2 research outputs found
INTRUSION DETECTION USING FEDERATED LEARNING WITH NEURAL NETWORKS
No full textThe amount of information shared amongst different devices and the variety of novel methods of network crimes have exponentially increased in recent years because of the widespread use of the internet. Quick identification of all types of attacks would not be possible with conventional methods including firewalls, which focused on data filtering. Dealing with the timely recognition of these types of assaults is very successful for intrusion detection systems (IDS) grounded on ML algorithms. They can efficiently manage the enormous amount of data in order to identify any harmful behaviour. Every network activity is searched for any possibly dangerous activity using IDS based on machine learning. The main objective of the planned effort is to provide analytical analyses of such current intrusion detection systems. Furthermore, examined in this work are the useful data sets and several techniques already in use to develop an effective IDS using single, hybrid, and ensemble machine learning algorithms. The approaches in the literature have then been investigated under several criteria in line to provide a clear road and direction for the next projects that will be successful. Nowadays, companies of all kinds include an intrusion detection system (IDS), which inhibits cybercrime to protect the network, resources, and private data. Many strategies have been suggested and implemented up till now to prevent uncivil behaviour. Since machine learning (ML) approaches are successful, the proposed approach applied several ML models for the intrusion detection system. The CIC IOT 2023 Dataset is the one applied in this paper. Tested were several techniques including random forest, XG Boost, logistic regression, MLP model, and RNN. Following fine-tuning, the federated learning model using neural networks had the best accuracy—99.84%
Hodnocení strukturálních vlastností geopolymerního betonu na bázi metakaolinu
No full textLast few decades, there has been a substantial advancement of geopolymer (GP) as a Portland cement substitute. It is vital to investigate potential building uses for geopolymer concrete (GPC). Six different mixes were cast for an alkaline to binder (A/B) ratio of 0.25-0.50 with an interval of 0.05. Metakaolin-based geopolymer were cured at ambient tem-perature and tested for 7, 14, 28, and 90 days. Metakaolin-Marble (MM00) mix was observed to have a maximum slump. For an A/B ratio of 0.35, maximum compressive, split tensile, flexural strength and modulus of elasticity was achieved. For elevated temperature resis-tance, geopolymer concrete cubes were exposed to temperatures (T) of 200, 400, to 600 C. As the temperature increased, compressive strength (CS) reduced. As the increase of the alkaline to binder (A/B) ratio, the strength of geopolymer concrete increases up to a specific limit beyond the limit strength decline. An empirical formula for split tensile (STS) value prediction using compressive strength values is proposed, valid for determining split tensile strength value. The correlation between compressive strength, split tensile strength, flexural strength, and bulk density varies linearly for a quadratic polynomial.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).V posledních několika desetiletích došlo k podstatnému rozvoji geopolymeru (GP) jako náhrady portlandského cementu. Je nezbytné prozkoumat potenciální využití geopolymerního betonu (GPC) ve stavebnictví. Bylo odlito šest různých směsí pro poměr alkálie k pojivu (A/B) 0,25-0,50 s intervalem 0,05. Geopolymer na bázi metakaolinu byl vytvrzen při okolní teplotě a testován po dobu 7, 14, 28 a 90 dnů. Bylo pozorováno, že směs metakaolin-mramor (MM00) má maximální propad. Pro poměr A/B 0,35 bylo dosaženo maximálního tlaku, pevnosti v tahu, pevnosti v ohybu a modulu pružnosti. Pro zvýšenou teplotní odolnost byly geopolymerní betonové kostky vystaveny teplotám (T) 200, 400 až 600 C. Se zvyšující se teplotou se snižovala pevnost v tlaku (CS). Se vzrůstajícím poměrem zásaditosti k pojivu (A/B) se pevnost geopolymerního betonu zvyšuje až ke specifické hranici nad mezní pokles pevnosti. Je navržen empirický vzorec pro predikci hodnoty dělené pevnosti v tahu (STS) pomocí hodnot pevnosti v tlaku, platný pro stanovení hodnoty dělené pevnosti v tahu. Korelace mezi pevností v tlaku, pevností v tahu, pevností v ohybu a objemovou hmotností se pro kvadratický polynom mění lineárně.(c) 2022 The Author(s). Vydal Elsevier B.V. Toto je článek s otevřeným přístupem pod licencí CC BY-NC-ND (http://creativecommons.org/licenses/by-nc-nd/4.0/)
