Large-scale malware classification using random projections and neural networks

By George E. Dahl, Jack W. Stokes, Li Deng, Dong Yu. 2013

461 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Anomaly & Outlier Detection, Signal Processing

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Citations in the corpus

Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks (2015, background): “As a result, DNNs are used in many settings [1], [2], [3], some of which are increasingly security-sensitive [4], [5], [6].”
Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning (2017, background): “Therefore, deep learning systems have become prevalent in our lives, including security-sensitive applications such as face recognition [55], [62], fingerprint identification [66], spam filtering [64], [56], malware detection [21], [57], and autonomous vehicles [17].”
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks (2017, background): “Deep Neural Networks (DNNs) perform exceptionally well on many artiﬁcial intelligence tasks, including security-sensitive applications like malware classiﬁcation [15, 4] and face recognition [22].”
The Limitations of Deep Learning in Adversarial Settings (2015, positive): “In short, DL is transforming computational processing of complex data in many domains such as vision [24], [37], speech recognition [15], [32], [33], language processing [13], financial fraud detection [23], and recently malware detection [14].”
Defeating Image Obfuscation with Deep Learning (2016, methodology): “Deep neural networks have been used for malware classiﬁcation in general [8, 37] and for speciﬁcally detecting Android malware [53].”
Automatically Evading Classifiers: A Case Study on PDF Malware Classifiers (2016, background): “58% accuracy in classifying Win32 malware using an ensemble deep neural network with dynamic features [9].”
Deep Learning: Methods and Applications (2014, background): “For interested readers, please consult recent papers on the applications of deep learning to optimal control in [219], to reinforcement learning in [256], to malware classification in [66], to compressed sensing in [277], to recognition confidence prediction in [173], to acoustic-articulatory invers”
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks (2017, background): “This can be problematic for classifiers used in security sensitive or safety focused regimes such as malware detection or self-driving cars [12, 6].”
Crafting adversarial input sequences for recurrent neural networks (2016, background): “In such settings, the output data nature characterizes varying problems like classification [12], [13], [14], pattern recognition [15], or regression [16].”
Malware classification with recurrent networks (2015, methodology): “∗The first author performed the work while at Microsoft Research wide variety of machine learning classifiers such as logistic regression [2], neural networks [3, 4], and decision trees [5] for malware classification.”
Enhancing robustness of machine learning systems via data transformations (2017, background): “Machine learning is being used in a number of essential applications such as image recognition [26], natural language processing [9], spam detection [10], autonomous vehicles [8], [33] and even malware detection [46], [11].”
Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey (2019, methodology): “[114] PE binary CNN in [27] Boundaries employ L∞ and L2 MAD”