MCIDS-Multi Classifier Intrusion Detection system for IoT Cyber Attack using Deep Learning algorithm

Singh, S. P.; Fernandes, Swedel Viola; Padmanabha, Vaibhav; Rubini, P

doi:10.1109/icicv50876.2021.9388579

Cited by 12 publications

(3 citation statements)

References 29 publications

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“…There will be no experiments on actual data. Identification and causation of cyberattacks in gas pipelines and water purification facilities High computational cost [39] Convolution Neural Networks (CNN)…”

Section: Nsl-kdd Using Integrated Protection For Identifying Threats ...mentioning

confidence: 99%

Screening Cyberattacks and Fraud via Heterogeneous Layering

Alahmadi

2024

IJACSA

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On the Internet of Things (IoT) age, intelligent equipment is employed to give effective and dependable utilization of applications. IoT devices may recognize and provide extensive information while also intelligently processing that data. Data systems, systems for control, plus sensing are growing increasingly vital in contemporary manufacturing processes. The amount of internet of things gadgets and methods used is growing, that has culminated in a rise in assaults. Such assaults have the potential to interrupt international activities and cause major financial losses. Multiple methods, including Machine learning (ML) in addition to Deep Learning (DL), are being utilized for identifying cyberattack. In this investigation, researchers offer an ensemble staking approach that is strong strategy in ML for detecting assaults via the Internet of Things having excellent accuracy. Tests were carried out using three distinct information: credit card data, NSL-KDD, and UNSW. Single fundamental classifications were beaten by the suggested layered ensembles classification. The results show that the cyberattack detection model in this research possessed a 95.15% accuracy percentage, while the credit card fraud detection model achieved a 93.50% accuracy percentage.

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Section: Nsl-kdd Using Integrated Protection For Identifying Threats ...mentioning

confidence: 99%

Screening Cyberattacks and Fraud via Heterogeneous Layering

Alahmadi

2024

IJACSA

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“…After analyzing the advancement of technology, the introduction and proliferation of cyber threats and attacks has grown exponentially due to the ever-present use of the Internet [5], computers, clever phones and tablets. As a result, anti-virus businesses and researchers have developed new procedures to detect and classify cyber threads [6].…”

Section: Related Workmentioning

confidence: 99%

Advanced cyber security using Spectral entity feature selection based on Cyber Crypto Proof Security Protocol (C2PSP)

Gopalakrishnan

Saikia²,

Vallem³

2023

MJCS

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The growth of internet become more development in communication medium to provide various services. Information sharing ad security is mostly suffered by crime attackers because of different models of cyber-attacks are carried out by attackers. Attackers creates jamming principles, communication delays, packet dropping, and information hacking, duplicate injection to do so many activities to destroy the security. Based on the communication data analysis and features are non-identified and difficult to find the malicious activities. So the development of cyber security needs advancement to find the attackers based on the communication breaking activities. To resolve this problem, we propose a Spectral entity feature selection based Cyber Crypto Proof Security Protocol (C2PSP) to improve the cyber security. The Defect Scaling Rate (DSR) is used to estimate the communication defect rate. By marginalize the scaling rate using Spectral entity feature selection approach (SEFSA) is applied to select the features and trained to identify with Artificial neural network classifier (ANN). Based on the attack principles and activities in communication medium, the Cyber Crypto Proof Security Protocol (C2PSP) is applied to ensure the security verification and validation to process the data safer and securely. The proposed system produce high performance compared to other system as well to identify the malicious activities to improve the security against the cyber-attacks

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“…First, deep representational learning detects control system imbalances; second, DNNs allocate assaults. [28] developed an MCIDS based on deep learning to recognize attacks using shellcode, generalist, worms, espionage, analysis, DoS, and fuzzes. [29] overcame the difficulty of changing data in the communications infrastructure that endangers cyber-physical systems.…”

Section: Ids For Smart Homes Precision Fmeasure Recallmentioning

confidence: 99%

A Novel Ensemble Model Using Learning Classifiers to Enhance Malware Detection for Cyber Security Systems

Das

Pramod

2022

IJRITCC

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In the Internet of Things arena, smart gadgets are employed to offer quick and dependable access to services. IoT technology has the ability to recognize extensive information, provide information reliably, and process that information intelligently. Data networks, controllers, and sensors are increasingly used in industrial systems nowadays. Attacks have increased as a result of the growth in connected systems and the technologies they employ. These attacks may interrupt international business and result in significant financial losses. Utilizing a variety of methods, including deep learning (DL) and machine learning (ML), cyber assaults have been discovered. In this research, we provide an ensemble staking approach to efficiently and quickly detect cyber-attacks in the IoT. The NSL, credit card, and UNSW information bases were the three separate datasets used for the experiments. The suggested novel combinations of ensemble classifiers are done better than the other individual classifiers from the base model. Additionally, based on the test outcomes, it could be concluded that all tree and bagging-based combinations performed admirably and that, especially when their corresponding hyperparameters are set properly, differences in performance across methods are not significant statistically. Additionally, compared to other comparable PE (Portable Executable) malware detectors that were published recently, the suggested tree-based ensemble approaches outperformed them.

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MCIDS-Multi Classifier Intrusion Detection system for IoT Cyber Attack using Deep Learning algorithm

Cited by 12 publications

References 29 publications

Screening Cyberattacks and Fraud via Heterogeneous Layering

Screening Cyberattacks and Fraud via Heterogeneous Layering

Advanced cyber security using Spectral entity feature selection based on Cyber Crypto Proof Security Protocol (C2PSP)

A Novel Ensemble Model Using Learning Classifiers to Enhance Malware Detection for Cyber Security Systems

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