Malware detection issues, challenges, and future directions: A survey
The evolution of recent malicious software with the rising use of digital services has
increased the probability of corrupting data, stealing information, or other cybercrimes by …
increased the probability of corrupting data, stealing information, or other cybercrimes by …
A survey of the recent trends in deep learning based malware detection
Monitoring Indicators of Compromise (IOC) leads to malware detection for identifying
malicious activity. Malicious activities potentially lead to a system breach or data …
malicious activity. Malicious activities potentially lead to a system breach or data …
A deep recurrent neural network based approach for internet of things malware threat hunting
Abstract Internet of Things (IoT) devices are increasingly deployed in different industries and
for different purposes (eg sensing/collecting of environmental data in both civilian and …
for different purposes (eg sensing/collecting of environmental data in both civilian and …
Opcode sequences as representation of executables for data-mining-based unknown malware detection
Malware can be defined as any type of malicious code that has the potential to harm a
computer or network. The volume of malware is growing faster every year and poses a …
computer or network. The volume of malware is growing faster every year and poses a …
Fuzzy pattern tree for edge malware detection and categorization in IoT
The surging pace of Internet of Things (IoT) development and its applications has resulted in
significantly large amounts of data (commonly known as big data) being communicated and …
significantly large amounts of data (commonly known as big data) being communicated and …
A survey on heuristic malware detection techniques
Z Bazrafshan, H Hashemi, SMH Fard… - The 5th conference on …, 2013 - ieeexplore.ieee.org
Malware is a malicious code which is developed to harm a computer or network. The
number of malwares is growing so fast and this amount of growth makes the computer …
number of malwares is growing so fast and this amount of growth makes the computer …
A survey of binary code similarity
Binary code similarityapproaches compare two or more pieces of binary code to identify their
similarities and differences. The ability to compare binary code enables many real-world …
similarities and differences. The ability to compare binary code enables many real-world …
Detecting cryptomining malware: a deep learning approach for static and dynamic analysis
Cryptomining malware (also referred to as cryptojacking) has changed the cyber threat
landscape. Such malware exploits the victim's CPU or GPU resources with the aim of …
landscape. Such malware exploits the victim's CPU or GPU resources with the aim of …
An improved two-hidden-layer extreme learning machine for malware hunting
Detecting unknown malware and their variants remains both an operational challenge and a
research challenge. In recent years, there have been attempts to design machine learning …
research challenge. In recent years, there have been attempts to design machine learning …
Semantics-based online malware detection: Towards efficient real-time protection against malware
Recently, malware has increasingly become a critical threat to embedded systems, while the
conventional software solutions, such as antivirus and patches, have not been so successful …
conventional software solutions, such as antivirus and patches, have not been so successful …