scholarly journals Convolution Neural Network-Based Higher Accurate Intrusion Identification System for the Network Security and Communication

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Zhiwei Gu ◽  
Shah Nazir ◽  
Cheng Hong ◽  
Sulaiman Khan
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Intrusion Detection ◽  
Communication Systems ◽  
Computer Network ◽  
Research Work ◽  
Convolution Neural Network ◽  
Smart Devices ◽  
Identification System ◽  
Network Building

With the development of communication systems, information securities remain one of the main concerns for the last few years. The smart devices are connected to communicate, process, compute, and monitor diverse real-time scenarios. Intruders are trying to attack the network and capture the organization’s important information for its own benefits. Intrusion detection is a way of identifying security violations and examining unwanted occurrences in a computer network. Building an accurate and effective identification system for intrusion detection or malicious activities can secure the existing system for smooth and secure end-to-end communication. In the proposed research work, a deep learning-based approach is followed for the accurate intrusion detection purposes to ensure the high security of the network. A convolution neural network based approach is followed for the feature classification and malicious data identification purposes. In the end, comparative results are generated after evaluating the performance of the proposed algorithm to other rival algorithms in the proposed field. These comparative algorithms were FGSM, JSMA, C&W, and ENM. After evaluating the performance of these algorithms and the proposed algorithm based on different threshold values ranging, Lp norms, and different parametric values for c, it was concluded that the proposed algorithm outperforms with small Lp values and high Kitsune scores. These results reflect that the proposed research is promising toward the identification of attack on data packets, and it also reflects the applicability of the proposed algorithms in the network security field.

scholarly journals Pioneer dataset and recognition of Handwritten Pashto characters using Convolution Neural Networks

2020 ◽  
pp. 002029402096482
Author(s):  
Sulaiman Khan ◽  
Abdul Hafeez ◽  
Hazrat Ali ◽  
Shah Nazir ◽  
Anwar Hussain
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Research Work ◽  
Recognition Rate ◽  
Recognition Task ◽  
Convolution Neural Network ◽  
Medium Size ◽  
Support Vector ◽  
Artificial Neural

This paper presents an efficient OCR system for the recognition of offline Pashto isolated characters. The lack of an appropriate dataset makes it challenging to match against a reference and perform recognition. This research work addresses this problem by developing a medium-size database that comprises 4488 samples of handwritten Pashto character; that can be further used for experimental purposes. In the proposed OCR system the recognition task is performed using convolution neural network. The performance analysis of the proposed OCR system is validated by comparing its results with artificial neural network and support vector machine based on zoning feature extraction technique. The results of the proposed experiments shows an accuracy of 56% for the support vector machine, 78% for artificial neural network, and 80.7% for the proposed OCR system. The high recognition rate shows that the OCR system based on convolution neural network performs best among the used techniques.

scholarly journals Recurrent Neural Network Model Based on a New Regularization Technique for Real-Time Intrusion Detection in SDN Environments

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Marwan Ali Albahar
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Intrusion Detection ◽  
Recurrent Neural Network ◽  
Network Performance ◽  
Integrated Approach ◽  
Regularization Technique ◽  
Model Based ◽  
Minimum Number ◽  
Sdr Model

Software-defined networking (SDN) is a promising approach to networking that provides an abstraction layer for the physical network. This technology has the potential to decrease the networking costs and complexity within huge data centers. Although SDN offers flexibility, it has design flaws with regard to network security. To support the ongoing use of SDN, these flaws must be fixed using an integrated approach to improve overall network security. Therefore, in this paper, we propose a recurrent neural network (RNN) model based on a new regularization technique (RNN-SDR). This technique supports intrusion detection within SDNs. The purpose of regularization is to generalize the machine learning model enough for it to be performed optimally. Experiments on the KDD Cup 1999, NSL-KDD, and UNSW-NB15 datasets achieved accuracies of 99.5%, 97.39%, and 99.9%, respectively. The proposed RNN-SDR employs a minimum number of features when compared with other models. In addition, the experiments also validated that the RNN-SDR model does not significantly affect network performance in comparison with other options. Based on the analysis of the results of our experiments, we conclude that the RNN-SDR model is a promising approach for intrusion detection in SDN environments.

scholarly journals Modulation Recognition of Communication Signal Based on Convolutional Neural Network

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2302
Author(s):  
Kaiyuan Jiang ◽  
Xvan Qin ◽  
Jiawei Zhang ◽  
Aili Wang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Communication Systems ◽  
Learning Algorithm ◽  
Recognition Task ◽  
Machine Learning Algorithms ◽  
Convolution Neural Network ◽  
Modulation Recognition ◽  
Signal Modulation ◽  
Communication Signal

