Machine learning based solutions for security of Internet of Things (IoT): A survey

SM Tahsien, H Karimipour, P Spachos - Journal of Network and Computer …, 2020 - Elsevier
Over the last decade, IoT platforms have been developed into a global giant that grabs every
aspect of our daily lives by advancing human life with its unaccountable smart services …

A survey of machine and deep learning methods for internet of things (IoT) security

MA Al-Garadi, A Mohamed, AK Al-Ali… - … surveys & tutorials, 2020 - ieeexplore.ieee.org
The Internet of Things (IoT) integrates billions of smart devices that can communicate with
one another with minimal human intervention. IoT is one of the fastest developing fields in …

A review of intrusion detection systems using machine and deep learning in internet of things: Challenges, solutions and future directions

J Asharf, N Moustafa, H Khurshid, E Debie, W Haider… - Electronics, 2020 - mdpi.com
The Internet of Things (IoT) is poised to impact several aspects of our lives with its fast
proliferation in many areas such as wearable devices, smart sensors and home appliances …

A fast network intrusion detection system using adaptive synthetic oversampling and LightGBM

J Liu, Y Gao, F Hu - Computers & Security, 2021 - Elsevier
Network intrusion detection systems play an important role in protecting the network from
attacks. However, Existing network intrusion data is imbalanced, which makes it difficult to …

Intrusion detection of imbalanced network traffic based on machine learning and deep learning

L Liu, P Wang, J Lin, L Liu - IEEE access, 2020 - ieeexplore.ieee.org
In imbalanced network traffic, malicious cyber-attacks can often hide in large amounts of
normal data. It exhibits a high degree of stealth and obfuscation in cyberspace, making it …

Intrudtree: a machine learning based cyber security intrusion detection model

IH Sarker, YB Abushark, F Alsolami, AI Khan - Symmetry, 2020 - mdpi.com
Cyber security has recently received enormous attention in today's security concerns, due to
the popularity of the Internet-of-Things (IoT), the tremendous growth of computer networks …

Cybersecurity threats and their mitigation approaches using Machine Learning—A Review

M Ahsan, KE Nygard, R Gomes… - … of Cybersecurity and …, 2022 - mdpi.com
Machine learning is of rising importance in cybersecurity. The primary objective of applying
machine learning in cybersecurity is to make the process of malware detection more …

A survey of data mining and machine learning methods for cyber security intrusion detection

AL Buczak, E Guven - IEEE Communications surveys & tutorials, 2015 - ieeexplore.ieee.org
This survey paper describes a focused literature survey of machine learning (ML) and data
mining (DM) methods for cyber analytics in support of intrusion detection. Short tutorial …

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 …

The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set

N Moustafa, J Slay - Information Security Journal: A Global …, 2016 - Taylor & Francis
Over the last three decades, Network Intrusion Detection Systems (NIDSs), particularly,
Anomaly Detection Systems (ADSs), have become more significant in detecting novel …