Network-Aware AutoML Framework for Software-Defined Sensor Networks

As the current detection solutions of distributed denial of service attacks (DDoS) need additional infrastructures to handle high aggregate data rates, they are not suitable for sensor networks or the Internet of Things. Besides, the security architecture of software-defined sensor networks needs to pay attention to the vulnerabilities of both software-defined networks and sensor networks. In this paper, we propose a network-aware automated machine learning (AutoML) framework which detects DDoS attacks in software-defined sensor networks. Our framework selects an ideal machine learning algorithm to detect DDoS attacks in network-constrained environments, using metrics such as variable traffic load, heterogeneous traffic rate, and detection time while preventing over-fitting. Our contributions are two-fold: (i) we first investigate the trade-off between the efficiency of ML algorithms and network/traffic state in the scope of DDoS detection. (ii) we design and implement a software architecture containing open-source network tools, with the deployment of multiple ML algorithms. Lastly, we show that under the denial of service attacks, our framework ensures the traffic packets are still delivered within the network with additional delays

Enhancement of educational services by using the internet of things applications for talent and intelligent schools

This study deals with the issue of improving educational services for schools of talent and intelligence. The availability of devices, equipment, sensors, and the Internet of things applications led to a direct contribution to improve the level of student education. In addition, the students can complete the tasks and homework easily. The talented and intelligent students are more efficient, skilled, and active. In addition, they are Deeping to understand virtual reality and coexist with it with awareness and consciousness of the development period of information, the spread of equipment, and smart devices. Educational entities achieved their goals by graduating intelligent students who can join the labor market and contribute to the development of the country. In this research, the important features of the Internet of things that are available in the educational environment were studied, and how to get the benefit from them in developing educational services and scientific research service. The Developing of artificial intelligence capabilities, building the right management strategies and creating comprehensive (security, health, and economic) databases that can be relied upon with complete reliability

Novel authentication framework for securing communication in internet-of-things

Internet-of-Things (IoT) offers a big boon towards a massive network of connected devices and is considered to offer coverage to an exponential number of the smart appliance in the very near future. Owing to the nascent stage of evolution of IoT, it is shrouded by security loopholes because of various reasons. Review of existing research-based solution highlights the usage of conventional cryptographic-based solution over the traditional mechanism of data forwarding process between IoT nodes and gateway. The proposed system presents a novel solution to this problem by a model that is capable of performing a highly secured and cost-effective authentication process. The proposed system introduces Authentication Using Signature (AUS) as well as Security with Complexity Reduction (SCR) for the purpose to resist participation of any form of unknown threats. The outcome of the model shows better security strength with faster response time and energy saving of the IoT nodes

Balancing Digital Innovation and Cybersecurity Capabilities through Organizational Ambidexterity – An Investigation in the Automotive Industry

An organization’s digital innovation capability, i.e., its ability to leverage (technological) trends and developments, is not only associated with opportunities but also entails challenges and risks. Various incidents underline the importance of cybersecurity in this context. While organizations in the automotive industry have recognized both as inevitable, they perceive a trade-off between their innovation and cybersecurity capabilities. As digital innovations are often prestigious, they might prioritize factors like time-to-market and postpone cybersecurity to development and operations. To identify factors enabling organizations to balance the ambidextrous requirements of the two, we conducted an interview study in the automotive industry. Our findings indicate that organizational ambidexterity enabled by strategic and operational elements can minimize the trade-off and the associated risks, with implications for both theory and practice

Cyber Security vs. Digital Innovation: A Trade-off for Logistics Companies?

Digital innovations are essential for companies in the 21st century. However, due to their reliance on (new) technologies, they are associated with cybersecurity risks. As the reduction of these can negatively affect an organization’s innovation capability, a trade-off might result. This trade-off has, to our knowledge, not yet been sufficiently researched. Our paper contributes to closing this research gap using semi-structured interviews with 14 digital innovation and cybersecurity experts in the German logistics industry. Findings from these interviews suggest that there are different types of tensions between digital innovation and cybersecurity capabilities detrimentally influencing innovations in three ways: by slowing down (temporally), requiring more resources (economically), or restricting innovative freedom (functionally). Furthermore, we were able to identify triggering and resolving factors. Thereby, our paper offers valuable contributions from both a theoretical as well as practical perspective

