Network Intrusion Detection System Research Topics

Network Intrusion Detection System research topic is now one of the trending topic to detect the malicious or unauthorized access in a computer network. Here in this research we propose Network Intrusion Detection system to overcome the existing issues:

1. Define Intrusion Detection System.

At the first stage we take a look on the definition of Intrusion Detection System (IDS), it is a part of software or hardware which examines network traffic for doubtful or harmful actions and alerts supervisors or if essential take action.

2. What is Intrusion Detection System?

Afterwards the definition of Intrusion Detection System next we see the deep explanations of Intrusion Detection System; it is a protective mechanism that maintains the system or network to point and notify with possible security issues or opposed activity.

3. Where Intrusion Detection System used?

Next to the deep explanations of Intrusion Detection System, we discuss where it is utilized. It is utilized in the fields like governmental networks, corporate networks and cloud computing environments and these all employs Intrusion Detection System to observe and consider network data for any suggestions of unwanted access or unauthorized actions. The possible attacks from hackers are identified and hindered with supporting.

4. Why Intrusion Detection System technology proposed? , previous technology issues

Intrusion Detection System alerts the system from malicious activities and we proposed this to overcome some issues on existing technologies. The Intrusion Detection System is proposed to enhance the safety. It identifies and responds to seek the malicious or destructive activities in a computer network. Some of the existing technology issue examples are high false-positive rates, failed to correctly classify and respond to new attack kinds and insufficient detection skills.

5. Algorithms / protocols

The Intrusion Detection System is proposed in this research and it overcomes some existing technology issues, here we provide some methods for Intrusion Detection System.

Some of the Network Intrusion Detection System methods are Genetic Algorithm, K Nearest Neighbor, Generalized Addictive Model and Deep Q Network.

The methods that utilized for the Intrusion Detection System are Multilayer Perceptron, Bayesian Optimization, Principal Component Analysis, Artificial Neural Networks and Isolation Forest.

6. Comparative study / Analysis

In comparative analysis we compared various methods to obtain the possible correct result. In this research for network intrusion detection system we compared the methods like

Network Intrusion Detection System

• Deep Q-Network (DQN) is used for dynamic resource allocation confirm effective and adaptive real-time cloud resource use while cutting down on unwanted and postponement.
• For Continuous routing optimization we utilize K-Nearest Neighbors (KNN) that decreases the delays by defining the shortest data transmission channels on the basis of closeness, improving network traffic effectively.
• To find network intrusion attacks and permits the system to constantly enhance the network security changing and modifying its detection strategies by employing GA-DQN.
• If applicable the intrusions are detected, and the Generalized Addictive Models (GAM) are incorporated to offer complete and helpful information which provide illegal understandings for quick reply and communication employing KNN – based optimum pathways.

Intrusion Detection System

• With the assistance of SDN switch and controller, we build a consistent IoT network by utilizing the Absolute Cohesion Network methods. Offer Scalability, significant data flow, connectivity and protection.
• For Intrusion Detection and preprocess the data to eliminate missing values, outliers, duplicates and noise by employing IoT-23 dataset.
• By utilizing Hybrid PCA, MLP, Isolation Forest and ANN to extract valuable features and train the IDS model. Ensure quick threat modifications and anomaly detection.
• To enhance the model performance by using Bayesian Optimization and for multiple routing data transfer multiple Dijkstra method is utilized, and warning and information are generated when privacy issues are created to assist with post-incident analysis and fast replies.

7. Simulation results / Parameters

Succeeding we compare the methods; next compare the parameters for obtaining the correct results.

Network Intrusion Detection System

The parameters that we compared for Network Intrusion Detection System are Energy consumption (j), Throughput (%), Detection rate (%) and Packet Delivery Ratio (%) with the No. of. users.

Intrusion Detection System

We also compare the parameters for Intrusion Detection Systems are Specificity, Accuracy, Detection rate, F1-score, Sensitivity and Precision.

