Intrusion Detection System based on Chaotic Opposition for IoT Network

The rapid advancement of network technologies and protocols has fueled the widespread endorsement of the Internet of Things (IoT) in numerous domains, including everyday life, healthcare, industries, agriculture, and more. However, this rapid growth has also given rise to numerous security concerns within IoT systems. Consequently, privacy and security have become paramount issues in the IoT framework. Due to the heterogeneous data produced by smart IoT devices, traditional intrusion detection system doesn\u27t work well with IoT system. The massive volume of heterogeneous data has several irrelevant, redundant, and unnecessary features which lead to high computation time and low accuracy of IDS. Therefore, to tackle these challenges, this paper presents a novel metaheuristic-based IDS model for the IoT systems. The chaotic opposition-based Harris Hawk optimization (CO-IHHO) algorithm is used to perform the feature selection of data traffic. The chosen features are subsequently inputted into a machine learning (ML) classifier to detect network traffic intrusions. The performance of the CO-IHHO based IDS model is verified against the BoT-IoT dataset. Experimental findings reveal that CO-IHHO-DT achieves the maximal accuracy of 99.65% for multiclass classification and 100% for binary classification, and minimal computation time of 31.34 sec for multiclass classification and 133.54 sec for binary classification

Dynamic set kNN self-join

In many applications, data objects can be represented as sets. For example, in video on-demand and social network services, the user data consists of a set of movies that have been watched and a set of users (friends), respectively, and they can be used for recommendation and information extraction. The problem of set similarity self-join hence has been studied extensively. Existing studies assume that sets are static, but in the above applications, sets are dynamically updated, and this requires continuous updating the join result. In this paper, we study a novel problem, dynamic set kNN self-join, i.e., for each set, we continuously compute its k nearest neighbor sets. Our problem poses a challenge for the efficiency of computation, because just an element insertion (deletion) into (from) a set may affect the kNN results of many sets. To address this challenge, we first investigate the property of the dynamic set kNN self-join problem to observe the search space derived from a set update. Then, based on this observation, we propose an efficient algorithm. This algorithm employs an indexing technique that enables incremental similarity computation and prunes unnecessary similarity computation. Our empirical studies using real datasets show the efficiency and scalability of our algorithm.Amagata D., Hara T., Xiao C.. Dynamic set kNN self-join. Proceedings - International Conference on Data Engineering 2019-April, 818 (2019); https://doi.org/10.1109/ICDE.2019.00078

Harvesting Intelligence: A Comprehensive Study on Transforming Aquaponic Agriculture with AI and IoT

Aquaponics, an agricultural technique that merges aquaculture and hydroponics, is on the brink of a transformative advancement with the amalgamation of Machine Learning (ML), Deep Learning (DL), and the Internet of Things (IoT). The incorporation of these cutting edge technologies in the field of aquaponics is bringing about a profound transformation in the realm of sustainable agriculture. This extensive investigation delves into the profound influence of these cutting-edge technologies on aquaponics, with a focus on predictive analysis, system optimization, environmental monitoring, and disease prevention. By means of ML and DL algorithms, historical and real-time data are scrutinized in order to forecast environmental fluctuations, optimize resource allocation, and facilitate the growth of crops and fish. IoT devices consistently gather data pertaining to crucial parameters, thereby enabling real-time monitoring and control of the aquaponic system. Furthermore, IoT technology enhances resource utilization and grants the ability to remotely monitor and manage the system. The detection of abnormalities in fish behavior and plant health through the utilization of ML and DL algorithms allows for the implementation of proactive measures aimed at preventing outbreaks and minimizing losses. Furthermore, these advanced technologies also offer personalized recommendations for effective management of various crop and fish species. The incorporation of ML, DL, and IoT into the field of aquaponics signifies a substantial advancement towards a more sustainable, efficient, and productive form of agriculture. These innovative technologies possess the capability to effectively address the challenges associated with global food security by optimizing the utilization of resources, maintaining environmental equilibrium, and mitigating the occurrence of disease outbreaks. In the context of the examined research endeavors presented in this article, it is anticipated that the utilization of smart control units in conjunction with the aquaponics system will yield greater profitability, increased intelligence, enhanced precision, and heightened efficacy. In the context of the examined research endeavors presented in this article, it is anticipated that the utilization of ML, DL and IoT in conjunction with the aquaponics system will yield greater profitability, increased intelligence, enhanced precision, and heightened efficacy

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars