Secure routing with multi-watchdog construction using deep particle convolutional model for IoT based 5G wireless sensor networks

Information security depends on Network Intrusion Detection (NID), which properly identifies network threats. This work explores simulating a NID system by stacking ensemble classifiers with various feature selection methods. We used the NSL-KDD dataset for the investigation. The binary classification performance integrates all assaults into one class and treats another target variable as usual. Random Forest (RF), Bagging Classifier (BC), and Extra Tree Ensemble (ETE) using Logistics Regression (LR) as a meta estimator. Chi2-square (Chi2) and Recursive Feature Elimination (RFE) are also used for feature selection. The experimental findings suggest that the proposed technique is ideal for developing a classification model with high accuracy and outperforms standard machine learning classification methods. The Chi 2 attribute selection strategy with modest characteristics has the greatest accuracy of 99.84 %. A careful comparison with prior research shows that our technique produces state-of-the-art performance with reduced computing costs.

Enhancing Network Intrusion Detection Using Effective Stacking of Ensemble Classifiers With Multi-Pronged Feature Selection Technique

The Internet of Things (IoT) has transformed our interaction with technology and introduced security challenges. The growing number of IoT attacks poses a significant threat to organizations and individuals. This paper proposes an approach for detecting attacks on IoT networks using ensemble feature selection and deep learning models. Ensemble feature selection combines filter techniques such as variance threshold, mutual information, Chi-square, ANOVA, and L1-based methods. By leveraging the strengths of each technique, the ensemble is formed by the union of selected features. However, this union operation may overlook redundancy and irrelevance, potentially leading to a larger feature set. To address this, a wrapper algorithm called Recursive Feature Elimination (RFE) is applied to refine the feature selection. The impact of the selected feature set on the performance of Deep Learning (DL) models (CNN, RNN, GRU, and LSTM) is evaluated using the IoT-Botnet 2020 dataset, considering detection accuracy, precision, recall, F1-measure, and False Positive Rate (FPR). All DL models achieved the highest detection accuracy, precision, recall, and F1 measure values, ranging from 97.05% to 97.87%, 96.99% to 97.95%, 99.80% to 99.95%, and 98.45% to 98.87%, respectively.

Approach for Detecting Attacks on IoT Networks Based on Ensemble Feature Selection and Deep Learning Models.

Enhancing Detection of R2L Attacks by Multistage Clustering Based Outlier Detection

Abstract Intrusion detection systems built on artificial intelligence (AI) are presented as latent mechanisms for actively detecting fresh attacks over a complex network. The authors used a qualitative method for analysing and evaluating the performance of network intrusion detection system (NIDS) in a systematic way. However, their approach has limitations as it only identifies gaps by analysing and summarising data comparisons without considering quantitative measurements of NIDS's performance. The authors provide a detailed discussion of various deep learning (DL) methods and explain data intrusion networks based on an infrastructure of networks and attack types. The authors’ main contribution is a systematic review that utilises meta‐analysis to provide an in‐depth analysis of DL and traditional machine learning (ML) in notable recent works. The authors assess validation methodologies and clarify recent trends related to dataset intrusion, detected attacks, and classification tasks to improve traditional ML and DL in NIDS‐based publications. Finally, challenges and future developments are discussed to pose new risks and complexities for network security. 

Meta‐analysis and systematic review for anomaly network intrusion detection systems: Detection methods, dataset, validation methodology, and challenges

Intrusion detection system is responsible for monitoring the systems and detect attacks, whether on (host or on a network) and identifying attacks that could come to the system and cause damage to them, that’s mean an IDS prevents unauthorized access to systems by giving an alert to the administrator before causing any serious harm. As a reasonable supplement of the firewall, intrusion detection technology can assist systems to deal with offensive, the Intrusions Detection Systems (IDSs) suffers from high false positive which leads to highly bad accuracy rate. So this work is suggested to implement (IDS) by using a Recursive Feature Elimination to select features and use Deep Neural Network (DNN) and Recurrent Neural Network (RNN) for classification, the suggested model gives good results with high accuracy rate reaching 94%, DNN was used in the binary classification to classify either attack or Normal, while RNN was used in the classifications for the five classes (Normal, Dos, Probe, R2L, U2R). The system was implemented by using (NSL-KDD) dataset, which was very efficient for offline analyses systems for IDS.

/pdf/intrusion-detection-system-for-nsl-kdd-dataset-based-on-deep-292mj5h0un.pdf

Intrusion Detection System for NSL-KDD Dataset Based on Deep Learning and Recursive Feature Elimination

Intrusion detection system is responsible for monitoring the systems and detect attacks, whether on (host or on a network) and identifying attacks that could come to the system and cause damage to them, that’s mean an IDS prevents unauthorized access to systems by giving an alert to the administrator before causing any serious harm As a reasonable supplement of the firewall, intrusion detection technology can assist systems to deal with offensive, the Intrusions Detection Systems (IDSs) suffers from high false positive which leads to highly bad accuracy rate So, this work is suggested to implement (IDS) by using a Principal component analysis to select features and Long short-term memory for classification, the suggested model gives good results with accuracy rate reaching 81% was used in the classifications for the five classes (Normal, Dos, Probe, R2L, U2R) The system was implemented by using (NSL-KDD) dataset, which was very efficient for offline analyses systems for IDS

Improving Intrusion Detection System Based On Long Short-Term Memory and Principal Component Analysis

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Intrusion Detection System for NSL-KDD Dataset Based on Deep Learning and Recursive Feature Elimination

Improving Intrusion Detection System Based On Long Short-Term Memory and Principal Component Analysis

Dark Web Access, Hidden Services and Security Challenges