Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges
In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …
[HTML][HTML] Cyber risk and cybersecurity: a systematic review of data availability
Cybercrime is estimated to have cost the global economy just under USD 1 trillion in 2020,
indicating an increase of more than 50% since 2018. With the average cyber insurance …
indicating an increase of more than 50% since 2018. With the average cyber insurance …
Explainable artificial intelligence applications in cyber security: State-of-the-art in research
This survey presents a comprehensive review of current literature on Explainable Artificial
Intelligence (XAI) methods for cyber security applications. Due to the rapid development of …
Intelligence (XAI) methods for cyber security applications. Due to the rapid development of …
[HTML][HTML] HCRNNIDS: Hybrid convolutional recurrent neural network-based network intrusion detection system
MA Khan - Processes, 2021 - mdpi.com
Nowadays, network attacks are the most crucial problem of modern society. All networks,
from small to large, are vulnerable to network threats. An intrusion detection (ID) system is …
from small to large, are vulnerable to network threats. An intrusion detection (ID) system is …
A survey on data-driven network intrusion detection
D Chou, M Jiang - ACM Computing Surveys (CSUR), 2021 - dl.acm.org
Data-driven network intrusion detection (NID) has a tendency towards minority attack
classes compared to normal traffic. Many datasets are collected in simulated environments …
classes compared to normal traffic. Many datasets are collected in simulated environments …
A survey on deep learning for cybersecurity: Progress, challenges, and opportunities
As the number of Internet-connected systems rises, cyber analysts find it increasingly difficult
to effectively monitor the produced volume of data, its velocity and diversity. Signature-based …
to effectively monitor the produced volume of data, its velocity and diversity. Signature-based …
[HTML][HTML] Analysis of autoencoders for network intrusion detection
As network attacks are constantly and dramatically evolving, demonstrating new patterns,
intelligent Network Intrusion Detection Systems (NIDS), using deep-learning techniques …
intelligent Network Intrusion Detection Systems (NIDS), using deep-learning techniques …
[HTML][HTML] Machine learning approach equipped with neighbourhood component analysis for DDoS attack detection in software-defined networking
The Software-Defined Network (SDN) is a new network paradigm that promises more
dynamic and efficiently manageable network architecture for new-generation networks. With …
dynamic and efficiently manageable network architecture for new-generation networks. With …
[HTML][HTML] Network intrusion detection model based on CNN and GRU
B Cao, C Li, Y Song, Y Qin, C Chen - Applied Sciences, 2022 - mdpi.com
A network intrusion detection model that fuses a convolutional neural network and a gated
recurrent unit is proposed to address the problems associated with the low accuracy of …
recurrent unit is proposed to address the problems associated with the low accuracy of …
[HTML][HTML] A deep learning ensemble for network anomaly and cyber-attack detection
Currently, expert systems and applied machine learning algorithms are widely used to
automate network intrusion detection. In critical infrastructure applications of communication …
automate network intrusion detection. In critical infrastructure applications of communication …