机器学习视角下的网络异常检测

Anomal

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"《Network Anomaly Detection: A Machine Learning Perspective》是一本专注于网络异常检测的理论书籍，结合了机器学习的视角。这本书由Dhruba K. Bhattacharyya和Jugal Kumar Kalita共同撰写，出版于2013年4月。书中探讨了网络异常检测的相关技术和应用，并在ResearchGate上获得了广泛的关注和引用。作者还参与了与之相关的项目，如Application-layer DDoS攻击检测和大数据分析等。" 网络异常检测是网络安全领域的一个关键问题，旨在识别和预防网络中的不寻常行为，这些行为可能是潜在的攻击或故障。这本书从机器学习的角度出发，介绍了如何利用算法和模型来发现和理解这些异常模式。机器学习在异常检测中的应用主要基于其从大量数据中学习和推断的能力，它可以自动发现规律并识别出偏离正常行为的事件。书中的内容可能涵盖了以下几个方面： 1. 异常检测的基本概念：包括异常定义、异常检测的目的和挑战，以及它在网络安全性、运维监控和业务智能等方面的重要性。 2. 机器学习基础：简述监督学习、无监督学习和半监督学习等机器学习方法，以及它们在异常检测中的应用场景。 3. 特征工程：讨论如何选择和构建能够有效捕获异常行为的特征，这对于训练准确的模型至关重要。 4. 异常检测算法：介绍各种常用的异常检测算法，如统计方法（如Z-Score、LOF）、基于聚类的方法（如Isolation Forest）、深度学习方法（如Autoencoder）等。 5. 实验与评估：阐述如何设置实验来验证模型的有效性，包括数据集的选择、性能指标（如F1分数、AUC-ROC曲线）和模型调优。 6. 应用实例：可能包括真实世界中的网络攻击案例，如DDoS攻击、入侵检测和欺诈检测，以及如何利用机器学习技术来解决这些问题。 7. 未来趋势：讨论当前研究的挑战和未来可能的发展方向，比如随着大数据和物联网的发展，如何应对更复杂、更实时的异常检测需求。 8. 作者的贡献和项目：Dhruba K. Bhattacharyya和Jugal Kumar Kalita在该领域的研究工作，以及他们在Application-layer DDoS攻击检测和大数据分析方面的项目，可能会在书中有所体现。这本书对于网络安全专业人士、数据科学家以及对机器学习和网络防御感兴趣的读者来说，提供了丰富的理论知识和实践经验，有助于提升网络防护能力。通过深入学习和理解书中的内容，读者可以掌握如何利用机器学习技术有效地实施网络异常检测，从而保护网络环境的安全。

