Intrusion detection systems using long short-term memory (LSTM)

Laghrissi, FatimaEzzahra; Douzi, Samira; Douzi, Khadija; Hssina, Badr

doi:10.1186/s40537-021-00448-4

Cited by 122 publications

(62 citation statements)

References 24 publications

(25 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…IIoT routing attacks can be detected using Nayak et al The GRU is the most effective model for botnet detection; however, it is computationally costly, yet it can handle large amounts of data and identify sequences efficiently. Laghrissi et al [58] developed deep learning techniques for identifying attacks based on the LSTM algorithm. They employed PCA and Mutual information to reduce the data's dimensionality and choose the best features.…”

Section: Articlementioning

confidence: 99%

Design and Development of RNN Anomaly Detection Model for IoT Networks

Ullah

Mahmoud

2022

IEEE Access

View full text Add to dashboard Cite

Cybersecurity is important today because of the increasing growth of the Internet of Things (IoT), which has resulted in a variety of attacks on computer systems and networks. As the number of various IoT devices and services grows, cyber security will become an increasingly difficult issue to manage. Malicious traffic identification using deep learning techniques has emerged as a key component of networkbased intrusion detection systems (IDS). Deep learning methods have been a research focus in network intrusion detection. A recurrent neural network is useful in a wide range of applications. This paper proposes a novel deep learning model for detecting anomalies in IoT networks using recurrent neural networks. The proposed model is implemented in IoT networks utilizing LSTM, BiLSTM, and GRU-based approaches for anomaly detection. A convolutional neural network can analyze input features without losing important information, making them particularly well suited for feature learning. In addition, we propose a hybrid deep learning model based on convolutional and recurrent neural networks. Finally, employing LSTM, BiLSTM, and GRU-based techniques, we propose a lightweight deep learning model for binary classification. The proposed deep learning models are validated using NSLKDD, BoT-IoT, IoT-NI, MQTT, MQTTset, IoT-23, and IoT-DS2 datasets. Our proposed binary and multiclass classification model achieved high accuracy, precision, recall, and F1 score compared to current deep learning implementations.

show abstract

Section: Articlementioning

confidence: 99%

Design and Development of RNN Anomaly Detection Model for IoT Networks

Ullah

Mahmoud

2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…This year, LSTM is being considered as a possible model for an IDS network, such as the so-called DL-IDS, which is currently under development. According to the results of an experiment on hybrid PCA/LSTM [30], the DL-IDS has an accuracy rate of 98.67%. PCA is in charge of reducing the size of raw data attacks, while LSTM is in charge of categorizing network attacks.…”

Section: Related Workmentioning

confidence: 99%

Enhance Intrusion Detection (IDS) System Using Deep SDAE to Increase Effectiveness of Dimensional Reduction in Machine Learning and Deep Learning

2022

IJIES

View full text Add to dashboard Cite

The intrusion detection system (IDS) is very essential tools to detect malicious network. IDS is a hardware or software approach to observe the internet for malicious attacks. It has ability to screening an internet or network that possibility dangerous activity or security threats. IDS application responsible to defend network territory in accordance with the network-based intrusion detection system (NIDS) or host-based intrusion detection system (HIDS). Using known normal network activity signatures, IDS applications perform tasks by comparing them to known attack activity signatures. In this study, a dimensional reduction and feature selection mechanism known as the stack denoising auto encoder (SDAE) was found to be effective in increasing the effectiveness of naive bayes, KNN, decision tree, and SVM classification algorithms. The researchers evaluated the performance using evaluation metrics such as a confusion matrix, accuracy, recall, and the F1-score, among other measures of success. When compared to the results of previous studies in the IDS field, our model using statistical pre-processing, dimensional reduction based on SDAE success to increase the effectiveness of KNN, naive bayes, decision tree, SVM and deep learning using LSTM. We applied our experiment in the NSL-KDD Dataset. According to evaluation metrics using confusion matrix, accuracy, recall, and f1 that the effectiveness of our model achieve more than 2% over several previous work without statistical pre-processing and dimensional reduction based on SDAE. Furthermore, the use of statistical approaches and SDAE improved the accuracy of traditional machine learning and modern deep learning based on LSTM. Aims to improve the effectiveness of IDS detection in the future, it may be possible to integrate SDAE with another deep learning model such as MLP, CNN, attention, and GAN.

show abstract

“…Laghrissi et al 68 introduced intrusion detection systems for IoT devices based on neural networks and LSTM to detect anomalies. Also, some reduction algorithms were used to decrease the number of features, which helped faster detection with acceptable accuracy.…”

Section: Organization Of Iot Threat Detection Techniquesmentioning

confidence: 99%

Artificial intelligence empowered threat detection in the Internet of Things: A systematic review

Soltani

Rahmani

Bohlouli

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

Summary Internet of Things (IoT) is a new phenomenon that proposes novel business opportunities. IoT allows the world to be programmable and might provide several benefits for organizations. Based on the IoT survey, cyber‐security issues are among the most extensive and complicated challenges faced by IoT devices. Threat detection is considered a preventive measure against malware threats, ransomware, and attacks, which become more serious each year because of the dramatic rise in malware attacks. This article investigates threat detection techniques that fall into three categories: malware detection, attack detection, and ransomware detection, published from 2017 to August 2021. We examine solutions, techniques, features, classifiers, and tools proposed by IoT researchers. Some questions are proposed, and answering the questions may help the researchers suggest a more efficient solution in future works. Furthermore, the achievement and disadvantages of each study are discussed. Finally, based on the reviewed studies, some open challenges and practical measures to future directions are suggested, worth further studying and researching threat detection techniques in the IoT.

show abstract

Intrusion detection systems using long short-term memory (LSTM)

Cited by 122 publications

References 24 publications

Design and Development of RNN Anomaly Detection Model for IoT Networks

Design and Development of RNN Anomaly Detection Model for IoT Networks

Enhance Intrusion Detection (IDS) System Using Deep SDAE to Increase Effectiveness of Dimensional Reduction in Machine Learning and Deep Learning

Artificial intelligence empowered threat detection in the Internet of Things: A systematic review

Contact Info

Product

Resources

About