首页|Intrusion Detection System Using Hybrid Convolutional Neural Network

Intrusion Detection System Using Hybrid Convolutional Neural Network

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  • NETL
  • NSTL
  • Springer Nature
The exponential development of the internet and electronic communications has led to a tremendous rise in the quantity of data exchanged. Intruders are always developing novel techniques to acquire or manipulate such data since they are so valuable. A rising number of such attacks pose a threat to the safety of the networks and is a formidable obstacle to intrusion detection. An IDS analyses network traffic to spot potential threats. There have been numerous studies and innovative IDS developed, however, IDS still requires improvements to have excellent detection performance with minimizing false rates. The challenges of classic IDS systems are as follows: Feature selection is crucial, and it is necessary for enhancing performance. Unbalanced data can limit efforts to improve accuracy. However, most IDS have trouble identifying zero-day attacks. In this paper, we take a hybrid Convolution Neural Network and Deep Watershed Auto-encoder (CNN-DWA) approach to address the above-mentioned challenges. The suggested network is trained and evaluated using the KDD CUP 1999 dataset. The benefits of the suggested model are demonstrated by comparing the results obtained using the CNN-DWA approach with the Convolution Neural Network (CNN) method. The results of the experiments indicate that the suggested model has a higher accuracy (98.05%) than CNN (94.54%).

InternetIntrusion DetectionFeature SelectionAttackDeep LearningTrue PositiveAccuracy

Amani K. Samha、Nidhi Malik、Deepak Sharma、Kavitha S、Papiya Dutta

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Management Information System Department, College of Business Administration, King Saud University, Riyadh, Saudi Arabia

Department of Computer Science & Engineering, The NorthCap University, Gurugram 122017, Haryana, India

Department of Computer Science, Aryabhatta College, University of Delhi, New Delhi 110021, India

MCA Department, Nitte Meenakshi Institute of Technology, Bangalore, Karnataka, India

Department of Electronics & Communication Engineering, Bharat Institute of Engineering and Technology, Hyderabad, Telangana, India 501510

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2024

10.1007/s11036-023-02223-6

Mobile networks & applications

Mobile networks & applications

SCI
ISSN：1383-469X
年,卷(期)：2024.29(6)
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