solutions rely entirely on the integrity, authenticity, and confidentiality of the data and control signals exchanged on the underlying relay communication networks. In this paper, we outline a scalable adaptive protection platform for distribution systems, and introduce a novel blockchain-based distributed network architecture to enhance data exchange security among the smart grid protection relays. The proposed mechanism utilizes a tiered blockchain architecture to counter the current technology limitations providing low latency with better scalability. The decentralized nature removes singular points of failure or contamination, enabling direct secure communication between smart grid relays. We also present a security analysis that demonstrates how the proposed framework prohibits any alterations on the blockchain ledger providing integrity and authenticity of the exchanged data (e.g., realtime measurements/relay settings). Finally, the performance of the proposed approach is evaluated through simulation on a blockchain benchmarking framework with the results demonstrating a promising solution for secure smart grid protection system communication.

The advances in quantum computing present a threat to public key primitives due to their ability to solve hard cryptographic problems in polynomial time. To address this threat to critical Internet security protocols like the Transfer Layer Security (TLS), and Secure Shell (SSH), the National Institute of Standards and Technology (NIST) is currently working on the new generation of quantum-resistant key encapsulation and authentication schemes. In this paper, we evaluate protocol handshake performance when both post-quantum key exchange and authentication are integrated into TLS and SSH. Our experiments consider realistic network conditions and reveal that the introduced handshake latency ranges between 1-300% for TLS and 0.5-50% for SSH depending on the post-quantum algorithms used. In addition, we examine how the initial TCP window size affects post-quantum TLS and SSH performance, and show that even a small size increase can reduce the observed post-quantum slowdown by 50%. Finally, we discuss alternatives that can encourage the early adoption of post-quantum cryptography with minimum protocol performance degradation.

Internet of Things (IoT) deployments are becoming the backbone of all future Smart City (SC) environments. They can, therefore, act as massive crowd-sourced data aggregators, driven by device-to-device interactions with SC users' mobile devices and their wireless interfaces. Provided that, our research focuses on developing probabilistic and machine learning models to (a) enable knowledge discovery from passive user interactions with the wireless IoT infrastructure and (b) apply the collected intelligence to increase the energy-efficiency and resiliency of the Smart City's IoT network. In this extended abstract we elaborate on the motivation behind our work, and the related challenges, while pointing to the solutions developed so far.

Multipurpose devices have emerged as part of modern Internet of Things (IoT) ecosystems. Such nodes are able of interchanging their operation between different sensing modes providing a large mixture of information towards various IoT applications. In this paper, a novel framework is introduced to govern and properly define the dynamic operation of a multipurpose device network deployment. Initially, the problem of socio- physical energy-efficient device sensing mode selection is confronted. Each multipurpose device acts as a learning automaton and through a machine learning mechanism selects the most appropriate operation mode, in terms of maximizing the revenue/cost relation of the provider. In addition, towards improving the communication efficiency, a coalition formation mechanism among the nodes is proposed, which considers: (a) nodes' spatial proximity reflecting physical conditions such as channel quality, (b) energy availability, and (c) operation mode correlation between multipurpose devices expressing social metrics. Given the mode selection and coalition formation among nodes, a distributed utility-based power control mechanism is proposed to determine each device's optimal transmission power in a Non- Orthogonal Multiple Access (NOMA) wireless network environment in order to fulfill its Quality of Service (QoS) prerequisites. The performance of the proposed approach is evaluated through modeling and simulation under several scenarios, and its superiority is demonstrated.

The accelerated evolution of Internet of Things (IoT) architectures and their incorporation in vehicles, buildings, or cities provide the ideal environment for the development and optimization of smart services. Under this light, positioning services that harvest location fingerprinting based on received signal strength indications (RSSIs) are widely popular due to the massive data generation that IoT settings provide. However, the labor-intensive and repetitive task of the radio map construction through offline RSSI fingerprint collection prevents such services from becoming standard equipment for future smart facilities. In this paper, we present a location-aware infrastructure that combines a broad sensing layer, edge computing, and centralized cloud federation support. Our setting gives rise to a sensing mechanism that enables in-facility crowdsourcing able to aid fingerprinting localization services. To that end, instead of extensive offline measurements, we use the facility occupants to gather unlabeled RSSI samples. To support the localization functionality, we develop a probabilistic cell-based model that is constructed by an unsupervised learning algorithm. Our black-box approach maintains the positioning accuracy regardless of changes in the underlying hardware or indoor environment. To evaluate our approach, we have deployed a multistorey facility testbed and performed an extensive real-subject trial to gather the unlabeled fingerprint dataset. The proposed unsupervised method yields average location classification accuracy of 0.8 that can rise up to 0.9 when a semi-supervised approach is considered. We also provide insights into the performance of the proposed infrastructure regarding mobility tracking, and under varying deployment scenarios.