Strengthening Mobile Network Security Using Machine Learning

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.

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Interpretability Framework of Network Security Traffic Classification Based on Machine Learning

Lecture Notes in Computer Science - Artificial Intelligence and Security ◽

10.1007/978-3-030-78612-0_25 ◽

2021 ◽

pp. 305-320

Author(s):

Mingshu He ◽

Lei Jin ◽

Mei Song

Keyword(s):

Machine Learning ◽

Network Security ◽

Traffic Classification

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Machine Learning Techniques for Network Intrusion Detection

Dynamic and Advanced Data Mining for Progressing Technological Development ◽

10.4018/978-1-60566-908-3.ch012 ◽

2010 ◽

pp. 273-299 ◽

Cited By ~ 1

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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A Novel Resource Management Framework for Fog Computing by Using Machine Learning Algorithm

Advances in Computer and Electrical Engineering - Architecture and Security Issues in Fog Computing Applications ◽

10.4018/978-1-7998-0194-8.ch002 ◽

2020 ◽

pp. 42-52

Author(s):

Shanthi Thangam Manukumar ◽

Vijayalakshmi Muthuswamy

Keyword(s):

Machine Learning ◽

Resource Management ◽

Mobile Devices ◽

Service Providers ◽

Learning Algorithm ◽

Fog Computing ◽

Mobile Network ◽

Resource Provisioning ◽

Management Framework ◽

Iot Devices

With the development of edge devices and mobile devices, the authenticated fast access for the networks is necessary and important. To make the edge and mobile devices smart, fast, and for the better quality of service (QoS), fog computing is an efficient way. Fog computing is providing the way for resource provisioning, service providers, high response time, and the best solution for mobile network traffic. In this chapter, the proposed method is for handling the fog resource management using efficient offloading mechanism. Offloading is done based on machine learning prediction technology and also by using the KNN algorithm to identify the nearest fog nodes to offload. The proposed method minimizes the energy consumption, latency and improves the QoS for edge devices, IoT devices, and mobile devices.

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