Detecting cybersecurity attacks in internet of things using artificial intelligence methods: A systematic literature review

M Abdullahi, Y Baashar, H Alhussian, A Alwadain… - Electronics, 2022 - mdpi.com
In recent years, technology has advanced to the fourth industrial revolution (Industry 4.0),
where the Internet of things (IoTs), fog computing, computer security, and cyberattacks have …

When smart cities get smarter via machine learning: An in-depth literature review

SS Band, S Ardabili, M Sookhak… - IEEE …, 2022 - ieeexplore.ieee.org
The manuscript represents a comeprehensive and systematic literature review on the
machine learning methods in the emerging applications of the smart cities. Application …

Future smart cities: Requirements, emerging technologies, applications, challenges, and future aspects

AR Javed, F Shahzad, S ur Rehman, YB Zikria… - Cities, 2022 - Elsevier
Future smart cities are the key to fulfilling the ever-growing demands of citizens. Information
and communication advancements will empower better administration of accessible …

An experimental analysis of attack classification using machine learning in IoT networks

A Churcher, R Ullah, J Ahmad, S Ur Rehman… - Sensors, 2021 - mdpi.com
In recent years, there has been a massive increase in the amount of Internet of Things (IoT)
devices as well as the data generated by such devices. The participating devices in IoT …

A tree-based stacking ensemble technique with feature selection for network intrusion detection

M Rashid, J Kamruzzaman, T Imam, S Wibowo… - Applied …, 2022 - Springer
Several studies have used machine learning algorithms to develop intrusion systems (IDS),
which differentiate anomalous behaviours from the normal activities of network systems. Due …

Confidence interval for micro-averaged F1 and macro-averaged F1 scores

K Takahashi, K Yamamoto, A Kuchiba, T Koyama - Applied Intelligence, 2022 - Springer
A binary classification problem is common in medical field, and we often use sensitivity,
specificity, accuracy, negative and positive predictive values as measures of performance of …

An improved PIO feature selection algorithm for IoT network intrusion detection system based on ensemble learning

OA Alghanam, W Almobaideen, M Saadeh… - Expert Systems with …, 2023 - Elsevier
With the rapid growth of the number of connected devices that exchange personal, sensitive,
and important data through the IoT based global network, attacks that are targeting security …

BoostedEnML: Efficient technique for detecting cyberattacks in IoT systems using boosted ensemble machine learning

OD Okey, SS Maidin, P Adasme, R Lopes Rosa… - Sensors, 2022 - mdpi.com
Following the recent advances in wireless communication leading to increased Internet of
Things (IoT) systems, many security threats are currently ravaging IoT systems, causing …

Cyber threat predictive analytics for improving cyber supply chain security

A Yeboah-Ofori, S Islam, SW Lee… - IEEE …, 2021 - ieeexplore.ieee.org
Cyber Supply Chain (CSC) system is complex which involves different sub-systems
performing various tasks. Security in supply chain is challenging due to the inherent …

Zero-day attack detection: a systematic literature review

R Ahmad, I Alsmadi, W Alhamdani… - Artificial Intelligence …, 2023 - Springer
With the continuous increase in cyberattacks over the past few decades, the quest to
develop a comprehensive, robust, and effective intrusion detection system (IDS) in the …