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Online and Adaptive Signature Learning for Intrusion Detection

This thesis presents the case of dynamically and adaptively learning signatures for network intrusion detection using genetic based machine;

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Intrusion Detection

, Intrusion Detection covers: practical considerations for selecting and implementing intrusion detection systems; methods of handling the results;

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State Of The Art In Intrusion Prevention

by examining traffic analysis and management for intrusion detection systems. It explores honeypots, honeynets, network traffic analysis, and;

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The State of the Art in Intrusion Prevention and Detection

by examining traffic analysis and management for intrusion detection systems. It explores honeypots, honeynets, network traffic analysis, and;

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The Practical Intrusion Detection Handbook

only intrusion detection book to present practical advice for the entire lifecycle: choosing products, planning, deployment, operations, and;

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Network Anomaly Detection

security, credit card and insurance fraud detection, and military surveillance for enemy activities. Network Anomaly Detection: A Machine Learning;

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Dynamic Multi Layer Signature based IDS System using Mobile Agents

Signature-based detection is the most widely used technique for Intrusion Detection Systems (IDS). One of the major challenges of signature;

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Spatio Temporal Anomaly Detector

Network anomalies occur in all sorts of networks, And as internet is growing more and more the intrusion detection mechanism are becoming;

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Intrusion Detection with Artificial Neural Networks

of this book is to design, implement and evaluate an anomaly based network intrusion detection system. The System learns about the normal users;

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An intrusion detection system using machine learning algorithm

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Metrics for Intrusion Detection in Wireless Sensor Networks

attacks. In this book a novel approach for intrusion detection in wireless sensor networks is developed. Common performance and network metrics;

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Intrusion Detection Networks

distributed IDN architecture design Considers trust management, intrusion detection decision making, resource management, and collaborator management;

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Data Analytics and Decision Support for Cybersecurity

chapters focus on diverse and critical Cybersecurity problems, such as Intrusion Detection, Insider Threats, Insider Threats, Collusion Detection;

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Recent Advances in Intrusion Detection

, anomaly- and specification-based detection, and network intrusion detection. ;

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Network Intrusion Detection

Written to be both a training aid and a technical reference for intrusion detection analysts, Northcutt's book contains unparalleled;

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Automatic Defense Against Zero-day Polymorphic Worms in Communication Networks

intrusion detection systems (IDS). And, because zero-day worms require only seconds to launch flooding attacks on your servers, using traditional;

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Automatic Defense Against Zero-day Polymorphic Worms in Communication Networks

intrusion detection systems (IDS). And, because zero-day worms require only seconds to launch flooding attacks on your servers, using traditional;

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Big Data Analytics with Applications in Insider Threat Detection

Today's malware mutates randomly to avoid detection, but reactively adaptive malware is more intelligent, learning and adapting to new;

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Implementing Intrusion Detection Systems

Configuring an Intrusion Detection System (IDS) is very challenging, and if improperly configured, an IDS is rendered ineffective. Packed;

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Viable Network Intrusion Detection

Network intrusion detection systems (NIDS) continuously monitor network traffic for malicious activity, raising alerts when detecting;

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Stateful intrusion detection in high-speed networks

The present work is aimed to develop and analyze a novel model for distributed stateful intrusion detection able to scale in order to keep;

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Practical Intrusion Analysis

, Cofounder, Stratum Security The Only Definitive Guide to New State-of-the-Art Techniques in Intrusion Detection and Prevention;

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Handbook of Research on Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance

the research necessity for real-time implications of precise fraud/defaulter detection, rare disease/reaction prediction, network intrusion;

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Intrusion Detection using Feature Selection and Hybridized

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Evaluation of Some Windows and Linux Intrusion Detection Tools

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Configure intrusion detection and alerts - Reduce security risks using Azure Security Service

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Machine Learning Methods for Behaviour Analysis and Anomaly Detection in Video

This thesis proposes machine learning methods for understanding scenes via behaviour analysis and online anomaly detection in video. The;

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