Secure Mechanism Applied to Big Data for IIoT by Using Security Event and Information Management System (SIEM)

Light fidelity (Li-Fi) can be defined as a type of wireless technology that sends data via light waves through LED light bulbs. Light fidelity (Li-Fi) offers high-speed data transmission capabilities and a large unlicensed bandwidth, making it a promising technology for the future. However, factors including interference, wall reflection, and blocking may cause the quality of a light fidelity (Li-Fi) channel to vary from one part of a room to another. Another type of wireless communication technology that offers broad coverage and slow transmission rates is wireless fidelity (Wi-Fi). Since the electromagnetic spectrums regarding such two technologies do not overlap, there is a possibility for building a hybrid Wi-Fi and Li-Fi network that provides seamless and high throughput global communication. One wireless fidelity access point (Wi-Fi AP) and four light fidelity access points (Li-Fi Aps) make up the downlink hybrid system we discuss in this work. Finding an access point (AP) assignment method that will increase long-term system throughput at the same time as still guaranteeing users' pleasure and fairness is challenging. Thus, the authors suggest using a reinforcement learning (RL) algorithm. The algorithm aims to balance the load of multiple access points (APs) by considering both the LiFi and Wi-Fi channels. The results obtained using MATLAB code for a hybrid system based on reinforcement learning (RL) and a standard solution set size (SSS) access technology called TDMA. The system was evaluated in terms of throughput and user satisfaction for 5 users. According to the results, the RL-based hybrid system achieved a throughput of up to 210 Mbps and an SSS of 180 Mbps. Additionally, the user satisfaction was reported to be 100%.

“…With a mean value of the power of 2.46 dB, small-scale fading gain hr follows independent identical Rayleigh distributions, while L (d) [17] can be determined by:…”

Section: B Wi-fi Channel Modelmentioning

confidence: 99%

Enhancing Hybrid System Based on Reinforcement Learning

2024

“…If you're going to implement a WSN system on a massive scale, data security must be a top priority. More stringent security procedures are essential for protecting WSN systems [3,4].…”

Section: Introductionmentioning

confidence: 99%

An Improved Intrusion Detection System Using Machine Learning with Singular Value Decomposition and Principal Component Analysis

2023

Among the most crucial components of a cyber physical system is a network of nodes via which a large number of autonomous, mobile or stationary sensors can communicate with one another. This network is known as a wireless sensor network (WSN). The network's nodes work together to sense the world around them, collect data on the items they detect, process that data, and then communicate it on to the network's owner since WSN has many potentials uses across many disciplines but little available resources, it is often coupled with IOT, making the network accessible to the outside world and susceptible to cyberattacks. Blackhole, grayhole, flooding, and scheduling attacks are some examples of common attacks in WSN that can do significant damage rapidly. Intrusion detection approaches for WSN suffer from issues like a low detection rate, a large calculation overhead, and a high false alarm rate because of the network's redundant and highly correlated data and the constraints imposed by sensor nodes' limited resources which is the research problem. This research proposes a solution, dubbed IDS-ML, that makes use of three different machine learning techniques-stochastic gradient descent (SGD), ridge regression (RR), and gaussian naive bayesto solve the problem of intrusion detection in wireless sensor networks (GNB). In order to reduce the computational burden of the technique, principal component analysis (PCA) and singular value decomposition (SVD) are applied to the original traffic data to lower the feature space dimension. Once network threats have been identified, an IDS-ML model is utilized to categorize them. Based on the experiments with two datasets WSN-DS and UNSW-NB15, the proposed IDS-ML achieves significantly higher accuracy rate of 99% than state-of-the-art detection algorithms for WSN-DS and UNSW-NB15 dataset. As the achieved higher accuracy rate against normal, blackhole, grayhole, flooding, and TDMA attacks are 99%, 100%, 72%, 100%, and 78% respectively.

“…The term "data swamp" refers to useless, undocumented, or inexploitable data. Therefore, enhancing the value of big data is made possible in large part by the data lake metadata management system, also known as "data intelligence" or "largescale data" [2]. Thus, the data lake concept was first introduced by Dixon, as an efficient solution to the gaps introduced by data marts [3].…”

Section: Introductionmentioning

confidence: 99%

EMEMODL: Extensible Metadata Model for Big Data Lakes

2023

The evolution of the huge amount of heterogeneous data sources introduces the emergence of several fresh new concepts. Among one of the most well-known concepts emerging as a recent and trending topic in big data is the data lake. Which represents a central repository that stores heterogeneous data sources in its native format without any predefined schema. In the absence of any enforced schema, effective metadata management based on metadata models remains an active research topic to address the data lake issues knowing by a data swamp. However, the examination of existing metadata models shows that none of them proposes a complete model. In this article, we propose a generic and extensible metadata model that supports high flexibility and better scalability in the integration of metadata. With these capabilities, EMEMODL enables comprehensive metadata management for data lakes. We show EMEMODL's feasibility through a prototypical implementation based on TPC-H datasets of different sizes to prove the scaling feature, using MySQL and Neo4J. The findings of these experiments revealed encouraging results for big data queries that give us a graph database, which shows an efficient ability to manage and process large amounts of data compared to a relational database in terms of retrieval time queries and resource consumption, including cpu and memory usage.