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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 …
sensors, software, and data processing technologies that can establish connections and …
Deep learning methods for malware and intrusion detection: A systematic literature review
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 …
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
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 …
(BD) operations. Their scalability, flexibility, and cost-effectiveness motivated this trend. In a …
Future smart connected communities to fight covid-19 outbreak
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 …
develop in terms of dimension and complexity, offering a wide range of devices to support a …
A survey on adversarial attacks for malware analysis
Machine learning-based malware analysis approaches are widely researched and
deployed in critical infrastructures for detecting and classifying evasive and growing …
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
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 …
amount of sensitive data on the internet. But networks are very much prone to intrusions …
Creating cybersecurity knowledge graphs from malware after action reports
After Action Reports (AARs) provide incisive analysis of cyber-incidents. Extracting cyber-
knowledge from these sources would provide security analysts with credible information …
knowledge from these sources would provide security analysts with credible information …
Automated machine learning for deep learning based malware detection
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 …
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
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 …
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
Several organizations are utilizing cloud technologies and resources to run a range of
applications. These services help businesses save on hardware management, scalability …
applications. These services help businesses save on hardware management, scalability …