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List of Tables

2.1 Guided vs. Unguided Media: Comparison . . . . . . . . 19

2.2 Comparison of Guided Media . . . . . . . . . . . . . . . 21

2.3 Six Diﬀerent Types of Service . . . . . . . . . . . . . . . 24

2.4 Service Primitives . . . . . . . . . . . . . . . . . . . . . 24

2.5 Classiﬁcation of Interconnected Processors by Scale . . . 37

2.6 Taxonomy of Attacks . . . . . . . . . . . . . . . . . . . . 55

4.1 Anomaly Score Estimation: A General Comparison . . . 142

4.2 Attack Distribution in KDD Cup Datasets . . . . . . . . 144

4.3 KDD Cup Attack List . . . . . . . . . . . . . . . . . . . 144

4.4 Attack Distribution in NSL-KDD Datasets . . . . . . . 146

4.5 Description of Attacks Present in the TUIDS Datasets . 147

4.6 Attacks and Tools Used in TUIDS Datasets . . . . . . . 149

4.7 Basic Packet Features in TUIDS Dataset . . . . . . . . . 150

4.8 Content-Based Packet Features in TUIDS Dataset . . . 151

4.9 Time-Based Packet Features in TUIDS Dataset . . . . . 151

4.10 Connection-Based Packet Features in TUIDS Dataset . 152

4.11 Flow Features of TUIDS Intrusion Dataset . . . . . . . 155

4.12 TUIDS Intrusion Datasets . . . . . . . . . . . . . . . . . 156

4.13 TUIDS Portscan Dataset . . . . . . . . . . . . . . . . . 156

4.14 TUIDS Portscan Feature Dataset . . . . . . . . . . . . . 157

5.1 Comparison with Existing Survey Articles . . . . . . . . 167

5.2 Comparison of Feature Selection Methods . . . . . . . . 177

5.3 Some Feature Selection Functions in FSelector . . . . . 188

5.4 Some Functions Associated with orngFSS . . . . . . . . 190

5.5 Some Operator Classes Available in RapidMiner . . . . 190

6.1 Statistical Paramteric Anomaly Detection Methods . . . 199

6.2 Statistical Nonparamteric Anomaly Detection Methods . 200

6.3 Unsupervised Anomaly Detection Methods . . . . . . . 208

6.4 Anomaly Detection Using Probabilistic Learning . . . . 217

6.5 Soft Computing-Based Anomaly Detection Methods . . 222

xvii

Preface

With the rapid rise in the ubiquity and sophistication of Internet tech-

nology and the accompanying, increasing number of network attacks,

network intrusion detection has become an important research area.

During the past several years, many signiﬁcant network defense mech-

anisms have been developed to thwart network attacks. The term

anomaly-based network intrusion detection refers to the problem of

ﬁnding exceptional or nonconforming patterns in network traﬃc data

with reference to normal behavior. Finding such atypical patterns, usu-

ally referred to as anomalies, has extensive applications in areas such as

intrusion detection for cyber security, fraud detection in credit cards,

insurance or health care and military surveillance for enemy activities.

The book discusses networks and network anomalies. In particular,

the book focuses on characterizing such anomalies and detecting them

from a machine learning perspective. It includes discussion on the pos-

sible vulnerabilities a network faces at various layers due to weaknesses

in protocols or other reasons. It introduces the reader to various types

of layer-speciﬁc intrusions and modes of operation. This book presents

machine learning techniques to counter network intrusion under cate-

gories such as supervised learning, unsupervised learning, probabilistic

learning, soft computing and combination learners. A detailed pros

and cons analysis of these method is also included to provide a clear

understanding of the ability of each method. To describe attacks consis-

tently, it presents a taxonomy of attacks. The reader will learn about

anomalous or attack patterns and their characteristics, how one can

look for such patterns in captured network traﬃc to unearth potential

unauthorized attempts to damage a system or a network and to im-

prove performance during attempts to locate such patterns. Looking for

such patterns involves systems and programs that implement machine

learning algorithms. We describe such algorithms and present appli-

cations in the context of intrusion detection in networks. The reader

will learn the speciﬁcs of a large number of attacks, the available tools

that intruders use to attack networks and how network defenders can

use the knowledge of such tools to protect networks from attacks. The

xix

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机器学习视角下的网络异常检测

Network Anomaly Detection-Machine Learning perspective

rethinking graph anomaly detection: a self-supervised group discrimination p

few-shot network anomaly detection via cross-network meta-learning

给出10个深度学习课程设计的题目

enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).

anomaly detection综述

Graph Convolutional Adversarial Networks for Spatiotemporal Anomaly Detection

abnormally detection paper

anomaly detection at scale: the case for deep distributional time series mod

Anomaly Detection via Reverse Distillation from One-Class Embedding的学习曲线

panda: adapting pretrained features for anomaly detection and segmentation

multiresolution knowledge distillation for anomaly detection

Outlier detection有几种deep learning的算法？

towards total recall in industrial anomaly detection

Anomaly-Transformer模型实现

论文"Unsupervised Anomaly Detection for Surface Defects with Dual-Siamese Network"中的DFF是怎么实现的，用pytorch实现

one class SVM

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