Intelligent Cyber Attack Detection and Classification for Network-Based Intrusion Detection Systems

Oliveira, Nuno; Praça, Isabel; Maia, Eva; Sousa, Orlando

doi:10.3390/app11041674

Cited by 61 publications

(19 citation statements)

References 22 publications

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“…Early detection and elimination of cyber threats have become of the utmost importance in the contemporary world, be it individuals or mega-organisations dealing with electronic information and data. Adversaries no longer just rely on conventional attack strategies and are evolving over time, which brings about a need for us to develop and evolve the pre-existing defence action plans [119][120][121][122][123][124][125][126][127]. Through this paper, we tend to bring together the various approaches put forward in recent studies through AI-enabled techniques such as ML and DL to enhance a sense of security in mobile networks .…”

Section: Discussionmentioning

confidence: 99%

A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks

Gupta

Johri

Srinivasan

et al. 2022

Sensors

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Today’s advancements in wireless communication technologies have resulted in a tremendous volume of data being generated. Most of our information is part of a widespread network that connects various devices across the globe. The capabilities of electronic devices are also increasing day by day, which leads to more generation and sharing of information. Similarly, as mobile network topologies become more diverse and complicated, the incidence of security breaches has increased. It has hampered the uptake of smart mobile apps and services, which has been accentuated by the large variety of platforms that provide data, storage, computation, and application services to end-users. It becomes necessary in such scenarios to protect data and check its use and misuse. According to the research, an artificial intelligence-based security model should assure the secrecy, integrity, and authenticity of the system, its equipment, and the protocols that control the network, independent of its generation, in order to deal with such a complicated network. The open difficulties that mobile networks still face, such as unauthorised network scanning, fraud links, and so on, have been thoroughly examined. Numerous ML and DL techniques that can be utilised to create a secure environment, as well as various cyber security threats, are discussed. We address the necessity to develop new approaches to provide high security of electronic data in mobile networks because the possibilities for increasing mobile network security are inexhaustible.

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Section: Discussionmentioning

confidence: 99%

A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks

Gupta

Johri

Srinivasan

et al. 2022

Sensors

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show abstract

“…The ability to model the sequence is the primary benefit of a recurrent neural network (RNN) over a conventional network. Oliveira et al [31] developed an intelligent ID and classification framework using LSTM deep learning and evaluated the proposed framework by using the CIDDS-001 dataset to achieve a higher ID accuracy as compared with traditional ML approaches. The convolutional neural network (CNN) is another popular DL approach that learns directly from the dataset without requiring manual feature extraction algorithms.…”

Section: Related Workmentioning

confidence: 99%

HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System

Khan

2021

Processes

132

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Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks. Currently, deep learning (DL) and machine learning (ML) are being applied in different domains, especially information security, for developing effective ID systems. These ID systems are capable of detecting malicious threats automatically and on time. However, malicious threats are occurring and changing continuously, so the network requires a very advanced security solution. Thus, creating an effective and smart ID system is a massive research problem. Various ID datasets are publicly available for ID research. Due to the complex nature of malicious attacks with a constantly changing attack detection mechanism, publicly existing ID datasets must be modified systematically on a regular basis. So, in this paper, a convolutional recurrent neural network (CRNN) is used to create a DL-based hybrid ID framework that predicts and classifies malicious cyberattacks in the network. In the HCRNNIDS, the convolutional neural network (CNN) performs convolution to capture local features, and the recurrent neural network (RNN) captures temporal features to improve the ID system’s performance and prediction. To assess the efficacy of the hybrid convolutional recurrent neural network intrusion detection system (HCRNNIDS), experiments were done on publicly available ID data, specifically the modern and realistic CSE-CIC-DS2018 data. The simulation outcomes prove that the proposed HCRNNIDS substantially outperforms current ID methodologies, attaining a high malicious attack detection rate accuracy of up to 97.75% for CSE-CIC-IDS2018 data with 10-fold cross-validation.

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“…Also, this method alleviates the limitation of the decision tree model in which the error of the upper decision layer is propagated to the next stage through techniques such as bagging and randomized node optimization. In particular, when the random forest model is applied to the dataset for network attack and abnormal behavior detection, it shows a relatively high detection effect for attack types with a tiny data ratio [23].…”

Section: Random Forestmentioning

confidence: 99%

A Study on High-Speed Outlier Detection Method of Network Abnormal Behavior Data Using Heterogeneous Multiple Classifiers

2022

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As the complexity and scale of the network environment increase continuously, various methods to detect attacks and intrusions from network traffic by classifying normal and abnormal network behaviors show their limitations. The number of network traffic signatures is increasing exponentially to the extent that semi-realtime detection is not possible. However, machine learning-based intrusion detection only gives simple guidelines as simple contents of security events. This is why security data for a specific environment cannot be configured due to data noise, diversification, and continuous alteration of a system and network environments. Although machine learning is performed and evaluated using a generalized data set, its performance is expected to be similar in that specific network environment only. In this study, we propose a high-speed outlier detection method for a network dataset to customize the dataset in real-time for a continuously changing network environment. The proposed method uses an ensemble-based noise data filtering model using the voting results of 6 classifiers (decision tree, random forest, support vector machine, naive Bayes, k-nearest neighbors, and logistic regression) to reflect the distribution and various environmental characteristics of datasets. Moreover, to prove the performance of the proposed method, we experimented with the accuracy of attack detection by gradually reducing the noise data in the time series dataset. As a result of the experiment, the proposed method maintains a training dataset of a size capable of semi-real-time learning, which is 10% of the total training dataset, and at the same time, shows the same level of accuracy as a detection model using a large training dataset. The improved research results would be the basis for automatic tuning of network datasets and machine learning that can be applied to special-purpose environments and devices such as ICS environments.

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Intelligent Cyber Attack Detection and Classification for Network-Based Intrusion Detection Systems

Cited by 61 publications

References 22 publications

A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks

A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks

HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System

A Study on High-Speed Outlier Detection Method of Network Abnormal Behavior Data Using Heterogeneous Multiple Classifiers

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