8. Dataset LINKS / Important URL

Below we provide some important links that helps to clear up the doubts that we come across while do a research on the basis of intrusion Detection system are

Network Intrusion Detection Links

• https://link.springer.com/chapter/10.1007/978-3-031-36574-4_21
• https://www.sciencedirect.com/science/article/pii/S0167404823004121
• https://www.sciencedirect.com/science/article/pii/S0957417422016840
• https://www.mdpi.com/2224-2708/11/3/54

Intrusion Detection System Links

• https://link.springer.com/article/10.1007/s44196-023-00205-w
• https://link.springer.com/article/10.1007/s10922-022-09697-x
• https://www.sciencedirect.com/science/article/pii/S2090447923001004
• https://www.mdpi.com/2071-1050/15/11/9001

9. Intrusion Detection System Applications

Some of the applications for intrusion detection systems are described below

• An Intrusion Detection System for IoT devices is employed in smart homes whether it displays linked apparatus, seeking any malicious entry or altering and informs the owner.
• It also incorporates industrial IoT to find network traffic abnormalities and inform system supervisors of possible network thefts or unwanted access tries.

10. Topology for Intrusion Detection System

Here we see the topology for intrusion detection system, for IoT devices we utilize a star topology, with main IDS server linking to another device through individual links. The other option is a ring topology that produces a closed loop network by connecting the IoT devices circularly one after it.

11. Environment in Intrusion Detection System

Next we see the environment in Intrusion Detection System. The network traffic is watched by network devices and security sensors and identifies possible protection breaks that create up the environment for intrusion detection system.

12. Simulation tools

Our proposed system follows the following software requirements for this research. We implement the research by utilizing the language like C++ or Python to obtain the outcome and is developed by the tool namely NS3. The operating system utilized for Intrusion Detection System was Ubuntu 16.04 LTS.

13. Results

Intrusion Detection System is utilized to detect the unauthorized or any criminal activities in the computer network. We proposed this to our research by overcoming the existing technology issues. This research can be implemented by using the language C++ or Python and is operated by the system OS Ubuntu 16.04 LTS.

Network Intrusion Detection System Research Ideas:

Here we give some research topics on the basis of Intrusion Detection System and these topics are useful when we clarify some doubts related to Intrusion Detection System.

