0% Complete
English
صفحه اصلی
/
چهاردهمین کنفرانس بین المللی فناوری اطلاعات و دانش
A Comparative Evaluation of Machine Learning Models for Anomaly-Based IDS in IoT Networks
نویسندگان :
Seyed Amir Mousavi
1
Mostafa Sadeghi
2
Mohammad Sadeq Sirjani
3
1- دانشگاه فردوسی مشهد
2- دانشگاه آزاد اسلامی واحد نجف آباد
3- دانشگاه فردوسی مشهد
کلمات کلیدی :
Network Security،Intrusion Detection System،Artificial Intelligence،Machine Learning
چکیده :
With the increasing Internet use, network security has become essential due to the rise in cyber-attacks on network services. To detect these attacks, a robust Intrusion Detection System (IDS) is required. Traditional IDS face challenges like high false alert rates and slow real-time attack detection. Machine learning (ML) can improve this situation, providing a low False Alarm Rate and high detection rates. This research used five ML methods (Logistic Regression, Random Forest, k-Nearest Neighbors, Support Vector Machine, and XGBoost) to classify the UNSW-NB15 dataset. The goal is to evaluate the performance of various machine learning classifiers in detecting attacks for Internet of Things (IoT) network intrusion detection. The study highlighted the importance of further research to reduce false positives and negatives. To evaluate these classifiers, precision, accuracy, recall, and F1 score were used. The results show that XGBoost achieved the highest accuracy and recall. However, only some algorithms performed perfectly in all aspects, suggesting the need for diverse detection strategies. Future research should focus on developing comprehensive systems and ensemble approaches to minimize false alerts and missed detections.
لیست مقالات
لیست مقالات بایگانی شده
An efficient hybrid approach for performance-based alternative design evaluation in systems engineering
Abbas Chaman Para - Maryam Nooraei Abadeh - Sondos Bahadori
Heart Sound Classification based on Group-based Sparse Features of PCG Signal
Zahra Hossein-Nejad - Mehdi Nasri
Presentation of a New Decoder Based on Quantum Cellular Automata Technology Along with an Analysis of Energy Consumption
- - -
Customer Churn Prediction Using Data Mining Techniques for an Iranian Payment Application
Olya Rezaeian - Dr ُSeyedhamidreza Shahabi Haghighi - Dr Jamal Shahrabi
هوشمندسازی پایش کیفیت رنگزنی داخلی گرین تایر و تحلیل داده برای بهینه سازی عمر بلادر، مصرف رنگ و ریشه یابی عیوب پخت
سامان ثنایی - رضا رحیمی
یک روش کارآمد جهت تشخیص آنلاین حملات DRDoS به سرویس های مبتنی بر UDP درمعماری SDN با استفاده از الگوریتم های یادگیری ماشین
میترا اکبری کهنه شهری - دکتر رضا محمدی - دکتر محمد نصیری میترا اکبری کهنه شهری - رضا محمدی - محمد نصیری -
Dealing with Black-hole Attacks in Inter-vehicle Networks Using the Packet Delivery Rate Algorithm
Marzieh Sedighi - Mehdi Hamidkhani - Mostafa Sadeghi
A Hybrid Crow Search and Penguin Optimization Algorithm (CPMM) for Efficient Cloud Workflow Scheduling
Reza Akraminejad - Farhad Kazemipour - Mozhdeh Koreh Davoodi
A U-Net architecture with graph attention networks to accurately define tooth boundaries
Ehsan Akefi - Hassan Khotanlou
A Multi Objective & Trust-Based Workflow Scheduling Method In Cloud Computing Based On The MVO Algorithm
Fatemeh Ebadifard
بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 42.5.2