Deep Learning-Based Intrusion Detection for IoT Networks

Ge, Mengmeng; Fu, Xiping; Syed, Naeem Firdous; Baig, Zubair A.; Teo, Gideon; Robles-Kelly, Antonio

doi:10.1109/prdc47002.2019.00056

Cited by 166 publications

(104 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Another IoT dataset is BoT-IoT [ 35 ]. Such dataset has been adopted for different applications, such as to train deep learning based intrusion detection systems [ 36 ] or to train a C5 classifier and a One Class Support Vector Machine classifier to detect cyber-threats on the network [ 37 ]. In BoT-IoT, MQTT is exploited for communications with AWS services.…”

Section: Related Workmentioning

confidence: 99%

MQTTset, a New Dataset for Machine Learning Techniques on MQTT

Vaccari

Chiola

Aiello

et al. 2020

Sensors

166

View full text Add to dashboard Cite

IoT networks are increasingly popular nowadays to monitor critical environments of different nature, significantly increasing the amount of data exchanged. Due to the huge number of connected IoT devices, security of such networks and devices is therefore a critical issue. Detection systems assume a crucial role in the cyber-security field: based on innovative algorithms such as machine learning, they are able to identify or predict cyber-attacks, hence to protect the underlying system. Nevertheless, specific datasets are required to train detection models. In this work we present MQTTset, a dataset focused on the MQTT protocol, widely adopted in IoT networks. We present the creation of the dataset, also validating it through the definition of a hypothetical detection system, by combining the legitimate dataset with cyber-attacks against the MQTT network. Obtained results demonstrate how MQTTset can be used to train machine learning models to implement detection systems able to protect IoT contexts.

show abstract

Section: Related Workmentioning

confidence: 99%

MQTTset, a New Dataset for Machine Learning Techniques on MQTT

Vaccari

Chiola

Aiello

et al. 2020

Sensors

166

View full text Add to dashboard Cite

show abstract

“…In our previous work [3], we adopted a Feed forward Neural Netowrk (FNN) to train and classify attacks for a BoT-IoT dataset. The FNN model achieved > 99% accuracy and a high F1 score in detecting several attack classes.…”

Section: Related Workmentioning

confidence: 99%

“…Hence instead of performing a mutli-class attack detection, we chose to convert this dataset into a binary classification problem by separating the attack instances into sub-datasets containing only one attack category and normal traffic during that time period arranged according to the packet arrival time. The first step in detecting intrusions is the conversion of the raw network traffic data into packet level features, which is discussed in detail in [3]. We used 29 packet header fields as features including fields in frame, IP, TCP/UDP, and HTTP header.…”

Section: Proposed Frameworkmentioning

confidence: 99%

“…In order to prevent these intrusions that target IoT devices, superior Intrusion Detection Systems (IDS) are required that do not just rely on attack signatures and instead use traffic characteristics to detect anomalous network connections. To address this problem a number of Deep Learning (DL) based IDS techniques have been proposed [2] which have superior detection performance compared to the traditional Machine Learning (ML) techniques such as SVM [3].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Intrusion Detection for Time-Series IoT Data with Recurrent Neural Networks and Feature Selection

Syed¹,

Ge²,

Baig³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

<div>Deep learning (DL) techniques are being widely researched for their effectiveness in detecting cyber intrusions against the Internet of Things (IoT). However, time sensitive Critical Infrastructures (CIs) that rely on IoT require quicker detection of cyber intrusions close to the constrained devices in order to prevent service delays. FoG layer with higher computing resources and close proximity to IoT devices makes them suitable to deploy deep learning techniques for effective intrusion detection. However, communication overheads due to large volume of IoT data and computation requirements for deep learning models prevents effective use of FoG layer and raises scalability issues. To counter these issues, we propose a novel IoT intrusion detection framework incorporates feature selection step on time-series IoT data, followed by a deep learning Recurrent Neural Network (SimpleRNN and Bi-directional Long Short-Term Memory (LSTM)) based IoT attack detection. The effectiveness of the proposed approach was evaluated using the high-dimensional BoT-IoT dataset which contains large volumes of realistic IoT attack traffic. Results show that feature selection methods significantly ( 90%) reduced the dataset size and still achieved an increased recall rate compared to full feature set without loosing class differentiation ability. The SimpleRNN and Bi-LSTM models also did not suffer any underfitting or overfitting with the reduced feature space. The proposed deep learning based IoT intrusion detection framework is suitable for FoG layer deployment and can scale well with effective use of sub-divided dataset.</div>

show abstract

“…Similar algorithm is proposed in [15] where deep learning is utilized for traffic flow intrusion detection in IoT networks. The proposed method generates the generic features from packet-level information.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Hybrid Intrusion Detection System (IDS) for the Detection of Internet of Things (IoT) Network Attacks

Ramadan¹,

Yadav²

2020

AETiC

View full text Add to dashboard Cite

Nowadays, IoT has been widely used in different applications to improve the quality of life. However, the IoT becomes increasingly an ideal target for unauthorized attacks due to its large number of objects, openness, and distributed nature. Therefore, to maintain the security of IoT systems, there is a need for an efficient Intrusion Detection System (IDS). IDS implements detectors that continuously monitor the network traffic. There are various IDs methods proposed in the literature for IoT security. However, the existing methods had the disadvantages in terms of detection accuracy and time overhead. To enhance the IDS detection accuracy and reduces the required time, this paper proposes a hybrid IDS system where a pre-processing phase is utilized to reduce the required time and feature selection as well as the classification is done in a separate stage. The feature selection process is done by using the Enhanced Shuffled Frog Leaping (ESFL) algorithm and the selected features are classified using Light Convolutional Neural Network with Gated Recurrent Neural Network (LCNN-GRNN) algorithm. This two-stage method is compared to up-to-date methods used for intrusion detection and it over performs them in terms of accuracy and running time due to the light processing required by the proposed method.

show abstract

Deep Learning-Based Intrusion Detection for IoT Networks

Cited by 166 publications

References 15 publications

MQTTset, a New Dataset for Machine Learning Techniques on MQTT

MQTTset, a New Dataset for Machine Learning Techniques on MQTT

Intrusion Detection for Time-Series IoT Data with Recurrent Neural Networks and Feature Selection

A Novel Hybrid Intrusion Detection System (IDS) for the Detection of Internet of Things (IoT) Network Attacks

Contact Info

Product

Resources

About