1. DoS Attack Detection with NIDS in Docker Environment
2. Hyper Parameter Optimized NIDS via Machine Learning in IoT Ecosystem
3. Empirical Evaluation of Autoencoder Models for Anomaly Detection in Packet-based NIDS
4. An NIDS for Known and Zero-Day Anomalies
5. Machine Learning on Public Intrusion Datasets: Academic Hype or Concrete Advances in NIDS?
6. Comparison of ML-based One-Stage and Two-Stage NIDS Models
7. NF-NIDS: Normalizing Flows for Network Intrusion Detection Systems
8. NIDS-CNNLSTM: Network Intrusion Detection Classification Model Based on Deep Learning
9. Network Intrusion Detection System (NIDS) Based on Pseudo-Siamese Stacked Autoencoders in Fog Computing
10. An Adaptive Flow-based NIDS for Smart Home Networks Against Malware Behavior Using XGBoost combined with Rough Set Theory
11. NIDS-VSB: Network Intrusion Detection System for VANET using Spark-Based Big Data Optimization and Transfer Learning
12. Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey
13. Performance Analysis of Blended NIDS Model for Network Intrusion Detection System in WSN
14. An Efficient Network Intrusion Detection System for Distributed Networks using Machine Learning Technique
15. Novel Online Network Intrusion Detection System for Industrial IoT Based on OI-SVDD and AS-ELM
16. Spark-based Distributed Intelligent Network Intrusion Detection System for Unified Dataset
17. An Enhanced AI-Based Network Intrusion Detection System Using Generative Adversarial Networks
18. A Network Intrusion Detection System for Building Automation and Control Systems
19. Contemplate and Investigate a Network based Intrusion Detection System
20. Attention-Based CNN-BiLSTM Deep Learning Approach for Network Intrusion Detection System in Software Defined Networks
21. A Smart Network Intrusion Detection System for Cyber Security of Industrial IoT
22. CNN-BiLSTM: A Hybrid Deep Learning Approach for Network Intrusion Detection System in Software-Defined Networking With Hybrid Feature Selection
23. A Novel Deep Learning based Model to Defend Network Intrusion Detection System against Adversarial Attacks
24. A Preliminary Study on the Application of Hybrid Machine Learning Techniques in Network Intrusion Detection Systems
25. Towards Generating Semi-Synthetic Datasets for Network Intrusion Detection System
26. ADCL: Toward an Adaptive Network Intrusion Detection System Using Collaborative Learning in IoT Networks
27. Improving the Robustness of DNNs-based Network Intrusion Detection Systems through Adversarial Training
28. CopulaGAN Boosted Random Forest based Network Intrusion Detection System for Hospital Network Infrastructure
29. A GAF and CNN based Wi-Fi Network Intrusion Detection System
30. Network Intrusion Detection System for Feature Extraction Based on Machine Learning Techniques
31. Data Balancing and CNN based Network Intrusion Detection System
32. Network Intrusion Detection System using Reinforcement learning
33. A Scalable Network Intrusion Detection System using Bi-LSTM and CNN
34. Performance Analysis of Deep-Learning Based Open Set Recognition Algorithms for Network Intrusion Detection Systems
35. AI based Techniques for Network-based Intrusion Detection System: A Review
36. F-NIDS — A Network Intrusion Detection System based on federated learning
37. An effective technique for detecting minority attacks in NIDS using deep learning and sampling approach
38. DI-NIDS: Domain invariant network intrusion detection system
39. An improved PIO feature selection algorithm for IoT network intrusion detection system based on ensemble learning
40. TAD: Transfer learning-based multi-adversarial detection of evasion attacks against network intrusion detection systems
41. An implementation of bi-phase network intrusion detection system by using real-time traffic analysis
42. A gradient-based approach for adversarial attack on deep learning-based network intrusion detection systems
43. Adv-Bot: Realistic adversarial botnet attacks against network intrusion detection systems
44. UInDeSI4.0: An efficient Unsupervised Intrusion Detection System for network traffic flow in Industry 4.0 ecosystem
45. GAN-AE: An unsupervised intrusion detection system for MQTT networks
46. Technology and System of Network Intrusion Detection Based on Big Data
47. Contemporary Machine Learning Approach for Anomaly Based Network Intrusion Detection System
48. Hybrid CatBoost Regression model based Intrusion Detection System in IoT-Enabled Networks
49. Feature Based Comparative Analysis of Traditional Intrusion Detection System and Software-Defined Networking Based Intrusion Detection System
50. Design and implementation of a computer network intrusion detection system based on convolutional neural network
51. Intrusion Detection System Using Machine Learning
52. An Efficient Network Intrusion Detection System for Distributed Networks using Machine Learning Technique