Deep learning with focal loss approach for attacks classification

The rapid development of deep learning improves the detection and classification of attacks on intrusion detection systems. However, the unbalanced data issue increases the complexity of the architecture model. This study proposes a novel deep learning model to overcome the problem of classifying multi-class attacks. The deep learning model consists of two stages. The pre-tuning stage uses automatic feature extraction with a deep autoencoder. The second stage is fine-tuning using deep neural network classifiers with fully connected layers. To reduce imbalanced class data, the feature extraction was implemented using the deep autoencoder and improved focal loss function in the classifier. The model was evaluated using 3 loss functions, including cross-entropy, weighted cross-entropy, and focal losses. The results could correct the class imbalance in deep learning-based classifications. Attack classification was achieved using automatic extraction with the focal loss on the CSE-CIC-IDS2018 dataset is a high-quality classifier with 98.38% precision, 98.27% sensitivity, and 99.82% specificity

A Machine Learning SDN-Enabled Big Data Model for IoMT System

[EN] In recent times, health applications have been gaining rapid popularity in smart cities using the Internet of Medical Things (IoMT). Many real-time solutions are giving benefits to both patients and professionals for remote data accessibility and suitable actions. However, timely medical decisions and efficient management of big data using IoT-based resources are the burning research challenges. Additionally, the distributed nature of data processing in many proposed solutions explicitly increases the threats of information leakages and damages the network integrity. Such solutions impose overhead on medical sensors and decrease the stability of the real-time transmission systems. Therefore, this paper presents a machine-learning model with SDN-enabled security to predict the consumption of network resources and improve the delivery of sensors data. Additionally, it offers centralized-based software define network (SDN) architecture to overcome the network threats among deployed sensors with nominal management cost. Firstly, it offers an unsupervised machine learning technique and decreases the communication overheads for IoT networks. Secondly, it predicts the link status using dynamic metrics and refines its strategies using SDN architecture. In the end, a security algorithm is utilized by the SDN controller that efficiently manages the consumption of the IoT nodes and protects it from unidentified occurrences. The proposed model is verified using simulations and improves system performance in terms of network throughput by 13%, data drop ratio by 39%, data delay by 11%, and faulty packets by 46% compared to HUNA and CMMA schemes.Haseeb, K.; Ahmad, I.; Iqbal Awan, I.; Lloret, J.; Bosch Roig, I. (2021). A Machine Learning SDN-Enabled Big Data Model for IoMT System. Electronics. 10(18):1-13. https://doi.org/10.3390/electronics10182228S113101

Inferring the Meaning of Non-personal, Anonymized, and Anonymous Data

On the awareness of the dynamism pertaining to data and its processing, this paper investigates the problem of having two mutually exclusive definitions of personal and non-personal data in the legal framework in force. The taxonomic analysis of key terms and their context of application highlights the risk to crystalize the whole system upon which the digital single market is built, suffocating its future development. With this premise, the paper discusses the extent of the two main data processing tools provided by the GDPR, questioning the ex-ante categorization of data and its outcome, supporting stakeholders in overcoming this issue

Setting Privacy "by Default" in Social IoT: Theorizing the Challenges and Directions in Big Data Research

[EN] The social Internet of Things (SIoT) shares large amounts of data that are then processed by other Internet of Thing (IoT) devices, which results in the generation, collection, and treatment of databases to be analyzed afterwards with Big Data techniques. This paradigm has given rise to users' concerns about their privacy, particularly with regard to whether users have to use a smart handling (self-establishment and self-management) in order to correctly install the SIoT, ensuring the privacy of the SIot-generated content and data. In this context, the present study aims to identify and explore the main perspectives that define user privacy in the SIoT; our ultimate goal is to accumulate new knowledge on the adoption and use of the concept of privacy "by default" in the scientific literature. To this end, we undertake a literature review of the main contributions on the topic of privacy in SIoT and Big Data processing. Based on the results, we formulate the following five areas of application of SIoT, including 29 key points relative to the concept of privacy "by default": (i) SIoT data collection and privacy; (ii) SIoT security; (iii) threats for SIoT devices; (iv) SIoT devices mandatory functions; and (v) SIoT and Big Data processing and analytics. In addition, we outline six research propositions and discuss six challenges for the SIoT industry. The results are theorized for the future development of research on SIoT privacy by "default" and Big Data processing.In gratitude to the Ministry of Science, Innovation and Uni-versities and the European Regional Development Fund: RTI2018-096295-B-C22.Saura, JR.; Ribeiro-Soriano, D.; Palacios Marqués, D. (2021). Setting Privacy "by Default" in Social IoT: Theorizing the Challenges and Directions in Big Data Research. Big Data Research. 25:1-12. https://doi.org/10.1016/j.bdr.2021.100245S1122