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Machine learning for intelligent data analysis and automation in cybersecurity: current and future prospects
IH Sarker - Annals of Data Science, 2023 - Springer
Due to the digitization and Internet of Things revolutions, the present electronic world has a
wealth of cybersecurity data. Efficiently resolving cyber anomalies and attacks is becoming a …
wealth of cybersecurity data. Efficiently resolving cyber anomalies and attacks is becoming a …
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 …
Ai-driven cybersecurity: an overview, security intelligence modeling and research directions
Artificial intelligence (AI) is one of the key technologies of the Fourth Industrial Revolution (or
Industry 4.0), which can be used for the protection of Internet-connected systems from cyber …
Industry 4.0), which can be used for the protection of Internet-connected systems from cyber …
Cybersecurity data science: an overview from machine learning perspective
IH Sarker, ASM Kayes, S Badsha, H Alqahtani… - Journal of Big …, 2020 - Springer
In a computing context, cybersecurity is undergoing massive shifts in technology and its
operations in recent days, and data science is driving the change. Extracting security …
operations in recent days, and data science is driving the change. Extracting security …
Benchmarking of machine learning for anomaly based intrusion detection systems in the CICIDS2017 dataset
ZK Maseer, R Yusof, N Bahaman, SA Mostafa… - IEEE …, 2021 - ieeexplore.ieee.org
An intrusion detection system (IDS) is an important protection instrument for detecting
complex network attacks. Various machine learning (ML) or deep learning (DL) algorithms …
complex network attacks. Various machine learning (ML) or deep learning (DL) algorithms …
An effective intrusion detection approach using SVM with naïve Bayes feature embedding
J Gu, S Lu - Computers & Security, 2021 - Elsevier
Network security has become increasingly important in recent decades, while intrusion
detection system plays a critical role in protecting it. Various machine learning techniques …
detection system plays a critical role in protecting it. Various machine learning techniques …
A hybrid model for building energy consumption forecasting using long short term memory networks
Data driven building energy consumption forecasting models play a significant role in
enhancing the energy efficiency of the buildings through building energy management …
enhancing the energy efficiency of the buildings through building energy management …
A survey on binary metaheuristic algorithms and their engineering applications
This article presents a comprehensively state-of-the-art investigation of the engineering
applications utilized by binary metaheuristic algorithms. Surveyed work is categorized based …
applications utilized by binary metaheuristic algorithms. Surveyed work is categorized based …
Machine learning for intrusion detection in industrial control systems: Applications, challenges, and recommendations
Methods from machine learning are used in the design of secure Industrial Control Systems.
Such methods focus on two major areas: detection of intrusions at the network level using …
Such methods focus on two major areas: detection of intrusions at the network level using …
A two-stage intrusion detection system with auto-encoder and LSTMs
Abstract 'Curse of dimensionality'and the trade-off between low false alarm rate and high
detection rate are the major concerns while designing an efficient intrusion detection system …
detection rate are the major concerns while designing an efficient intrusion detection system …