Survey of Network Intrusion Detection Methods from the Perspective of
the Knowledge Discovery in Databases Process
- URL: http://arxiv.org/abs/2001.09697v1
- Date: Mon, 27 Jan 2020 11:21:05 GMT
- Title: Survey of Network Intrusion Detection Methods from the Perspective of
the Knowledge Discovery in Databases Process
- Authors: Borja Molina-Coronado and Usue Mori and Alexander Mendiburu and Jos\'e
Miguel-Alonso
- Abstract summary: We review the methods that have been applied to network data with the purpose of developing an intrusion detector.
We discuss the techniques used for the capture, preparation and transformation of the data, as well as, the data mining and evaluation methods.
As a result of this literature review, we investigate some open issues which will need to be considered for further research in the area of network security.
- Score: 63.75363908696257
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The identification of cyberattacks which target information and communication
systems has been a focus of the research community for years. Network intrusion
detection is a complex problem which presents a diverse number of challenges.
Many attacks currently remain undetected, while newer ones emerge due to the
proliferation of connected devices and the evolution of communication
technology. In this survey, we review the methods that have been applied to
network data with the purpose of developing an intrusion detector, but contrary
to previous reviews in the area, we analyze them from the perspective of the
Knowledge Discovery in Databases (KDD) process. As such, we discuss the
techniques used for the capture, preparation and transformation of the data, as
well as, the data mining and evaluation methods. In addition, we also present
the characteristics and motivations behind the use of each of these techniques
and propose more adequate and up-to-date taxonomies and definitions for
intrusion detectors based on the terminology used in the area of data mining
and KDD. Special importance is given to the evaluation procedures followed to
assess the different detectors, discussing their applicability in current real
networks. Finally, as a result of this literature review, we investigate some
open issues which will need to be considered for further research in the area
of network security.
Related papers
- Model Inversion Attacks: A Survey of Approaches and Countermeasures [59.986922963781]
Recently, a new type of privacy attack, the model inversion attacks (MIAs), aims to extract sensitive features of private data for training.
Despite the significance, there is a lack of systematic studies that provide a comprehensive overview and deeper insights into MIAs.
This survey aims to summarize up-to-date MIA methods in both attacks and defenses.
arXiv Detail & Related papers (2024-11-15T08:09:28Z) - Enhanced Anomaly Detection in Industrial Control Systems aided by Machine Learning [2.2457306746668766]
This study investigates whether combining both network and process data can improve attack detection in ICSs environments.
Our findings suggest that integrating network traffic with operational process data can enhance detection capabilities.
Although the results are promising, they are preliminary and highlight the need for further studies.
arXiv Detail & Related papers (2024-10-25T17:41:33Z) - Adversarial Challenges in Network Intrusion Detection Systems: Research Insights and Future Prospects [0.33554367023486936]
This paper provides a comprehensive review of machine learning-based Network Intrusion Detection Systems (NIDS)
We critically examine existing research in NIDS, highlighting key trends, strengths, and limitations.
We discuss emerging challenges in the field and offer insights for the development of more robust and resilient NIDS.
arXiv Detail & Related papers (2024-09-27T13:27:29Z) - TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns
for Intrusion Detection [0.5261718469769447]
Existing datasets often fall short, lacking the necessary diversity and alignment with the contemporary network environment.
This paper introduces TII-SSRC-23, a novel and comprehensive dataset designed to overcome these challenges.
arXiv Detail & Related papers (2023-09-14T05:23:36Z) - Exploring the Use of Data-Driven Approaches for Anomaly Detection in the
Internet of Things (IoT) Environment [4.724825031148412]
The Internet of Things (IoT) is a system that connects physical computing devices, sensors, software, and other technologies.
Data can be collected, transferred, and exchanged with other devices over the network without requiring human interactions.
Research on anomaly detection in the IoT environment has become popular and necessary in recent years.
arXiv Detail & Related papers (2022-12-31T06:28:58Z) - Adversarial Machine Learning In Network Intrusion Detection Domain: A
Systematic Review [0.0]
It has been found that deep learning models are vulnerable to data instances that can mislead the model to make incorrect classification decisions.
This survey explores the researches that employ different aspects of adversarial machine learning in the area of network intrusion detection.
arXiv Detail & Related papers (2021-12-06T19:10:23Z) - Finding Facial Forgery Artifacts with Parts-Based Detectors [73.08584805913813]
We design a series of forgery detection systems that each focus on one individual part of the face.
We use these detectors to perform detailed empirical analysis on the FaceForensics++, Celeb-DF, and Facebook Deepfake Detection Challenge datasets.
arXiv Detail & Related papers (2021-09-21T16:18:45Z) - A Comprehensive Survey on Community Detection with Deep Learning [93.40332347374712]
A community reveals the features and connections of its members that are different from those in other communities in a network.
This survey devises and proposes a new taxonomy covering different categories of the state-of-the-art methods.
The main category, i.e., deep neural networks, is further divided into convolutional networks, graph attention networks, generative adversarial networks and autoencoders.
arXiv Detail & Related papers (2021-05-26T14:37:07Z) - A Survey of Community Detection Approaches: From Statistical Modeling to
Deep Learning [95.27249880156256]
We develop and present a unified architecture of network community-finding methods.
We introduce a new taxonomy that divides the existing methods into two categories, namely probabilistic graphical model and deep learning.
We conclude with discussions of the challenges of the field and suggestions of possible directions for future research.
arXiv Detail & Related papers (2021-01-03T02:32:45Z) - Deep Learning for Sensor-based Human Activity Recognition: Overview,
Challenges and Opportunities [52.59080024266596]
We present a survey of the state-of-the-art deep learning methods for sensor-based human activity recognition.
We first introduce the multi-modality of the sensory data and provide information for public datasets.
We then propose a new taxonomy to structure the deep methods by challenges.
arXiv Detail & Related papers (2020-01-21T09:55:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.