Impact of Machine Learning in Various Network Security Applications

2019 ◽  
Author(s):  
Jayashree Banerjee ◽  
Sumana Maiti ◽  
Sumalya Chakraborty ◽  
Surajit Dutta ◽  
Arpita Chakraborty ◽  
...  
Keyword(s):  
Machine Learning ◽  
Network Security ◽  
Security Applications

scholarly journals Behavioral Biometric Data Analysis for Gender Classification Using Feature Fusion and Machine Learning

2021 ◽  
Vol 8 ◽  
Author(s):  
Shivanand S. Gornale ◽  
Sathish Kumar ◽  
Abhijit Patil ◽  
Prakash S. Hiremath
Keyword(s):  
Machine Learning ◽  
Feature Fusion ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Gender Classification ◽  
Biometric Data ◽  
Security Applications ◽  
Biometric Security ◽  
Learning Techniques ◽  
Handwritten Signature

Biometric security applications have been employed for providing a higher security in several access control systems during the past few years. The handwritten signature is the most widely accepted behavioral biometric trait for authenticating the documents like letters, contracts, wills, MOU’s, etc. for validation in day to day life. In this paper, a novel algorithm to detect gender of individuals based on the image of their handwritten signatures is proposed. The proposed work is based on the fusion of textural and statistical features extracted from the signature images. The LBP and HOG features represent the texture. The writer’s gender classification is carried out using machine learning techniques. The proposed technique is evaluated on own dataset of 4,790 signatures and realized an encouraging accuracy of 96.17, 98.72 and 100% for k-NN, decision tree and Support Vector Machine classifiers, respectively. The proposed method is expected to be useful in design of efficient computer vision tools for authentication and forensic investigation of documents with handwritten signatures.

Network Security Policy Automation

2022 ◽  
pp. 83-112
Author(s):  
Myo Zarny ◽  
Meng Xu ◽  
Yi Sun
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Network Security ◽  
Security Policy ◽  
Learning Technologies ◽  
Use Cases ◽  
Hybrid Cloud ◽  
Cloud Environments ◽  
Enterprise Security

Network security policy automation enables enterprise security teams to keep pace with increasingly dynamic changes in on-premises and public/hybrid cloud environments. This chapter discusses the most common use cases for policy automation in the enterprise, and new automation methodologies to address them by taking the reader step-by-step through sample use cases. It also looks into how emerging automation solutions are using big data, artificial intelligence, and machine learning technologies to further accelerate network security policy automation and improve application and network security in the process.

Machine Learning Techniques for Network Intrusion Detection

2010 ◽  
pp. 273-299 ◽  
Author(s):  
Tich Phuoc Tran ◽  
Pohsiang Tsai ◽  
Tony Jan ◽  
Xiangjian He
Keyword(s):  
Machine Learning ◽  
Network Security ◽  
Intrusion Detection ◽  
Computer Systems ◽  
Computational Cost ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Complex Nature ◽  
Processing Power ◽  
Linear Relationships

Most of the currently available network security techniques are not able to cope with the dynamic and increasingly complex nature of cyber attacks on distributed computer systems. Therefore, an automated and adaptive defensive tool is imperative for computer networks. Alongside the existing prevention techniques such as encryption and firewalls, Intrusion Detection System (IDS) has established itself as an emerging technology that is able to detect unauthorized access and abuse of computer systems by both internal users and external offenders. Most of the novel approaches in this field have adopted Artificial Intelligence (AI) technologies such as Artificial Neural Networks (ANN) to improve performance as well as robustness of IDS. The true power and advantages of ANN lie in its ability to represent both linear and non-linear relationships and learn these relationships directly from the data being modeled. However, ANN is computationally expensive due to its demanding processing power and this leads to overfitting problem, i.e. the network is unable to extrapolate accurately once the input is outside of the training data range. These limitations challenge IDS with low detection rate, high false alarm rate and excessive computation cost. This chapter proposes a novel Machine Learning (ML) algorithm to alleviate those difficulties of existing AI techniques in the area of computer network security. The Intrusion Detection dataset provided by Knowledge Discovery and Data Mining (KDD-99) is used as a benchmark to compare our model with other existing techniques. Extensive empirical analysis suggests that the proposed method outperforms other state-of-the-art learning algorithms in terms of learning bias, generalization variance and computational cost. It is also reported to significantly improve the overall detection capability for difficult-to-detect novel attacks which are unseen or irregularly occur in the training phase.

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