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Malware Analysis Using Artificial Intelligence and Deep Learning
Discuss artificial intelligence and deep learning techniques applied to malware analysis and detection, as well as other cybersecurity challenges. They cover various neural network architectures like MLPs, CNNs, RNNs, LSTMs, and GANs, and their effectiveness in tasks such as classifying malware families, identifying malicious URLs, and detecting anomalies in network traffic or system logs. The papers also explore methods for feature extraction from malware binaries, including static and dynamic analysis, and how adversarial examples can challenge these detection systems. Furthermore, they address the use of AI for troll detection on social media platforms and image spam classification.
You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cyber_security_summary
Get the Book now from Amazon:
https://www.amazon.com/Malware-Analysis-Artificial-Intelligence-Learning/dp/3030625818?&linkCode=ll1&tag=cvthunderx-20&linkId=c97d080f094227b8fb921fea640e5e56&language=en_US&ref_=as_li_ss_tl
Discover our free courses in tech and cybersecurity, Start learning today:
https://linktr.ee/cybercode_academy
You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cyber_security_summary
Get the Book now from Amazon:
https://www.amazon.com/Malware-Analysis-Artificial-Intelligence-Learning/dp/3030625818?&linkCode=ll1&tag=cvthunderx-20&linkId=c97d080f094227b8fb921fea640e5e56&language=en_US&ref_=as_li_ss_tl
Discover our free courses in tech and cybersecurity, Start learning today:
https://linktr.ee/cybercode_academy
View all episodes
By CyberSecurity Summary
2.2
55 ratings
Discuss artificial intelligence and deep learning techniques applied to malware analysis and detection, as well as other cybersecurity challenges. They cover various neural network architectures like MLPs, CNNs, RNNs, LSTMs, and GANs, and their effectiveness in tasks such as classifying malware families, identifying malicious URLs, and detecting anomalies in network traffic or system logs. The papers also explore methods for feature extraction from malware binaries, including static and dynamic analysis, and how adversarial examples can challenge these detection systems. Furthermore, they address the use of AI for troll detection on social media platforms and image spam classification.
You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cyber_security_summary
Get the Book now from Amazon:
https://www.amazon.com/Malware-Analysis-Artificial-Intelligence-Learning/dp/3030625818?&linkCode=ll1&tag=cvthunderx-20&linkId=c97d080f094227b8fb921fea640e5e56&language=en_US&ref_=as_li_ss_tl
Discover our free courses in tech and cybersecurity, Start learning today:
https://linktr.ee/cybercode_academy
You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cyber_security_summary
Get the Book now from Amazon:
https://www.amazon.com/Malware-Analysis-Artificial-Intelligence-Learning/dp/3030625818?&linkCode=ll1&tag=cvthunderx-20&linkId=c97d080f094227b8fb921fea640e5e56&language=en_US&ref_=as_li_ss_tl
Discover our free courses in tech and cybersecurity, Start learning today:
https://linktr.ee/cybercode_academy
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