TY  - GEN
A1  - Gawron, Marian
A1  - Cheng, Feng
A1  - Meinel, Christoph
T1  - Automatic vulnerability classification using machine learning
T2  - Risks and Security of Internet and Systems
N2  - The classification of vulnerabilities is a fundamental step to derive formal attributes that allow a deeper analysis. Therefore, it is required that this classification has to be performed timely and accurate. Since the current situation demands a manual interaction in the classification process, the timely processing becomes a serious issue. Thus, we propose an automated alternative to the manual classification, because the amount of identified vulnerabilities per day cannot be processed manually anymore. We implemented two different approaches that are able to automatically classify vulnerabilities based on the vulnerability description. We evaluated our approaches, which use Neural Networks and the Naive Bayes methods respectively, on the base of publicly known vulnerabilities.
KW  - Vulnerability analysis
KW  - Security analytics
KW  - Data mining Machine learning
KW  - Neural Networks
Y1  - 2018
SN  - 978-3-319-76687-4
SN  - 978-3-319-76686-7
U6  - https://doi.org/10.1007/978-3-319-76687-4_1
SN  - 0302-9743
SN  - 1611-3349
SP  - 3
EP  - 17
PB  - Springer
CY  - Cham
ER  - 
TY  - JOUR
A1  - Long, Xiang
A1  - de Melo, Gerard
A1  - He, Dongliang
A1  - Li, Fu
A1  - Chi, Zhizhen
A1  - Wen, Shilei
A1  - Gan, Chuang
T1  - Purely attention based local feature integration for video classification
JF  - IEEE Transactions on Pattern Analysis and Machine Intelligence
N2  - Recently, substantial research effort has focused on how to apply CNNs or RNNs to better capture temporal patterns in videos, so as to improve the accuracy of video classification. In this paper, we investigate the potential of a purely attention based local feature integration. Accounting for the characteristics of such features in video classification, we first propose Basic Attention Clusters (BAC), which concatenates the output of multiple attention units applied in parallel, and introduce a shifting operation to capture more diverse signals. Experiments show that BAC can achieve excellent results on multiple datasets. However, BAC treats all feature channels as an indivisible whole, which is suboptimal for achieving a finer-grained local feature integration over the channel dimension. Additionally, it treats the entire local feature sequence as an unordered set, thus ignoring the sequential relationships. To improve over BAC, we further propose the channel pyramid attention schema by splitting features into sub-features at multiple scales for coarse-to-fine sub-feature interaction modeling, and propose the temporal pyramid attention schema by dividing the feature sequences into ordered sub-sequences of multiple lengths to account for the sequential order. Our final model pyramidxpyramid attention clusters (PPAC) combines both channel pyramid attention and temporal pyramid attention to focus on the most important sub-features, while also preserving the temporal information of the video. We demonstrate the effectiveness of PPAC on seven real-world video classification datasets. Our model achieves competitive results across all of these, showing that our proposed framework can consistently outperform the existing local feature integration methods across a range of different scenarios.
KW  - Feature extraction
KW  - Convolution
KW  - Computational modeling
KW  - Plugs
KW  - Three-dimensional displays
KW  - Task analysis
KW  - Two dimensional displays
KW  - Video classification
KW  - action recognition
KW  - attention mechanism
KW  - computer vision
KW  - Algorithms
KW  - Neural Networks
KW  - Computer
Y1  - 2020
U6  - https://doi.org/10.1109/TPAMI.2020.3029554
SN  - 0162-8828
SN  - 1939-3539
SN  - 2160-9292
VL  - 44
IS  - 4
SP  - 2140
EP  - 2154
PB  - Inst. of Electr. and Electronics Engineers
CY  - Los Alamitos
ER  -