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FINGERPRINTING MALICIOUS IP TRAFFIC

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Correlation based malicious traffic analysis system

International Journal of Knowledge-based and Intelligent Engineering Systems, 2021
Artificial intelligence methods have often been applied to carry out specific functions or errands in the cyber-defense realm. However, as adversary methods become more complex and difficult to divine, piecemeal efforts to understand cyber-attacks, and malware-based attacks in particular, are not providing sufficient means for malware analysts to ...
Arivudainambi, D., Varun Kumar, K.A., Satapathy, Suresh Chandra   +2 more
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Frequency Domain Feature Based Robust Malicious Traffic Detection

IEEE/ACM Transactions on Networking, 2023
Machine learning (ML) based malicious traffic detection is an emerging security paradigm, particularly for zero-day attack detection, which is complementary to existing rule based detection. However, the existing ML based detection achieves low detection
Chuanpu Fu, Qi Li, Meng Shen, Ke Xu
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Flow-MAE: Leveraging Masked AutoEncoder for Accurate, Efficient and Robust Malicious Traffic Classification

International Symposium on Recent Advances in Intrusion Detection, 2023
Malicious traffic classification is crucial for Intrusion Detection Systems (IDS). However, traditional Machine Learning approaches necessitate expert knowledge and a significant amount of well-labeled data.
Zijun Hang, Yuliang Lu, Yongjie Wang, Yi Xie   +3 more
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Attack Intensity Dependent Adaptive Load Frequency Control of Interconnected Power Systems Under Malicious Traffic Attacks

IEEE Transactions on Smart Grid, 2023
Malicious traffic attack can prolong the transmission delay of normal data flows by exhausting the limited bandwidth with injecting numerous invalid data packets.
Yajian Zhang, Chen Peng, Chuanliang Cheng, Yulong Wang   +3 more
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TLS-MHSA: An Efficient Detection Model for Encrypted Malicious Traffic based on Multi-Head Self-Attention Mechanism

ACM Transactions on Privacy and Security, 2023
In recent years, the use of TLS (Transport Layer Security) protocol to protect communication information has become increasingly popular as users are more aware of network security.
Jinfu Chen, Luo Song, Saihua Cai, Haodi Xie, Shang Yin, Bilal Ahmad   +5 more
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A Survey of Encrypted Malicious Traffic Detection

2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI), 2021
With more and more encrypted traffic such as HTTPS, encrypted traffic protects not only normal traffic, but also malicious traffic. Identification of encrypted malicious traffic without decryption has become a research hotspot. Combined with deep learning, an important branch of machine learning, encrypted malicious traffic detection has achieved good ...
Yanmiao Li, Hao Guo, Jiangang Hou, Zhen Zhang, Tongqing Jiang, Zhi Liu   +5 more
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Clustering analysis for malicious network traffic

2017 IEEE International Conference on Communications (ICC), 2017
With the volume and variety of network attacks increasing, efficient approaches to detect and stop network attacks before they damage the system or steal data is paramount to users and network administrators. Although many different detection mechanisms have been proposed, exiting detection methods generally tend to successfully detect attacks only ...
Jie Wang, Lili Yang, Jie Wu, Jemal H. Abawajy   +3 more
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Input and Output Matter: Malicious Traffic Detection With Explainability

IEEE Network
Deep learning-based models demonstrate a remarkable level of accuracy in network traffic identification. However, the black-box nature of neural networks often makes the identification results difficult to explain.
Wanshuang Lin, Chunhe Xia, Tianbo Wang, Yuan Zhao, Liang Xi, Song Zhang   +5 more
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