53. CopulaGAN Boosted Random Forest based Network Intrusion Detection System for Hospital Network Infrastructure
54. Attention-Based CNN-BiLSTM Deep Learning Approach for Network Intrusion Detection System in Software Defined Networks
55. A novel hybrid automatic intrusion detection system using machine learning technique for anomalous detection based on traffic prediction
56. Design And Implementation of Laser Radar-Based Railway Foreign Object Intrusion Detection System
57. Hybrid CatBoost Regression model based Intrusion Detection System in IoT-Enabled Networks
58. Intrusion Detection System Using Ensemble Techinque
59. An Intrusion Detection System for MANET to Detect Gray Hole Attack using Fuzzy Logic System
60. Design and Implementation of Intrusion Detection System Based on Deep Learning
61. Feature Based Comparative Analysis of Traditional Intrusion Detection System and Software-Defined Networking Based Intrusion Detection System
62. Adversarial Attack of ML-based Intrusion Detection System on In-vehicle System using GAN
63. Contemporary Machine Learning Approach for Anomaly Based Network Intrusion Detection System
64. A Survey on Supervised Machine Learning in Intrusion Detection Systems for Internet of Things
65. Intrusion Detection System Architecture for Cyber-Physical System
66. An Intelligent Hybrid Intrusion Detection System for Internet of Things-based Applications
67. Development of Machine Learning Subsystem in Intrusion Detection System for Cyber Physical System
68. A New Anomaly-Based Intrusion Detection System for MIL-STD-1553
69. Enhancing IoT Intrusion Detection System Performance with the Diversity Measure as a Novel Drift Detection Method
70. Construction of a computer network fault analysis and intrusion detection system based on K-means clustering algorithm
71. Machine Learning Approach for Anomaly-Based Intrusion Detection Systems Using Isolation Forest Model and Support Vector Machine
72. Enhancing Intrusion Detection Systems Accuracy Using Machine Learning
73. A Review Paper on Designing Intelligent Intrusion Detection System Using Deep Learning
74. CHI2CV : Feature Selection using Chi-Square with Cross-Validation for Intrusion Detection System
75. A Novel Deep Learning based Model to Defend Network Intrusion Detection System against Adversarial Attacks
76. Towards Generating Semi-Synthetic Datasets for Network Intrusion Detection System
77. Ensemble based Intrusion Detection System for IoT Device
78. An investigation of the Intrusion detection system for the NSL-KDD dataset using machine-learning algorithms
79. Improving the Accuracy of Intrusion Detection System in the Detection of DoS using Naive Bayes with Lasso Feature Elimination and Comparing with Naive Bayes without Feature Elimination in Wireless Adhoc Network
80. Computer Networks Cyber Security Via an Intrusion Detection System
81. Explainable SCADA-Edge Network Intrusion Detection System: Tree-LIME Approach
82. Intrusion Detection System Based on Probabilistic Suffix Tree
83. Implementation of Intrusion Detection System Using Various Machine Learning Approaches with Ensemble learning
84. Optimizing LightGBM for Intrusion Detection Systems using GOA
85. Collaborative Intrusion Detection System for SDVN: A Fairness Federated Deep Learning Approach
86. Intrusion Detection System Using Incremental Learning Method
87. Evaluation of Intrusion Detection System for the Distributed Denial of Service Attack on Internet of Things in Fog Computing Environment
88. Network Intrusion Detection System using Reinforcement learning
89. Intrusion Detection Systems Based on Machine Learning Approaches: A Systematic Review
90. Design of Intrusion Detection System for Wireless ADHOC Network in the Detection of DOS Attack using Oneclass SVM with Wrapper Approach Feature Selection Comparing with Information Gain Algorithm
91. Two-Stage Intrusion Detection System in Intelligent Transportation Systems Using Rule Extraction Methods From Deep Neural Networks
92. An Enhanced AI-Based Network Intrusion Detection System Using Generative Adversarial Networks
93. An IoT Intrusion Detection System Based on TON IoT Network Dataset
94. A Scalable Network Intrusion Detection System using Bi-LSTM and CNN
95. A New Intrusion Detection System Based on Convolutional Neural Network
96. Conditional Generative Adversarial Network with Optimal Machine Learning Based Intrusion Detection System
97. Spark-based Distributed Intelligent Network Intrusion Detection System for Unified Dataset
98. Efficient Intrusion Detection System Using Convolutional Long Short Term Memory Network
99. An Optimized Intrusion Detection System for Cyber-Physical System Attack Using Long Short-Term Memory
100. Comparison of Machine Learning Algorithms Trained Under Differential Privacy for Intrusion Detection Systems