Deep learning offering resilience from trending cyber-attacks, a review

Khanday, Shahbaz Ahmad; Fatima, Hoor; Rakesh, Nitin

doi:10.1109/compe53109.2021.9752099

Cited by 1 publication

(1 citation statement)

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“…Table 1 of the manuscript contains the acronyms used throughout the manuscript However, many machine learning-based intrusion detection models can benefit from extensible data preprocessing procedures to overcome noise and extract meaningful insights from large data chunks. Furthermore, several reasons make identifying and stopping attacks against IoT devices difficult (Khanday et al, 2021). For instance, scattered connections and lightweight devices having constrained resources and without security features could make identifying and mitigating threats more difficult.…”

Section: Introductionmentioning

confidence: 99%

A Novel Data Preprocessing Model for Lightweight Sensory IoT Intrusion Detection

Khanday,

Fatima,

Rakesh

2024

Int. j. math. eng. manag. sci.

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IoT devices or sensor nodes are essential components of the machine learning (ML) application workflow because they gather abundant information for building models with sensors. Uncontrollable factors may impact this process and add inaccuracies to the data, raising the cost of computational resources for data preparation. Choosing the best method for this data pre-processing stage can lessen the complexity of ML models and wasteful bandwidth use for cloud processing. Devices in the IoT ecosystem with limited resources provide an easy target for attackers, who can make use of these devices to create botnets and spread malware. To repel attacks directed towards IoT, robust and lightweight intrusion detection systems are the need of an hour. Furthermore, data preprocessing remains the first step for modish machine learning models, ensemble techniques, and hybrid methods in developing anti-intrusion applications for lightweight IoT. This article proposes a novel data preprocessing model as a core structure using an Extra Tree classifier for feature selection and two classifiers LSTM and 1D-CNN for classification. The dataset used in this research is CIC IoT 2023 with 34 attack classes and SMOTE (Synthetic Memory Oversampling Technique) has been used for class balancing. The article evaluates the performance of 1D-CNN and LSTM on the CIC IoT 23 dataset using classification metrics. The proposed ensemble approach using LSTM has obtained 92% accuracy and with 1D-CNN the model obtained 99.87% accuracy.

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Section: Introductionmentioning

confidence: 99%