Smart grid cyber-physical situational awareness of complex operational technology attacks: A review
The smart grid (SG), regarded as the complex cyber-physical ecosystem of infrastructures,
orchestrates advanced communication, computation, and control technologies to interact …
orchestrates advanced communication, computation, and control technologies to interact …
A survey on ethereum systems security: Vulnerabilities, attacks, and defenses
Blockchain technology is believed by many to be a game changer in many application
domains. While the first generation of blockchain technology (ie, Blockchain 1.0) is almost …
domains. While the first generation of blockchain technology (ie, Blockchain 1.0) is almost …
Deepwukong: Statically detecting software vulnerabilities using deep graph neural network
Static bug detection has shown its effectiveness in detecting well-defined memory errors, eg,
memory leaks, buffer overflows, and null dereference. However, modern software systems …
memory leaks, buffer overflows, and null dereference. However, modern software systems …
Vuldeepecker: A deep learning-based system for vulnerability detection
The automatic detection of software vulnerabilities is an important research problem.
However, existing solutions to this problem rely on human experts to define features and …
However, existing solutions to this problem rely on human experts to define features and …
Sysevr: A framework for using deep learning to detect software vulnerabilities
The detection of software vulnerabilities (or vulnerabilities for short) is an important problem
that has yet to be tackled, as manifested by the many vulnerabilities reported on a daily …
that has yet to be tackled, as manifested by the many vulnerabilities reported on a daily …
A review of recent approaches on wrapper feature selection for intrusion detection
In this paper, we present a review of recent advances in wrapper feature selection
techniques for attack detection and classification, applied in intrusion detection area. Due to …
techniques for attack detection and classification, applied in intrusion detection area. Due to …
Cyber threat intelligence model: an evaluation of taxonomies, sharing standards, and ontologies within cyber threat intelligence
V Mavroeidis, S Bromander - 2017 European Intelligence and …, 2017 - ieeexplore.ieee.org
Threat intelligence is the provision of evidence-based knowledge about existing or potential
threats. Benefits of threat intelligence include improved efficiency and effectiveness in …
threats. Benefits of threat intelligence include improved efficiency and effectiveness in …
Vuldeelocator: a deep learning-based fine-grained vulnerability detector
Automatically detecting software vulnerabilities is an important problem that has attracted
much attention from the academic research community. However, existing vulnerability …
much attention from the academic research community. However, existing vulnerability …
Adversarial deep ensemble: Evasion attacks and defenses for malware detection
Malware remains a big threat to cyber security, calling for machine learning based malware
detection. While promising, such detectors are known to be vulnerable to evasion attacks …
detection. While promising, such detectors are known to be vulnerable to evasion attacks …
[HTML][HTML] A comprehensive model of information security factors for decision-makers
Decision-making in the context of organizational information security is highly dependent on
various information. For information security managers, not only relevant information has to …
various information. For information security managers, not only relevant information has to …