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.acceptedVersio

Network Mobility Management Challenges, Directions, and Solutions: An Architectural Perspective

Efficient mobility management solutions are essential to provide users with seamless connectivity and session continuity during movement. However, user mobility was not envisaged as one of the early Internet's use cases due to the early adoption of destination based routing and the assumption that end-nodes are static. This has become a critical hinder for providing efficient mobility support. This paper presents the challenges, drivers, and solutions that aim to overcome the drawbacks of current mobility management approaches. Furthermore, it introduces a promising solution that builds on emerging path-based forwarding architectures that identify network links rather than end nodes. Delivery path information is stored inside the packet while forwarding is achieved by performing a simple set membership test rather than the current destination-based routing approach. Mobility management in these architectures simply requires partial recomputation of the delivery path allowing for efficient mobility support over an optimal path. Evaluation results show significant cost savings in terms of delivery paths and end-to-end packet delay when using a path forwarding architecture

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 nonstationary 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

Detection of Botnet Attacks against Industrial IoT Systems by Multilayer Deep Learning Approaches

Industry 4.0 is the next revolution in manufacturing technology that is going to change the production and distribution of goods and services within the following decade. Powered by different enabling technologies that are also being developed simultaneously, it has the potential to create radical changes in our societies such as by giving rise to highly-integrated smart cities. The Industrial Internet of Things (IIoT) is one of the main areas of development for Industry 4.0. These IIoT devices are used in mission-critical sectors such as the manufacturing industry, power generation, and healthcare management. However, smart factories and cities can only function when threats to cyber security, data privacy, and information integrity are properly managed. In this regard, securing IIoT devices and their networks is vital to preserving data and privacy. The use of artificial intelligence is an enabler for more secure IIoT systems. In this study, we propose high-performing deep learning models for the classification of botnet attacks that commonly affect IIoT devices and networks. Evaluation of results shows that deep learning models such as the artificial neural network (ANN), the long short-term memory (LSTM), and the gated recurrent unit (GRU) can successfully be used for classifications of IIoT malware attacks with an accuracy of up to 99%

Network Mobility Management Challenges, Directions, and Solutions: An Architectural Perspective

Efficient mobility management solutions are essential to provide users with seamless connectivity and session continuity during movement. However, user mobility was not envisaged as one of the early Internet’s use cases due to the early adoption of destination based routing and the assumption that end-nodes are static. This has become a critical hinder for providing efficient mobility support. This paper presents the challenges, drivers, and solutions that aim to overcome the drawbacks of current mobility management approaches. Furthermore, it introduces a promising solution that builds on emerging path-based forwarding architectures that identify network links rather than end nodes. Delivery path information is stored inside the packet while forwarding is achieved by performing a simple set membership test rather than the current destination-based routing approach. Mobility management in these architectures simply requires partial recomputation of the delivery path allowing for efficient mobility support over an optimal path. Evaluation results show significant cost savings in terms of delivery paths and end-to-end packet delay when using a path forwarding architecture

Network Mobility Management Challenges, Directions, and Solutions: An Architectural Perspective

Efficient mobility management solutions are essential to provide users with seamless connectivity and session continuity during movement. However, user mobility was not envisaged as one of the early Internet’s use cases due to the early adoption of destination based routing and the assumption that end-nodes are static. This has become a critical hinder for providing efficient mobility support. This paper presents the challenges, drivers, and solutions that aim to overcome the drawbacks of current mobility management approaches. Furthermore, it introduces a promising solution that builds on emerging path-based forwarding architectures that identify network links rather than end nodes. Delivery path information is stored inside the packet while forwarding is achieved by performing a simple set membership test rather than the current destination-based routing approach. Mobility management in these architectures simply requires partial recomputation of the delivery path allowing for efficient mobility support over an optimal path. Evaluation results show significant cost savings in terms of delivery paths and end-to-end packet delay when using a path forwarding architecture

A Nature-Inspired Partial Distance-Based Clustering Algorithm

In the rapidly advancing landscape of digital technologies, clustering plays a critical role in the domains of artificial intelligence and big data. Clustering is essential for extracting meaningful insights and patterns from large, intricate datasets. Despite the efficacy of traditional clustering techniques in handling diverse data types and sizes, they encounter challenges posed by the increasing volume and dimensionality of data, as well as the complex structures inherent in high-dimensional spaces. This research recognizes the constraints of conventional clustering methods, including sensitivity to initial centroids, dependence on prior knowledge of cluster counts, and scalability issues, particularly in large datasets and Internet of Things implementations. In response to these challenges, we propose a K-level clustering algorithm inspired by the collective behavior of fish locomotion. K-level introduces a novel clustering approach based on greedy merging driven by distances in stages. This iterative process efficiently establishes hierarchical structures without the need for exhaustive computations. K-level gives users enhanced control over computational complexity, enabling them to specify the number of clusters merged simultaneously. This flexibility ensures accurate and efficient hierarchical clustering across diverse data types, offering a scalable solution for processing extensive datasets within a reasonable timeframe. The internal validation metrics, including the Silhouette Score, Davies–Bouldin Index, and Calinski–Harabasz Index, are utilized to evaluate the K-level algorithm across various types of datasets. Additionally, comparisons are made with rivals in the literature, including UPGMA, CLINK, UPGMC, SLINK, and K-means. The experiments and analyses show that the proposed algorithm overcomes many of the limitations of existing clustering methods, presenting scalable and adaptable clustering in the dynamic landscape of evolving data challenges

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

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).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.Peer reviewe

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

Clinical Assessment and Management of Spondyloarthritides in the Middle East: A Multinational Investigation

Data on spondyloarthritis (SpA) from the Middle East are sparse and the management of these diseases in this area of the world faces a number of challenges, including the relevant resources to enable early diagnosis and referral and sufficient funds to aid the most appropriate treatment strategy. The objective was to report on the characteristics, disease burden, and treatment of SpA in the Middle East region and to highlight where management strategies could be improved, with the overall aim of achieving better patient outcomes. This multicenter, observational, cross-sectional study collected demographic, clinical, laboratory, and treatment data on 169 consecutive SpA patients at four centers (Egypt, Kuwait, Qatar, and Saudi Arabia). The data collected presents the average time from symptom onset to diagnosis along with the presence of comorbidities in the region and comparisons between treatment with NSAIDs and biologics. In the absence of regional registries of SpA patients, the data presented here provide a rare snapshot of the characteristics, disease burden, and treatment of these patients, highlighting the management challenges in the region