Transparent Encryption for IoT using Offline Key Exchange over Public Blockchains

Internet of Things (IoTs) framework involves of a wide range of com- puting devices that rely on cloud storage for various applications. For instance, monitoring, analytics, surveillance and storing data for later processing within other applications. Due to compliance with security standards and trust issues with third- party cloud storage servers, the IoT data has to be encrypted before moving it to cloud server for storage. However, a major concern with uploading encrypted IoT data to cloud is the management of encryption keys and managing access policies to data. There are several techniques that can be used for storing cryptographic keys used for encryption/decryption of data. For instance, the keys can be stored with encrypted data on the cloud, a third-party key storage vault can be used for storing keys or the keys can stay with client so that they could download and decrypt the data by themselves. In case of encryption keys leakage, the data stored on the cloud storage could be compromised. To resolve the challenge of key management and secure access to data in third-party cloud storage, an end-to-end transparent encryp- tion model has been proposed that securely publishes the cryptographic keys in a blockchain ledger. The data is encrypted at edge gateway before it is transmitted to cloud for storage. The user does not require cryptographic keys to access data; a seamless process involves the client proving their identity to a crypto proxy agent built upon zero trust security principles, ensuring continuous verification

Towards Secure and Privacy-Preserving IoT enabled Smart Home: Architecture and Experimental Study

Internet of Things (IoT) technology is increasingly pervasive in all aspects of our life and its usage is anticipated to significantly increase in future Smart Cities to support their myriad of revolutionary applications. This paper introduces a new architecture that can support several IoT-enabled smart home use cases, with a specified level of security and privacy preservation. The security threats that may target such an architecture are highlighted along with the cryptographic algorithms that can prevent them. An experimental study is performed to provide more insights about the suitability of several lightweight cryptographic algorithms for use in securing the constrained IoT devices used in the proposed architecture. The obtained results showed that many modern lightweight symmetric cryptography algorithms, as CLEFIA and TRIVIUM, are optimized for hardware implementations and can consume up to 10 times more energy than the legacy techniques when they are implemented in software. Moreover, the experiments results highlight that CLEFIA significantly outperforms TRIVIUM under all of the investigated test cases, and the latter performs 100 times worse than the legacy cryptographic algorithms tested

Transfer learning auto-encoder neural networks for anomaly detection of DDoS generating IoT devices

Machine Learning based anomaly detection ap-proaches have long training and validation cycles. With IoT devices rapidly proliferating, training anomaly models on a per device basis is impractical. This work explores the "transfer-ability"of a pre-trained autoencoder model across devices of similar and different nature. We hypothesized that devices of similar nature would have similar high level feature character-istics represented by the initial layers of the autoencoder, while the more distinct features are captured by the innermost layer of the neural network. In our experiments, the centre-most layers of autoencoder models were re-trained with limited new data belonging to a different device. Datasets of seven Mirai infected and nine Bashlite infected IoT devices were used; each dataset also included benign records representing un-infected behaviour. We observed that the model's detection accuracy improved by an average of 9.52% for Mirai and 44.59% for Bashlite. The highest performance improvement of 26.68% and 73.00% was observed when the anomaly model of Ecobee thermostat was tested on other devices before and after transfer learning for Mirai and Bashlite respectively. Additionally, transfer learning took 47.31% and 58.27% less time for Mirai and Bashlite respectively. We further trialed the efficacy of the autoencoder based anomaly model on flow based records of network traffic using the CIC-IDS2017 dataset. It was observed that the model performed best when distinct outliers in the dataset were present, whereas the model failed to perform decently in cases where the malicious activity did not cause significant deviation in network traffic's footprint

A pre-trained BERT for Korean medical natural language processing

Abstract With advances in deep learning and natural language processing (NLP), the analysis of medical texts is becoming increasingly important. Nonetheless, despite the importance of processing medical texts, no research on Korean medical-specific language models has been conducted. The Korean medical text is highly difficult to analyze because of the agglutinative characteristics of the language, as well as the complex terminologies in the medical domain. To solve this problem, we collected a Korean medical corpus and used it to train the language models. In this paper, we present a Korean medical language model based on deep learning NLP. The model was trained using the pre-training framework of BERT for the medical context based on a state-of-the-art Korean language model. The pre-trained model showed increased accuracies of 0.147 and 0.148 for the masked language model with next sentence prediction. In the intrinsic evaluation, the next sentence prediction accuracy improved by 0.258, which is a remarkable enhancement. In addition, the extrinsic evaluation of Korean medical semantic textual similarity data showed a 0.046 increase in the Pearson correlation, and the evaluation for the Korean medical named entity recognition showed a 0.053 increase in the F1-score