In the noncooperation communication scenario, digital signal modulation recognition will help people to identify the communication targets and have better management over them. To solve problems such as high complexity, low accuracy and cumbersome manual extraction of features by traditional machine learning algorithms, a kind of communication signal modulation recognition model based on convolution neural network (CNN) is proposed. In this paper, a convolution neural network combines bidirectional long short-term memory (BiLSTM) with a symmetrical structure to successively extract the frequency domain features and timing features of signals and then assigns importance weights based on the attention mechanism to complete the recognition task. Seven typical digital modulation schemes including 2ASK, 4ASK, 4FSK, BPSK, QPSK, 8PSK and 64QAM are used in the simulation test, and the results show that, compared with the classical machine learning algorithm, the proposed algorithm has higher recognition accuracy at low SNR, which confirmed that the proposed modulation recognition method is effective in noncooperation communication systems.

scholarly journals Accurate and Efficient Differentiation Between Normal and Epileptic Seizure of Eyes Using 13 Layer Convolution Neural Network

2021 ◽  
Vol 38 (4) ◽  
pp. 1161-1169
Author(s):  
Veeramosu Priyanka Brahmaiah ◽  
Yarlagadda Padma Sai ◽  
Mahendra N. Giri Prasad
Keyword(s):  
Neural Network ◽  
Epileptic Seizure ◽  
Background Noise ◽  
Research Work ◽  
Convolution Neural Network ◽  
Normal Brain ◽  
Improved Outcomes ◽  
Dynamic Time ◽  
Dynamic Time Wrapping ◽  
Optimal Feature

Epileptic seizure is one which affects the normal brain activities of human being and considered to be a risky disease. The eye ball movement signals pattern plays a significant role in determining the epileptic seizure in precise manner. In addition to it, EOG signals has its influence in detecting epileptic seizure through assessment of eye ball movement signals precisely. Detecting Epilepsy using genetical based Convolutional Neural Network plays a major role in the previous research works. Conversely, the existence of background noise on eye ball signals may impact on the outcome failure. Noise aware Epileptic Seizure Detection using Thirteen Layer Convolution Neural Network (NESD-TLCNN) is adopted in this research to mitigate this issue and thereby ensuring the prediction rate more precisely. Furthermore, Hybrid Dynamic Time Wrapping based Hidden Markov Model (HDWT-HMM) is greatly utilized for primary background noise detection and removal by estimating the noise depending on distance metric. Once after the completion of noise estimation, perfect detection of epileptic seizure is accomplished using feature extraction. The peculiar features involved are saccade, fixation and blink features. Subsequently, Particle swarm optimization (PSO) technique is also involved in this research for optimal feature selection. Thirteen Layer Convolution Neural Network (TLCNN) is applied at last for learning and differentiation of epileptic seizure from the normal eyes. This research is being carried out in MATLAB platform which also reveals that the anticipated methodology produces improved outcomes when contrasted with the existing research work.

scholarly journals ANTHROPOMETRIC IDENTIFICATION SYSTEM USING CONVOLUTION NEURAL NETWORK BASED ON REGION PROPOSAL NETWORK

2021 ◽  
Vol 506 (1-2) ◽  
Author(s):  
Ho Nguyen Anh Tuan ◽  
Pham Dang Dieu ◽  
Nguyen Dao Xuan Hai ◽  
Nguyen Truong Thinh ◽  
Le Gia Vinh
Keyword(s):  
Neural Network ◽  
Data Retrieval ◽  
Medical Diagnostics ◽  
Convolution Neural Network ◽  
Identification System ◽  
Network System ◽  
Retrieval Time ◽  
Neural Network System

The operating of the anthropometric identification supported byConvolution Neural Network system is to locate precious anthropology spots and certaindistances between each feature area on a person's face. Identifies the anthropometricpoints from 2D captured pictures by 3 perspective views promised an implementation between medical diagnostics to solve the problem of data retrieval time and efficiency compared to other manual measures.

scholarly journals Survey paper on intrusion detection techniques

2013 ◽  
Vol 6 (2) ◽  
pp. 329-335
Author(s):  
Rachna Kulhare ◽  
Dr. Divakar Singh
Keyword(s):  
Network Security ◽  
Intrusion Detection ◽  
Detection Rate ◽  
Computer Network ◽  
Detection Method ◽  
Detection Techniques ◽  
Survey Paper ◽  
Secure Information ◽  
Important Field ◽  
Computer Network Management

Network security has been one of the most important problems in Computer Network Management and Intrusion is the most publicized threats to security. In recent years, intrusion detection has emerged as an important field for network security. IDSs obtain better results when each class ofattacks is treated as a separate problem and handled by specialized algorithms. Now in days various model and method are available for intrusion detection. In this paper, we present a study of intrusion detection. Detection method to improve the detection rate & helping the users to develop secure information systems.

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