Malware traffic classification using convolutional neural network for representation learning

By Wei Wang, Ming Zhu, Xuewen Zeng, Xiaozhou Ye, Yiqiang Sheng. International Conference on Information Networking, 2017

924 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/886800987225c3a5d4c8bbf1d69294de9b891d48

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Paul Patras
2. Chaoyun Zhang
3. Hamed Haddadi
4. Essam Soliman Debie
5. Javed Asharf
6. Abdul Wahab
7. Hasnat Khurshid
8. Waqas Haider

In Computer Networks and Communications

1. Paul Patras
2. Chaoyun Zhang
3. Hamed Haddadi
4. Essam Soliman Debie
5. Javed Asharf
6. Abdul Wahab
7. Hasnat Khurshid
8. Waqas Haider

In Signal Processing

1. Essam Soliman Debie
2. Javed Asharf
3. Abdul Wahab
4. Hasnat Khurshid
5. Waqas Haider
6. Paul Patras
7. Hamed Haddadi
8. Chaoyun Zhang

Citations in the corpus

• A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions (2020, background): “CNNs [189,190] Malware attacks -CNN is best suited for highly efficient and fast feature extraction from raw data.”
• ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification (2022, background): “Task 2: EncryptedMalware Classification (EMC) is a collection of encrypted traffic consisting of malware and benign applications [39].”
• Deep Learning in Mobile and Wireless Networking: A Survey (2018, methodology): “[200] Malware traffic classification CNN SGD First work to use representation learning for malware classification from raw traffic Aceto et al.”
• Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017, methodology): “…provide a solution to this problem by self-organizing in real time and creating a competitive environment for neurons, automatically discriminating/creating new clusters [23] CNN CNN is used to extract features from the Internet traffic where traffic is considered as an image for malware detection.”
• A Survey of Deep Learning Methods for Cyber Security (2019, methodology): “[130] 17 Intrusion Detection CTU-13 [59], IXIA [131]”
• BAT: Deep Learning Methods on Network Intrusion Detection Using NSL-KDD Dataset (2020, background): “In [9], the authors propose a mal-ware traffic classificationmethod based on convolutional”
• HAST-IDS: Learning Hierarchical Spatial-Temporal Features Using Deep Neural Networks to Improve Intrusion Detection (2018, methodology): “Combining the previous research results [13], [48], we conclude that deep neural networks can automatically learn features directly from raw network traffic data and achieve good results in the field of intrusion detection or network anomaly detection.”