Deep learning-based feature extraction and optimizing pattern matching for intrusion detection using finite state machine

Abbasi, Junaid Shabbir; Bashir, Faisal; Qureshi, Kashif Naseer; Islam, Muhammad; Jeon, Gwanggil

doi:10.1016/j.compeleceng.2021.107094

Cited by 22 publications

(8 citation statements)

References 9 publications

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“…Thus, applying machine learning for IoT system security is considered an optimal opportunity to protect them from intrusion attacks, especially by detecting any outlier activity that emerges in the system. It is worth noting that machine learning also shows excellent performance in other areas such as [ 16 , 17 , 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning

Qaddoura

Al-Zoubi

Faris

et al. 2021

Sensors

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The security of IoT networks is an important concern to researchers and business owners, which is taken into careful consideration due to its direct impact on the availability of the services offered by IoT devices and the privacy of the users connected with the network. An intrusion detection system ensures the security of the network and detects malicious activities attacking the network. In this study, a deep multi-layer classification approach for intrusion detection is proposed combining two stages of detection of the existence of an intrusion and the type of intrusion, along with an oversampling technique to ensure better quality of the classification results. Extensive experiments are made for different settings of the first stage and the second stage in addition to two different strategies for the oversampling technique. The experiments show that the best settings of the proposed approach include oversampling by the intrusion type identification label (ITI), 150 neurons for the Single-hidden Layer Feed-forward Neural Network (SLFN), and 2 layers and 150 neurons for LSTM. The results are compared to well-known classification techniques, which shows that the proposed technique outperforms the others in terms of the G-mean having the value of 78% compared to 75% for KNN and less than 50% for the other techniques.

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Section: Introductionmentioning

confidence: 99%

A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning

Qaddoura

Al-Zoubi

Faris

et al. 2021

Sensors

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“…The implemented library is based on Bioinfokit. 1 Both computational time and accuracy are calculated and evaluated in the experiments. The runtime is measured in seconds, and the accuracy is determined by prediction rate (PR) and intrusion detection rate (IR) measures, which are respectively defined as: The baseline algorithms used in these experiments are RNN-LF [4], and Tripres [40] for flow prediction, and kNN-TF [11], and LOF-TF [14] for intrusion detection, which are all the state-of-the-art models for comparisons.…”

Section: Experimental Environmentmentioning

confidence: 99%

“…Various types of sensors deployed in IoT settings yields the generation of massive data that needs to be analyzed. Deep learning based solutions [1,18,32] performed well for solving IoT problems such as intrusion detection, and prediction. However, they suffer from two main challenges:…”

Section: Introductionmentioning

confidence: 99%

When explainable AI meets IoT applications for supervised learning

et al. 2022

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This paper introduces a novel and complete framework for solving different Internet of Things (IoT) applications, which explores eXplainable AI (XAI), deep learning, and evolutionary computation. The IoT data coming from different sensors is first converted into an image database using the Gamian angular field. The images are trained using VGG16, where XAI technology and hyper-parameter optimization are introduced. Thus, analyzing the impact of the different input values in the output and understanding the different weights of a deep learning model used in the learning process helps us to increase interpretation of the overall process of IoT systems. Extensive testing was conducted to demonstrate the performance of our developed model on two separate IoT datasets. Results show the efficiency of the proposed approach compared to the baseline approaches in terms of both runtime and accuracy.

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“…The rules have to be cleverly crafted so they can be applied to multiple attacks. Rules can detect one type or several types of intrusion activity [6]- [8].…”

Section: Introductionmentioning

confidence: 99%

Implementation of signature-based intrusion detection system using SNORT to prevent threats in network servers

2022

joscex

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Security is an important factor in today's digital era. In a network, implementing a security system is the focus of a network developer. One of the most basic network securities is in the form of access. To manage the security of a system must be known in advance who is involved in the system and what activities are carried out. Just like a security alarm, which monitors work conditions, this is the function of the Intrusion Detection System (IDS). IDS has several effective methods for detecting threats, one of which is the Signature-based method. IDS can be implemented through the open-source SNORT application, and the method works with rules which are commands to IDS to recognize various attacks. IDS rules will be included in the signature matching process, which means matching between rules and incoming attacks and views of both protocols, then the IDS will generate alerts that contain notifications. This study conducted a reading of the MIT-DARPA 1999 dataset on 1,252,412 packages and tested alerting with Network Scanning and DoS attacks. Analyze Package Data runs at a speed of 83,494 packets /second and gets a true positive percentage reaching 100% and an accuracy of 98.10%.

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Deep learning-based feature extraction and optimizing pattern matching for intrusion detection using finite state machine

Cited by 22 publications

References 9 publications

A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning

A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning

When explainable AI meets IoT applications for supervised learning

Implementation of signature-based intrusion detection system using SNORT to prevent threats in network servers

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