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[PDF] Top 20 Anomaly-based network intrusion detection using machine learning

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Anomaly-based network intrusion detection using machine learning

Anomaly-based network intrusion detection using machine learning

... same network, one probe could train a new neural network model and propagate it to the others to continuously adapt to the evolutions of the ...global network security plan, or the topology of the ... Voir le document complet

123

ONTIC: D5.4: Use Case #1 Network Intrusion Detection

ONTIC: D5.4: Use Case #1 Network Intrusion Detection

... general anomaly detection system should therefore be able to detect a wide range of anomalies with diverse structures, using the least amount of previous knowledge and information, ideally ... Voir le document complet

71

Low-Rate False Alarm Anomaly-Based Intrusion Detection System with One-Class SVM

Low-Rate False Alarm Anomaly-Based Intrusion Detection System with One-Class SVM

... "A machine learning method for anomaly-based intrusion detection ...rate based on one-class SVM ...core network design and management, and monitoring and ... Voir le document complet

100

Energy performance based anomaly detection in non-residential buildings using symbolic aggregate approximation

Energy performance based anomaly detection in non-residential buildings using symbolic aggregate approximation

... of anomaly detection using analyses of the whole building energy ...in machine learning and data mining techniques have led to several methods that may be leveraged by building energy ... Voir le document complet

7

Cache-Based Side-Channel Intrusion Detection using Hardware Performance Counters

Cache-Based Side-Channel Intrusion Detection using Hardware Performance Counters

... run-time detection approach for cache-based side channel attacks ...constitutes machine learning models which take real- time data from hardware performance counters for detection ... Voir le document complet

3

Network intrusion detection system for drone fleet using both spectral analysis and robust controller / observer

Network intrusion detection system for drone fleet using both spectral analysis and robust controller / observer

... improve intrusion detection systems in the specific context of drone ...traffic. Based on a wavelet analysis, this traffic characterization process provides a preliminary level of knowledge about ... Voir le document complet

14

Machine learning-based EDoS attack detection technique using execution trace analysis

Machine learning-based EDoS attack detection technique using execution trace analysis

... a machine- learning algorithm that can automatically predict and detect other types of attacks using this expanded ...real-world network traffic was limited in this work and the evaluation of ... Voir le document complet

21

A new statistical approach to network anomaly detection

A new statistical approach to network anomaly detection

... an anomaly based network intrusion detection system, which detects anomalies using sta- tistical characterizations of the TCP ...achieved using high order Markovian ... Voir le document complet

8

Machine learning for IoT network monitoring

Machine learning for IoT network monitoring

... different machine learning based approaches for IoT network ...home network was built to generate network traffic ...deep learning algorithms, such as autoencoder, to ... Voir le document complet

4

Anomaly detection in mixed telemetry data using a sparse representation and dictionary learning

Anomaly detection in mixed telemetry data using a sparse representation and dictionary learning

... vector machine (7) , near­ est neighbour techniques (8-10) or neural networks ...contextual anomaly is shown in Fig. 1 (box #7). The detection of this kind of abnormal behaviour requires a ... Voir le document complet

12

Anomaly detection in mixed telemetry data using a sparse representation and dictionary learning

Anomaly detection in mixed telemetry data using a sparse representation and dictionary learning

... vector machine (7) , near­ est neighbour techniques (8-10) or neural networks ...contextual anomaly is shown in Fig. 1 (box #7). The detection of this kind of abnormal behaviour requires a ... Voir le document complet

11

Towards privacy preserving cooperative cloud based intrusion detection systems

Towards privacy preserving cooperative cloud based intrusion detection systems

... cloud-based Intrusion Detection System (IDS) to detect all attacks, because of limited and incomplete knowledge about attacks and their ...cloud- based IDSs can bring higher detection ... Voir le document complet

88

Random Partitioning Forest for Point-Wise and Collective Anomaly Detection - Application to Network Intrusion Detection

Random Partitioning Forest for Point-Wise and Collective Anomaly Detection - Application to Network Intrusion Detection

... deep learning and self-encoding based ...deep learning framework under the form of auto-encoder (AE) [20] and Variational Auto- Encoder (VAE) ...of anomaly detection, reconstruction ... Voir le document complet

17

Distance Measures for Anomaly Intrusion Detection

Distance Measures for Anomaly Intrusion Detection

... general, intrusion detection methods based on the transition information model temporal variation of the audit ...The intrusion detection methods using the frequency information, ... Voir le document complet

10

CP-based cloud workload annotation as a preprocessing for anomaly detection using deep neural networks

CP-based cloud workload annotation as a preprocessing for anomaly detection using deep neural networks

... supervised learning has been a subject of great ...unsupervised learning. To overcome this issue in the context of anomaly detection in a cloud workload, we propose a method that relies on ... Voir le document complet

13

Machine learning and extremes for anomaly detection

Machine learning and extremes for anomaly detection

... classical Anomaly Detection algorithms actions to be taken, especially in situations where human expertise is required to check each observation is ...a machine learning perspective, ... Voir le document complet

221

Joint Optimization of Monitor Location and Network Anomaly Detection

Joint Optimization of Monitor Location and Network Anomaly Detection

... and network anomaly detection ...generated network topologies, in order to investigate the complexity of the problem and to obtain a deeper understanding of the interpaly between the ... Voir le document complet

5

Cyber security risk analysis framework : network traffic anomaly detection

Cyber security risk analysis framework : network traffic anomaly detection

... The experiments were conducted utilizing various time series algorithms (Seasonal ETS, Seasonal ARIMA, TBATS, Double-Seasonal Holt-Winters, and Ensemble methods) and Lo[r] ... Voir le document complet

86

Heuristics for Joint Optimization of Monitor Location and Network Anomaly Detection

Heuristics for Joint Optimization of Monitor Location and Network Anomaly Detection

... paths using only 2 monitors and without generating redundant measurements, and then, it balances the load between monitors and links while covering the remaining ... Voir le document complet

6

Sequence Covering for Efficient Host-Based Intrusion Detection

Sequence Covering for Efficient Host-Based Intrusion Detection

... Host-based Intrusion Detection, System Calls, Semi-Supervised Learning, Zero-Day F 1 I NTRODUCTION I NTRUSION Detection Systems (IDS) are more and more heavily challenged by ... Voir le document complet

15

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