Convolutional Neural Networks with LSTM for Intrusion Detection

A variety of attacks are regularly attempted at network infrastructure. With the increasing development of artificial intelligence algorithms, it has become effective to prevent network intrusion for more than two decades. Deep learning methods can achieve high accuracy with a low false alarm rate to detect network intrusions. A novel approach using a hybrid algorithm of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) is introduced in this paper to provide improved intrusion detection. This bidirectional algorithm showed the highest known accuracy of 99.70% on a standard dataset known as NSL KDD. The performance of this algorithm is measured using precision, false positive, F1 score, and recall which found promising for deployment on live network infrastructure.

Detection of Cyberattacks Traces in IoT Data

JUCS - Journal of Universal Computer Science ◽

10.3897/jucs.2020.075 ◽

2020 ◽

Vol 26 (11) ◽

pp. 1422-1434

Author(s):

Vibekananda Dutta ◽

Michał Choraś ◽

Marek Pawlicki ◽

Rafał Kozik

Keyword(s):

Machine Learning ◽

Intrusion Detection ◽

Short Term Memory ◽

Research Network ◽

Learning Approaches ◽

Short Term ◽

Term Memory ◽

Network Intrusion ◽

Artificial Intelligence plays a significant role in building effective cybersecurity tools. Security has a crucial role in the modern digital world and has become an essential area of research. Network Intrusion Detection Systems (NIDS) are among the first security systems that encounter network attacks and facilitate attack detection to protect a network. Contemporary machine learning approaches, like novel neural network architectures, are succeeding in network intrusion detection. This paper tests modern machine learning approaches on a novel cybersecurity benchmark IoT dataset. Among other algorithms, Deep AutoEncoder (DAE) and modified Long Short Term Memory (mLSTM) are employed to detect network anomalies in the IoT-23 dataset. The DAE is employed for dimensionality reduction and a host of ML methods, including Deep Neural Networks and Long Short-Term Memory to classify the outputs of into normal/malicious. The applied method is validated on the IoT-23 dataset. Furthermore, the results of the analysis in terms of evaluation matrices are discussed.

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering - Wireless Internet ◽

Using Long-Short-Term Memory Based Convolutional Neural Networks for Network Intrusion Detection

10.1007/978-3-030-06158-6_9 ◽

2019 ◽

pp. 86-94 ◽

Cited By ~ 1

Author(s):

Chia-Ming Hsu ◽

He-Yen Hsieh ◽

Setya Widyawan Prakosa ◽

Muhammad Zulfan Azhari ◽

Jenq-Shiou Leu

Keyword(s):

Neural Networks ◽

Intrusion Detection ◽

Convolutional Neural Networks ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Network Intrusion ◽

Study of long short-term memory in flow-based network intrusion detection system

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-169836 ◽

2018 ◽

Vol 35 (6) ◽

pp. 5947-5957 ◽

Cited By ~ 3

Author(s):

Lee Nicholas ◽

Shih Yin Ooi ◽

Ying Han Pang ◽

Seong Oun Hwang ◽

Syh-Yuan Tan

Keyword(s):

Intrusion Detection ◽

Intrusion Detection System ◽

Short Term Memory ◽

Detection System ◽

Short Term ◽

Term Memory ◽

Network Intrusion ◽

Network Intrusion Detection System ◽

Network Intrusion Detection Based on an Improved Long-Short-Term Memory Model in Combination with Multiple Spatiotemporal Structures

Wireless Communications and Mobile Computing ◽

10.1155/2021/6623554 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Xiaolong Huang

Keyword(s):

Intrusion Detection ◽

Short Term Memory ◽

Short Term ◽

Detection Model ◽

Existing Problems ◽

Network Intrusion ◽

The Neural Network ◽

Long Short Term Memory ◽

Better Than

Aimed at the existing problems in network intrusion detection, this paper proposes an improved LSTM combined with spatiotemporal structure for intrusion detection. The unsupervised spatiotemporal encoder is used to intelligently extract the spatial characteristics of network traffic data samples. It can not only retain the overall/nonlocal characteristics of the data samples but also extract the most essential deep features of the data samples. Finally, the extracted features are used as input of the LSTM model to realize classification and identification for intrusion samples. Experimental verification shows that the accuracy and false alarm rate of the intrusion detection model based on the neural network are significantly better than those of other traditional models.