Elevating IDS Capabilities: The Convergence of SVM, Deep Learning, and RFECV in Network Security DOI
G. Aditya Kumar,

Aditi Katiyar,

Kathiravan Srinivasan

и другие.

Опубликована: Фев. 22, 2024

By presenting an improved Intrusion Detection System (IDS) that combines deep learning with support vector machines (SVM), this research increases network security. The main goal is to increase the accuracy of SVM detection by using a methodical feature selection and optimization technique tailored complexity intrusions. 35 out 42 features were chosen for RFECV, algorithmic in machine learning. To ensure preserved are those contribute most model's predictive capacity redundant deleted, techniques such as RFECV priority ranking ExtraTreesClassifier take performance into account during process. improve classifier performance, strategies hyperparameter tuning used, focusing on important data cutting down redundancies. several kernel functions, including linear, polynomial, RBF, sigmoid, compared study. Linear model combined was shown perform best. Our outperforms current IDS frameworks, demonstrated comparative analysis, confirming efficacy integrating SVMs real-time threat detection. KDD Cup 99 dataset, which has been widely used benchmark assessing different models, work. It offers consistent, varied, large dataset so researchers may evaluate contrast their methods. Researchers can experiment reduction enhance because dataset's broad set features.

Язык: Английский

Enhanced Hybrid Deep Learning Models-Based Anomaly Detection Method for Two-Stage Binary and Multi-Class Classification of Attacks in Intrusion Detection Systems DOI Creative Commons
Hesham M. Kamal, Maggie Mashaly

Algorithms, Год журнала: 2025, Номер 18(2), С. 69 - 69

Опубликована: Янв. 28, 2025

As security threats become more complex, the need for effective intrusion detection systems (IDSs) has grown. Traditional machine learning methods are limited by extensive feature engineering and data preprocessing. To overcome this, we propose two enhanced hybrid deep models, an autoencoder–convolutional neural network (Autoencoder–CNN) a transformer–deep (Transformer–DNN). The Autoencoder reshapes traffic data, addressing class imbalance, CNN performs precise classification. transformer component extracts contextual features, which DNN uses accurate Our approach utilizes adaptive synthetic sampling–synthetic minority oversampling technique (ADASYN-SMOTE) binary classification SMOTE multi-class classification, along with edited nearest neighbors (ENN) further imbalance handling. models were designed to minimize false positives negatives, improve real-time detection, identify zero-day attacks. Evaluations based on CICIDS2017 dataset showed 99.90% accuracy Autoencoder–CNN 99.92% Transformer–DNN in 99.95% 99.96% respectively. On NF-BoT-IoT-v2 dataset, achieved 99.98% 97.95% while reached 97.90%, These results demonstrate superior performance of proposed compared traditional handling diverse

Язык: Английский

Процитировано

3

Enhancing Multi-Class Attack Detection in Graph Neural Network through Feature Rearrangement DOI Open Access
H.-S. Le, Minho Park

Electronics, Год журнала: 2024, Номер 13(12), С. 2404 - 2404

Опубликована: Июнь 19, 2024

As network sizes grow, attack schemes not only become more varied but also increase in complexity. This diversification leads to a proliferation of variants, complicating the identification and differentiation potential threats. Enhancing system security necessitates implementation multi-class intrusion detection systems. approach enables categorization incoming traffic into distinct types illustrates specific encountered within Internet. Numerous studies have leveraged deep learning (DL) for Network-based Intrusion Detection Systems (NIDS), aiming improve detection. Among these DL algorithms, Graph Neural Networks (GNN) stand out their ability efficiently process unstructured data, especially traffic, making them particularly suitable NIDS applications. Although usually monitors outgoing flows network, represented as edge features graph format, traditional GNN consider node features, overlooking features. oversight can result losing important flow data diminish system’s detect attacks effectively. To address this limitation, our research makes several key contributions: (1) Emphasize significance enhancing detection, (2) Utilize port information, which is essential identifying often overlooked during training, (3) Reorganize embedded graph. By doing this, represent close actual showing endpoint information such IP addresses ports; contains related Duration, Number Packet/s, Length…; (4) Compared methods, experiments demonstrate significant performance improvements on both CIC-IDS-2017 (98.32%) UNSW-NB15 (96.71%) datasets.

Язык: Английский

Процитировано

5

Comparative Analysis of Advanced Machine Learning Models for Exploit Detection in Intrusion Detection Systems DOI

Aadil Khan,

Deepali Gupta, Sheifali Gupta

и другие.

