A taxonomy and survey of attacks against machine learning
The majority of machine learning methodologies operate with the assumption that their
environment is benign. However, this assumption does not always hold, as it is often …
environment is benign. However, this assumption does not always hold, as it is often …
A review of spam email detection: analysis of spammer strategies and the dataset shift problem
F Jáñez-Martino, R Alaiz-Rodríguez… - Artificial Intelligence …, 2023 - Springer
Spam emails have been traditionally seen as just annoying and unsolicited emails
containing advertisements, but they increasingly include scams, malware or phishing. In …
containing advertisements, but they increasingly include scams, malware or phishing. In …
A survey on machine learning techniques for cyber security in the last decade
Pervasive growth and usage of the Internet and mobile applications have expanded
cyberspace. The cyberspace has become more vulnerable to automated and prolonged …
cyberspace. The cyberspace has become more vulnerable to automated and prolonged …
MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks
Many real-world cyber-physical systems (CPSs) are engineered for mission-critical tasks
and usually are prime targets for cyber-attacks. The rich sensor data in CPSs can be …
and usually are prime targets for cyber-attacks. The rich sensor data in CPSs can be …
Wild patterns: Ten years after the rise of adversarial machine learning
Deep neural networks and machine-learning algorithms are pervasively used in several
applications, ranging from computer vision to computer security. In most of these …
applications, ranging from computer vision to computer security. In most of these …
A novel method for improving the robustness of deep learning-based malware detectors against adversarial attacks
Malware is constantly evolving with rising concern for cyberspace. Deep learning-based
malware detectors are being used as a potential solution. However, these detectors are …
malware detectors are being used as a potential solution. However, these detectors are …
Why do adversarial attacks transfer? explaining transferability of evasion and poisoning attacks
Transferability captures the ability of an attack against a machine-learning model to be
effective against a different, potentially unknown, model. Empirical evidence for …
effective against a different, potentially unknown, model. Empirical evidence for …
Adversarial malware binaries: Evading deep learning for malware detection in executables
Machine learning has already been exploited as a useful tool for detecting malicious
executable files. Data retrieved from malware samples, such as header fields, instruction …
executable files. Data retrieved from malware samples, such as header fields, instruction …
A survey on security threats and defensive techniques of machine learning: A data driven view
Machine learning is one of the most prevailing techniques in computer science, and it has
been widely applied in image processing, natural language processing, pattern recognition …
been widely applied in image processing, natural language processing, pattern recognition …
Ai-based mobile edge computing for iot: Applications, challenges, and future scope
New technology is needed to meet the latency and bandwidth issues present in cloud
computing architecture specially to support the currency of 5G networks. Accordingly, mobile …
computing architecture specially to support the currency of 5G networks. Accordingly, mobile …