B
Bo Lang
Researcher at Beihang University
Publications - 61
Citations - 2311
Bo Lang is an academic researcher from Beihang University. The author has contributed to research in topics: Hash table & Dynamic perfect hashing. The author has an hindex of 23, co-authored 60 publications receiving 1752 citations. Previous affiliations of Bo Lang include Xidian University & University of Michigan.
Papers
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Journal ArticleDOI
Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey
Hongyu Liu,Bo Lang +1 more
TL;DR: A taxonomy of IDS is proposed that takes data objects as the main dimension to classify and summarize machine learning- based and deep learning-based IDS literature, and believes that this type of taxonomy framework is fit for cyber security researchers.
Proceedings ArticleDOI
Decorrelated Batch Normalization
TL;DR: Decorrelated batch normalization (DBN) as mentioned in this paper whitens the activations to accelerate the training of deep models by centering and scaling activations within mini-batches.
Journal ArticleDOI
CNN and RNN based payload classification methods for attack detection
TL;DR: These two approaches learn feature representations from original payloads without feature engineering and support end-to-end detection and achieve accuracies comparable to or better than those of state-of-the-art methods.
Proceedings ArticleDOI
Collaborative Hashing
TL;DR: This work proposes a collaborative hashing scheme for the data in matrix form to enable fast search in various applications such as image search using bag of words and recommendation using user-item ratings, and demonstrates that the proposed method outperforms state-of-the-art baselines.
Proceedings Article
Orthogonal Weight Normalization: Solution to Optimization Over Multiple Dependent Stiefel Manifolds in Deep Neural Networks.
TL;DR: In this article, an orthogonal weight normalization method was proposed to solve OMDSM in feed-forward Neural Networks (FNNs), which can stabilize the distribution of network activations and regularize FNNs.