The recent advances in mobile technologies have resulted in IoT-enabled devices becoming more pervasive and integrated into our daily lives. The security challenges that need to be overcome mainly stem from the open nature of a wireless medium such as a Wi-Fi network. An impersonation attack is an attack in which an adversary is disguised as a legitimate party in a system or communications protocol. The connected devices are pervasive, generating high-dimensional data on a large scale, which complicates simultaneous detections. Feature learning, however, can circumvent the potential problems that could be caused by the large-volume nature of network data. This study thus proposes a novel Deep-Feature Extraction and Selection (D-FES), which combines stacked feature extraction and weighted feature selection. The stacked autoencoding is capable of providing representations that are more meaningful by reconstructing the relevant information from its raw inputs. We then combine this with modified weighted feature selection inspired by an existing shallow-structured machine learner. We finally demonstrate the ability of the condensed set of features to reduce the bias of a machine learner model as well as the computational complexity. Our experimental results on a well-referenced Wi-Fi network benchmark dataset, namely, the Aegean Wi-Fi Intrusion Dataset (AWID), prove the usefulness and the utility of the proposed D-FES by achieving a detection accuracy of 99.918% and a false alarm rate of 0.012%, which is the most accurate detection of impersonation attacks reported in the literature.

The exponential growth of big data and deep learning has increased the data exchange traffic in society. Machine Learning as a Service, (MLaaS) which leverages deep learning techniques for predictive analytics to enhance decision-making, has become a hot commodity. However, the adoption of MLaaS introduces data privacy challenges for data owners and security challenges for deep learning model owners. Data owners are concerned about the safety and privacy of their data on MLaaS platforms, while MLaaS platform owners worry that their models could be stolen by adversaries who pose as clients. Consequently, Privacy-Preserving Deep Learning (PPDL) arises as a possible solution to this problem. Recently, several papers about PPDL for MLaaS have been published. However, to the best of our knowledge, no previous paper has summarized the existing literature on PPDL and its specific applicability to the MLaaS environment. In this paper, we present a comprehensive survey of privacypreserving techniques, starting from classical privacy-preserving techniques to well-known deep learning techniques. Additionally, we present a detailed description of PPDL and address the issue of using PPDL for MLaaS. Furthermore, we undertake detailed comparisons between state-of-the-art PPDL methods. Subsequently, we classify an adversarial model on PPDL by highlighting possible PPDL attacks and their potential solutions. Ultimately, our paper serves as a single point of reference for detailed knowledge on PPDL and its applicability to MLaaS environments for both new and experienced researchers.

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Group Key Exchange (GKE) is required for secure group communication with high confidentiality. In particular, a trusted authority can handle issues that happen by the malicious actions of group members, but it is expensive to deploy and not suitable in a dynamic setting where the network requires frequent membership status changes. To overcome these issues, we designed yet another quantum-resistant constant-round GKE based on lattice without a trusted authority based on Apon et al.'s protocol (PQCrypto 2019) by modifying their key computation phase. Then, we describe the novel dynamic authenticated GKE (called DRAG) with membership addition/deletion procedures in Ring Learning with Errors (RLWE) setting, while the former ones are built from Diffie-Hellman problem. Under the specific adversary who can leak the long-term secret key from the party, we suggest a rigorous proof of DRAG in the random oracle model based on the hardness assumption of RLWE problem and the property of Rényi divergence. As a proof of concept, implementation details are described to meet level 1 NIST security. Our implementation is reasonable for practical use since the total runtime to get a group secret key takes about 3 msec and it can be considered as a reference implementation of other quantum-resistant GKEs. INDEX TERMS Authenticated group key exchange, key establishment, lattice-based cryptography, post quantum cryptography, ring learning with errors (RLWE).

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