Explainable artificial intelligence applications in cyber security: State-of-the-art in research

Z Zhang, H Al Hamadi, E Damiani, CY Yeun… - IEEE …, 2022 - ieeexplore.ieee.org
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 …

A survey on deep learning for cybersecurity: Progress, challenges, and opportunities

M Macas, C Wu, W Fuertes - Computer Networks, 2022 - Elsevier
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 …

Explainable artificial intelligence in cybersecurity: A survey

N Capuano, G Fenza, V Loia, C Stanzione - IEEE Access, 2022 - ieeexplore.ieee.org
Nowadays, Artificial Intelligence (AI) is widely applied in every area of human being's daily
life. Despite the AI benefits, its application suffers from the opacity of complex internal …

A comparative performance analysis of data resampling methods on imbalance medical data

M Khushi, K Shaukat, TM Alam, IA Hameed… - IEEE …, 2021 - ieeexplore.ieee.org
Medical datasets are usually imbalanced, where negative cases severely outnumber
positive cases. Therefore, it is essential to deal with this data skew problem when training …

A novel method for improving the robustness of deep learning-based malware detectors against adversarial attacks

K Shaukat, S Luo, V Varadharajan - Engineering Applications of Artificial …, 2022 - Elsevier
Malware is constantly evolving with rising concern for cyberspace. Deep learning-based
malware detectors are being used as a potential solution. However, these detectors are …

A novel deep learning-based approach for malware detection

K Shaukat, S Luo, V Varadharajan - Engineering Applications of Artificial …, 2023 - Elsevier
Malware detection approaches can be classified into two classes, including static analysis
and dynamic analysis. Conventional approaches of the two classes have their respective …

A systematic review on machine learning and deep learning models for electronic information security in mobile networks

C Gupta, I Johri, K Srinivasan, YC Hu, SM Qaisar… - Sensors, 2022 - mdpi.com
Today's advancements in wireless communication technologies have resulted in a
tremendous volume of data being generated. Most of our information is part of a widespread …

Realguard: A lightweight network intrusion detection system for IoT gateways

XH Nguyen, XD Nguyen, HH Huynh, KH Le - Sensors, 2022 - mdpi.com
Cyber security has become increasingly challenging due to the proliferation of the Internet of
things (IoT), where a massive number of tiny, smart devices push trillion bytes of data to the …

Application of machine learning techniques in injection molding quality prediction: Implications on sustainable manufacturing industry

H Jung, J Jeon, D Choi, JY Park - Sustainability, 2021 - mdpi.com
With sustainable growth highlighted as a key to success in Industry 4.0, manufacturing
companies attempt to optimize production efficiency. In this study, we investigated whether …

Ai/ml for network security: The emperor has no clothes

AS Jacobs, R Beltiukov, W Willinger… - Proceedings of the …, 2022 - dl.acm.org
Several recent research efforts have proposed Machine Learning (ML)-based solutions that
can detect complex patterns in network traffic for a wide range of network security problems …