Add to ORKGThis dissertation proposes several improvements to existing adversarial attacks against MalConv, a raw-byte malware classifier for Windows PE files. The included contributions greatly improve the success rates and performance of gradient-based file overlay attacks. All improvements are included in a new open-source attack utility called BitCamo. Several new payload initialization strategies for use with gradient-based attacks are proposed and evaluated as potential replacements for the randomized initialization method used by current attacks. An algorithm for determining the optimal payload size is also proposed. The resulting improvements achieve a 100% evasion rate against eligible target executables using an average payload size of only ...
With the rise of the popularity of machine learning (ML), it has been shown that ML-based classifier...
Both malware and anti-virus detection tools advance in their capabilities–malware’s aim is to evade ...
With the increasingly rapid development of new malicious computer software by bad faith actors, both...
The global volume of malware attacks has risen significantly over the last decade. A large majority ...
Recent work has shown that adversarial Windows malware samples - referred to as adversarial EXEmples...
Modern commercial antivirus systems increasingly rely on machine learning to keep up with the rampan...
In the realm of modern technology, malware has become a paramount concern. Defined as any software d...
Training classifiers that are robust against adversarially modified examples is becoming increasingl...
While machine learning is vulnerable to adversarial examples, it still lacks systematic procedures a...
Modern commercial antivirus systems increasingly rely on machine learning to keep up with the rampan...
Cyber security is used to protect and safeguard computers and various networks from ill-intended dig...
Machine learning has proved to be a promising technology to determine whether a piece of software is...
Nowadays, Machine Learning (ML) solutions are widely adopted in modern malware and network intrusion...
Machine learning has proved to be a promising technology to determine whether a piece of software is...
While machine learning is vulnerable to adversarial examples, it still lacks systematic procedures a...
With the rise of the popularity of machine learning (ML), it has been shown that ML-based classifier...
Both malware and anti-virus detection tools advance in their capabilities–malware’s aim is to evade ...
With the increasingly rapid development of new malicious computer software by bad faith actors, both...
The global volume of malware attacks has risen significantly over the last decade. A large majority ...
Recent work has shown that adversarial Windows malware samples - referred to as adversarial EXEmples...
Modern commercial antivirus systems increasingly rely on machine learning to keep up with the rampan...
In the realm of modern technology, malware has become a paramount concern. Defined as any software d...
Training classifiers that are robust against adversarially modified examples is becoming increasingl...
While machine learning is vulnerable to adversarial examples, it still lacks systematic procedures a...
Modern commercial antivirus systems increasingly rely on machine learning to keep up with the rampan...
Cyber security is used to protect and safeguard computers and various networks from ill-intended dig...
Machine learning has proved to be a promising technology to determine whether a piece of software is...
Nowadays, Machine Learning (ML) solutions are widely adopted in modern malware and network intrusion...
Machine learning has proved to be a promising technology to determine whether a piece of software is...
While machine learning is vulnerable to adversarial examples, it still lacks systematic procedures a...
With the rise of the popularity of machine learning (ML), it has been shown that ML-based classifier...
Both malware and anti-virus detection tools advance in their capabilities–malware’s aim is to evade ...
With the increasingly rapid development of new malicious computer software by bad faith actors, both...