A Systematic Review of IoT Security: Research Potential, Challenges, and Future Directions

W Fei, H Ohno, S Sampalli - ACM Computing Surveys, 2023 - dl.acm.org
The Internet of Things (IoT) encompasses a network of physical objects embedded with
sensors, software, and data processing technologies that can establish connections and …

Deep learning methods for malware and intrusion detection: A systematic literature review

R Ali, A Ali, F Iqbal, M Hussain… - Security and …, 2022 - Wiley Online Library
Android and Windows are the predominant operating systems used in mobile environment
and personal computers and it is expected that their use will rise during the next decade …

Security by design for big data frameworks over cloud computing

FM Awaysheh, MN Aladwan, M Alazab… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
Cloud deployment architectures have become a preferable computation model of Big Data
(BD) operations. Their scalability, flexibility, and cost-effectiveness motivated this trend. In a …

Future smart connected communities to fight covid-19 outbreak

D Gupta, S Bhatt, M Gupta, AS Tosun - Internet of Things, 2021 - Elsevier
Abstract Internet of Things (IoT) has grown rapidly in the last decade and continues to
develop in terms of dimension and complexity, offering a wide range of devices to support a …

A survey on adversarial attacks for malware analysis

K Aryal, M Gupta, M Abdelsalam, P Kunwar… - IEEE …, 2024 - ieeexplore.ieee.org
Machine learning-based malware analysis approaches are widely researched and
deployed in critical infrastructures for detecting and classifying evasive and growing …

Intrusion detection in cyber-physical systems using a generic and domain specific deep autoencoder model

S Thakur, A Chakraborty, R De, N Kumar… - Computers & Electrical …, 2021 - Elsevier
The rapid growth of network-related services in the last decade has produced a huge
amount of sensitive data on the internet. But networks are very much prone to intrusions …

Creating cybersecurity knowledge graphs from malware after action reports

A Piplai, S Mittal, A Joshi, T Finin, J Holt, R Zak - IEEE Access, 2020 - ieeexplore.ieee.org
After Action Reports (AARs) provide incisive analysis of cyber-incidents. Extracting cyber-
knowledge from these sources would provide security analysts with credible information …

Automated machine learning for deep learning based malware detection

A Brown, M Gupta, M Abdelsalam - Computers & Security, 2024 - Elsevier
Deep learning (DL) has proven to be effective in detecting sophisticated malware that is
constantly evolving. Even though deep learning has alleviated the feature engineering …

RWArmor: a static-informed dynamic analysis approach for early detection of cryptographic windows ransomware

MA Ayub, A Siraj, B Filar, M Gupta - International Journal of Information …, 2024 - Springer
Ransomware attacks have captured news headlines worldwide for the last few years due to
their criticality and intensity. Ransomware-as-a-service (RaaS) kits are aiding adversaries to …

Recurrent neural networks based online behavioural malware detection techniques for cloud infrastructure

JC Kimmel, AD Mcdole, M Abdelsalam, M Gupta… - IEEE …, 2021 - ieeexplore.ieee.org
Several organizations are utilizing cloud technologies and resources to run a range of
applications. These services help businesses save on hardware management, scalability …