Deep Learning and Regularization Algorithms for Malicious Code Classification
نویسندگان
چکیده
منابع مشابه
A Hybrid Malicious Code Detection Method based on Deep Learning
In this paper, we propose a hybrid malicious code detection scheme based on AutoEncoder and DBN (Deep Belief Networks). Firstly, we use the AutoEncoder deep learning method to reduce the dimensionality of data. This could convert complicated high-dimensional data into low dimensional codes with the nonlinear mapping, thereby reducing the dimensionality of data, extracting the main features of t...
متن کاملDeep learning - Regularization
where, θ̃ is an estimator of θ coming from update equations or solution of optimization procedure. Variability in θ̃ is because of randomness in data and bias is due to model mismatch. Well known bias-variance trade offAs complexity of the model is increased model mismatch(bias) is decreased while variance in the prediction is increased because of randomness in training inputs. 4. Deep Learning s...
متن کاملMalicious Code Detection Using Active Learning
The recent growth in network usage has motivated the creation of new malicious code for various purposes, including economic and other malicious purposes. Currently, dozens of new malicious codes are created every day and this number is expected to increase in the coming years. Today’s signature-based anti-viruses and heuristic-based methods are accurate, but cannot detect new malicious code. R...
متن کاملRegularization for Deep Learning: A Taxonomy
Regularization is one of the crucial ingredients of deep learning, yet the term regularization has various definitions, and regularization methods are often studied separately from each other. In our work we present a systematic, unifying taxonomy to categorize existing methods. We distinguish methods that affect data, network architectures, error terms, regularization terms, and optimization p...
متن کاملRegularization for Deep Learning: A Taxonomy
Regularization is one of the crucial ingredients of deep learning, yet the term regularization has various definitions, and regularization methods are often studied separately from each other. In our work we present a novel, systematic, unifying taxonomy to categorize existing methods. We distinguish methods that affect data, network architectures, error terms, regularization terms, and optimiz...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2021
ISSN: 2169-3536
DOI: 10.1109/access.2021.3090464