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What is Anomaly detection

Handbook of Research on Wireless Security
Anomaly detection is a type of intrusion detection in which historical normal behavior of the network is used. Any deviation of a behavior from the normal will raise an alarm.
Published in Chapter:
Cutting the Gordian Knot: Intrusion Detection Systems in Ad Hoc Networks
John Felix Charles Joseph (Nanyang Technological University, Singapore), Amitabha Das (Nanyang Technological University, Singapore), Boon-Chong Seet (Auckland Univerisity of Technology, New Zealand), and Bu-Sung Lee (Nanyang Technological University, Singapore)
Copyright: © 2008 |Pages: 16
DOI: 10.4018/978-1-59904-899-4.ch033
Abstract
Intrusion detection in ad hoc networks is a challenge because of the inherent characteristics of these networks, such as, the absence of centralized nodes, the lack of infrastructure, and so forth. Furthermore, in addition to application-based attacks, ad hoc networks are prone to attacks targeting routing protocols. Issues in intrusion detection in ad hoc networks are addressed by numerous research proposals in literature. In this chapter, we first enumerate the properties of ad hoc networks which hinder intrusion detection systems. After that, significant intrusion detection system (IDS) architectures and methodologies proposed in the literature are elucidated. Strengths and weaknesses of these works are studied and are explained. Finally, the future directions which will lead to the successful deployment of intrusion detection in ad hoc networks are discussed.
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More Results
A Study on Supervised Machine Learning Technique to Detect Anomalies in Networks
It is used to identify certain points, events, an observation which diverge from normal behavior.
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A Preliminary Framework to Fight Tax Evasion in the Home Renovation Market
Highlight, discovery, identification of an irregularity, a deviation from expectations arising from significant deviation (more or less) with a standard or a majority of cases a priori similar and potentially indicative of fraud, error or fault. Synonym for outlier detection.
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Big Data Analytics for Intrusion Detection: An Overview
Finds deviations from normal. Rare events or observations raise suspicions when differ significantly from most of the data.
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Big Data in Real Time to Detect Anomalies
Generally understood to be the identification of rare items, events or observations which deviate significantly from the majority of the data and do not conform to a well defined notion of normal behaviour.
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Artificial Immune Systems for Anomaly Detection
a process to determine if a variable is within the normal region of operation.
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A Dynamic Subspace Anomaly Detection Method Using Generic Algorithm for Streaming Network Data
The process for detecting those data that are considered as inconsistent, abnormal when compared with the majority of the data in the databases or population.
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Malware Detection in Network Flows With Self-Supervised Deep Learning
Anomaly detection refers to finding unusual events or outliers within a given dataset. Many techniques for detecting anomalies for univariate statistical data exist in statistics, and multivariate techniques exist as well but are more complex and less scalable with standard statistical methods, and are also potentially less accurate due to a traditional model being limited to homogenized methods across all variables and generally being limited to linear relationships between independent variables. Anomaly detection in this paper utilizes a multilayer perceptron neural network that is potentially more sensitive to complex features of the multivariate data including both linear and nonlinear attributes.
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Digital Forensics and Data Mining
In data mining, anomaly detection is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data.
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Intelligent Log Analysis Using Machine and Deep Learning
Analyzing data to detect unusual or abnormal behavior.
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Anomaly Detection in Hyperspectral Imagery: An Overview
A blind detection of objects from hyperspectral images that deviate from the rest of the image.
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Hybrid Intrusion Detection System for Smart Home Applications
This refers to the act of identifying rare events in network operation that could be the result of network intrusions. Anomalies raise suspicion because they differ significantly from the data observed during normal operation.
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Evolving the Security Paradigm for Industrial IoT Environments
Analysis techniques which try to identify deviations regarding an established normal behavior or operational pattern of a system.
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Anomaly Detection in IoT Frameworks Using Machine Learning
Anomaly detection (additionally known as outlier discovery) is the identification of uncommon things, occasions, or perceptions, which raise doubts by varying fundamentally from most of the data.
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Social Network Analysis: Basic Concepts, Tools, and Applications
Is the detection of the noise, misbehavior, incoherent data point in the data that deviate markedly from the rest of the data.
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The Role of Data Mining in Intrusion Detection Technology
It detects activity that deviates from normal activity. Profile-based anomaly detection depends on the statistical definition of what is normal and can be prone to a large number of false positives.
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An Auto-Reclosing-Based Intrusion Detection Technique for Enterprise Networks
An approach which considers any unusual pattern as an anomaly and therefore an attack. It helps in detecting both known and unknown attacks.
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Outlier Detection in Big Data
The task of finding anomalies in a business’s data. Some authors use “anomaly detection” to specifically refer to network intrusion detection.
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A Hybrid Intelligent Risk Identification Model for Configuration Management in Aerospace Systems
A Data Mining technique in charge of the identification of events, items and observations not expected as a pattern.
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