Machine learning in cybersecurity: A review

Handa, Anand; Sharma, Ashu; Shukla, Sandeep K.

doi:10.1002/widm.1306

Cited by 103 publications

(55 citation statements)

References 48 publications

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“…This research study the effectiveness Bi-directional recurrent neural network( BRNN) for intrusion detection which has provided promising results compared to some literature work. The full KDD Cup'99-intrusion detection dataset [5,32] were used to evaluate the algorithm.…”

Section: International Journal Of Advanced Engineering Research and Science (Ijaers)mentioning

confidence: 99%

Bi-directional Recurrent Neural network for Intrusion Detection System (IDS) in the internet of things (IoT)

Dushimimana¹,

Tao²,

Kindong³

et al. 2020

IJAERS

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With IoT technology bringing a large number of day-to-day objects into the digital fold to make them smarter. It is also evident that the IoT technology is going to transform into a multi-trillion-dollar industry in the near future. However, the reality is that IoT bandwagon rushing full steam ahead is prone to countless cyberattacks in the extremely hostile environment like the internet. Nowadays, standard Personal Computer (PC) security solutions won't solve the challenge of privacy and data security transmitted over the internet. In this research, we have applied a Bidirectional Recurrent Neural Network (BRNN) to build a security solution with high durability for IoT network security. Deep learning and Machine learning have shown remarkable result in dealing with multimodal and voluminous heterogenous data in regards to intrusion detection especially with the architecture of Recurrent Neural Network(RNN). Feature selection mechanisms were also implemented to help identify and remove non-essential variables from data that does not affect the accuracy of the prediction model. In this case a Random Forest (RF) algorithm was implemented over Principal Component Analysis (PCA) because of flexibility, and easy in using machine learning algorithm that allows production without hyper-parameter tuning, building of multiple decision trees and merging them together to get a more accurate and stable prediction. In this study a novel algorithm (BRNN) out-performed both RNN(Recurrent Neural Network) and GRNN(Gated Recurrent Neural Network) because it considers both information from the past and the future with back and forward hidden neurons.

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Section: International Journal Of Advanced Engineering Research and Science (Ijaers)mentioning

confidence: 99%

Bi-directional Recurrent Neural network for Intrusion Detection System (IDS) in the internet of things (IoT)

Dushimimana¹,

Tao²,

Kindong³

et al. 2020

IJAERS

View full text Add to dashboard Cite

show abstract

“…According to [64], despite the fact that the awareness of cyber-threats to building operations among FM professionals is high, there is no adequate standardization of the role specific responsibilities with regards to the mitigation of concerns. [65] present the use of machine learning based applications for the detection of cyber-attacks, noting that the manipulation of such tools is a major growing concern.…”

Section: Cyber Security and Data Privacymentioning

confidence: 99%

Drivers and Challenges Associated With the Implementation of Big Data Within U.K. Facilities Management Sector: An Exploratory Factor Analysis Approach

Konanahalli

Marinelli

Oyedele

2022

IEEE Trans. Eng. Manage.

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“…In [ 4 ], the study offers a scientific classification of some dangers of the existing IoT security, and provides a guide for novel and energizing research difficulties in applying ML and SDN (Software-Defined Network) ideas to address IoT security concerns. In [ 5 ], the authors reviewed the applications of ML in cybersecurity. There has also been a debate on the risks of utilizing cyber attacks as training and testing data for classification.…”

Section: Related Workmentioning

confidence: 99%

Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System

Rim

Praça

2021

Sensors

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The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).

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Machine learning in cybersecurity: A review

Cited by 103 publications

References 48 publications

Bi-directional Recurrent Neural network for Intrusion Detection System (IDS) in the internet of things (IoT)

Bi-directional Recurrent Neural network for Intrusion Detection System (IDS) in the internet of things (IoT)

Drivers and Challenges Associated With the Implementation of Big Data Within U.K. Facilities Management Sector: An Exploratory Factor Analysis Approach

Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System

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