Towards robust autonomous driving systems through adversarial test set generation.

Correct environmental perception of objects on the road is vital for the safety of autonomous driving. Making appropriate decisions by the autonomous driving algorithm could be hindered by data perturbations and more recently, by adversarial attacks. We propose an adversarial test input generation approach based on uncertainty to make the machine learning (ML) model more robust against data perturbations and adversarial attacks. Adversarial attacks and uncertain inputs can affect the ML model's performance, which can have severe consequences such as the misclassification of objects on the road by autonomous vehicles, leading to incorrect decision-making. We show that we can obtain more robust ML models for autonomous driving by making a dataset that includes highly-uncertain adversarial test inputs during the re-training phase. We demonstrate an improvement in the accuracy of the robust model by more than 12%, with a notable drop in the uncertainty of the decisions returned by the model. We believe our approach will assist in further developing risk-aware autonomous systems.

Secure Bluetooth Communication in Smart Healthcare Systems: A Novel Community Dataset and Intrusion Detection System.

Smart health presents an ever-expanding attack surface due to the continuous adoption of a broad variety of Internet of Medical Things (IoMT) devices and applications. IoMT is a common approach to smart city solutions that deliver long-term benefits to critical infrastructures, such as smart healthcare. Many of the IoMT devices in smart cities use Bluetooth technology for short-range communication due to its flexibility, low resource consumption, and flexibility. As smart healthcare applications rely on distributed control optimization, artificial intelligence (AI) and deep learning (DL) offer effective approaches to mitigate cyber-attacks. This paper presents a decentralized, predictive, DL-based process to autonomously detect and block malicious traffic and provide an end-to-end defense against network attacks in IoMT devices. Furthermore, we provide the BlueTack dataset for Bluetooth-based attacks against IoMT networks. To the best of our knowledge, this is the first intrusion detection dataset for Bluetooth classic and Bluetooth low energy (BLE). Using the BlueTack dataset, we devised a multi-layer intrusion detection method that uses deep-learning techniques. We propose a decentralized architecture for deploying this intrusion detection system on the edge nodes of a smart healthcare system that may be deployed in a smart city. The presented multi-layer intrusion detection models achieve performances in the range of 97-99.5% based on the F1 scores.

Advanced Deep Learning for Resource Allocation and Security Aware Data Offloading in Industrial Mobile Edge Computing.

The Internet of Things (IoT) is permeating our daily lives through continuous environmental monitoring and data collection. The promise of low latency communication, enhanced security, and efficient bandwidth utilization lead to the shift from mobile cloud computing to mobile edge computing. In this study, we propose an advanced deep reinforcement resource allocation and security-aware data offloading model that considers the constrained computation and radio resources of industrial IoT devices to guarantee efficient sharing of resources between multiple users. This model is formulated as an optimization problem with the goal of decreasing energy consumption and computation delay. This type of problem is non-deterministic polynomial time-hard due to the curse-of-dimensionality challenge, thus, a deep learning optimization approach is presented to find an optimal solution. In addition, a 128-bit Advanced Encryption Standard-based cryptographic approach is proposed to satisfy the data security requirements. Experimental evaluation results show that the proposed model can reduce offloading overhead in terms of energy and time by up to 64.7% in comparison with the local execution approach. It also outperforms the full offloading scenario by up to 13.2%, where it can select some computation tasks to be offloaded while optimally rejecting others. Finally, it is adaptable and scalable for a large number of mobile devices.

Time-series forecasting of Bitcoin prices using high-dimensional features: a machine learning approach.

Bitcoin is a decentralized cryptocurrency, which is a type of digital asset that provides the basis for peer-to-peer financial transactions based on blockchain technology. One of the main problems with decentralized cryptocurrencies is price volatility, which indicates the need for studying the underlying price model. Moreover, Bitcoin prices exhibit non-stationary behavior, where the statistical distribution of data changes over time. This paper demonstrates high-performance machine learning-based classification and regression models for predicting Bitcoin price movements and prices in short and medium terms. In previous works, machine learning-based classification has been studied for an only one-day time frame, while this work goes beyond that by using machine learning-based models for one, seven, thirty and ninety days. The developed models are feasible and have high performance, with the classification models scoring up to 65% accuracy for next-day forecast and scoring from 62 to 64% accuracy for seventh-ninetieth-day forecast. For daily price forecast, the error percentage is as low as 1.44%, while it varies from 2.88 to 4.10% for horizons of seven to ninety days. These results indicate that the presented models outperform the existing models in the literature.

Cystic lymphangioma of the lesser omentum in a pregnant woman: a case report and review of the literature.

OBJECTIVE: Lymphangiomas are rare benign tumors which are generally seen in pediatric population and the etiopathogenesis has not yet been understood. They occasionally occur in the head and neck or axillary region with only 5% of them being located in the abdominal or mediastinal cavity. These tumors may be asymptomatic or may cause acute abdominal symptoms due to the location and extention. In the English literature, only 4 cases of lymphangioma were reported to have occurred in the pregnancy period. Herein, we report a case of cystic lymphangioma of the lesser omentum detected incidentally on the ultrasonogram of a 21 year-old, 26-week pregnant woman. The patient was followed up uneventfully during pregnancy. Caesarean section was performed due to transverse presentation of the fetus, and the tumor was completely resected during the same session. The patient is recurrence-free after 1 year of postoperative follow-up.

A Case of Plasma Cell Granuloma Located on the Gingiva.

Plasma cell granuloma (PCG) is an uncommon benign tumor of unknown etiology, primarily located in the lungs. We report a case of PCG on the gingiva mimicking benign and malignant tumors in a 56-year-old woman. Histopathological examination revealed a relatively sharp circumscribed inflammatory cell infiltration under the mucosa-containing plasma cells, predominantly those including Russell bodies. Plasma cells are stained by CD138 immunohistochemistry. Polyclonal status of the lesion was confirmed by kappa and lambda light chaining. The typical histopathological and immunohistochemical findings in combination with the clinical features were consistent with PCG, about which the literature reports very few cases.

Nasal chondromesenchymal hamartoma, a rare pediatric tumor: Case report.

Nasal chondromesenchymal hamartoma has characteristic clinicopathological features and it is accepted as the upper airway analogue of mesenchymal hamartoma of the chest wall. It is a rare lesion and only 31 cases have been reported in the English literature until 2014. In this article, a 13-year-old nasal chondromesenchymal hamartoma case is presented, which is the first nasal chondromesenchymal hamartoma case from Turkey. Although, nasal chondromesenchymal hamartoma has been accepted as a benign lesion, the possibility of malignant transformation should be kept in mind, and detailed histologic examination should be performed particularly in adult nasal chondromesenchymal hamartoma cases.