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Апрель 10, 2025

Abstract The integrity of network infrastructure against malicious exploit attacks relies mostly on Intrusion Detection Systems (IDS). These techniques are very essential for identifying and lowering threats before they start to cause significant damage. This manuscript evaluates three advanced Machine Learning (ML) models CatBoost, XGBoost, Long Short-Term Memory (LSTM) a real-world traffic dataset determine their suitability IDS applications. Every model is evaluated using key metrics: accuracy, precision, recall, F1-score, error measures including Root Mean Squared Error (RMSE) (MSE). Based the results, Catboost exceeds other with 98.55% accuracy lowest rates. Given CatBoost's remarkable performance, it fitting real-time systems where reducing false positives negatives extremely crucial. XGBoost provides balanced computationally affordable solution even if significantly less accurate; ideal scenarios requiring fast responses limited resources. Strong in sequential pattern recognition, LSTM has higher rate positives, suggesting that further tuning needed improve its overall reliability surroundings. possibility enhancing performance gradient boosting such as CatBoost cybersecurity underlined this study.

Язык: Английский

Процитировано

0

Classification of Multiclass DDOS Attack Detection Using Bayesian Weighted Random Forest Optimized With Gazelle Optimization Algorithm DOI

R. Barona,

E. Babu Raj

Transactions on Emerging Telecommunications Technologies, Год журнала: 2025, Номер 36(4)

Опубликована: Апрель 1, 2025

ABSTRACT The increase in Distributed Denial of Service (DDoS) attacks poses a considerable threat to the security and stability current network, especially Internet Things (IoT) cloud environments. Traditional detection methods often struggle with inability achieve balance between accuracy computational efficiency. In this manuscript, Classification Multiclass DDOS Attack Detection using Bayesian Weighted Random Forest Optimized Gazelle Optimization Algorithm (DDOS‐AD‐BWRF‐GOA) is proposed. First, raw data gathered from CICDDoS2019 dataset. Then, input are preprocessed utilizing Adaptive Bitonic Filtering for normalizing values. fed Improved Feed Forward Long Short‐Term Memory technique selecting features that model's execution time. selected supplied (BWRF), which classifies multiclass attack. general, does not adopt any optimization define optimal parameters guarantee exact identification. Hence, GOA proposed optimize classifier. method implemented MATLAB. performance metrics, such as Accuracy, Precision, Recall, F 1‐score, Specificity, Error rate, Computational time evaluated. attains 15.34%, 24.1%, 18.9% higher 12.4%, 18.24%, 22.6% precision when analyzed existing techniques: Hybrid deep learning classification (HDL‐DDOS‐DC), Edge‐HetIoT Defense against DDoS attack techniques (EHD‐DDOS‐LT), Digital twin‐enabled intelligent autonomous core networks (DTI‐DDOS‐ACN), respectively.

Язык: Английский

Процитировано

0

Transformative role of private AI in secondary education DOI

Raghu Kumar Lingamallu,

Karthik Jangam,

Nikith Sai Reddy Banda

и другие.

AIP conference proceedings, Год журнала: 2025, Номер 3157, С. 020004 - 020004

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

Block chain role in real estate DOI

Laxmikanth Mangalagiri,

Harikrishna Bommala,

Naveen Kumar Pattipati

и другие.

AIP conference proceedings, Год журнала: 2025, Номер 3157, С. 100003 - 100003

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

Boosting in high dimensional data classification DOI

Kuncharam Ramakrishna Reddy,

Vineet Sharma, R. Dhanasekaran

и другие.

AIP conference proceedings, Год журнала: 2025, Номер 3157, С. 080008 - 080008

Опубликована: Янв. 1, 2025

Процитировано

0

Machine learning algorithms for personalized QoS aware web service recommendation DOI

Nasra Fatima,

Harikrishna Bommala,

Barath Reddy Sadda

и другие.

AIP conference proceedings, Год журнала: 2025, Номер 3157, С. 080012 - 080012

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

Implementation of YOLO8 for real-time object recognition and tracking for visually impaired DOI

Ponguvala Haindavi,

Bhukya Madhu,

D. Ganesh

и другие.

AIP conference proceedings, Год журнала: 2025, Номер 3157, С. 070014 - 070014

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

Cloud task scheduling using context-aware task scheduling with machine learning (CATSM-ML) DOI

Logabiraman Govardhan,

Bhukya Madhu,

D. Raghava Raju

и другие.

AIP conference proceedings, Год журнала: 2025, Номер 3157, С. 080009 - 080009

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0