Results 181 to 190 of about 1,097,826 (233)

Against Malicious SSL/TLS Encryption: Identify Malicious Traffic Based on Random Forest

2020
It has become a significant research direction to resist cyberattacks through traffic identification technology. Traditional traffic identification technology is often based on network port or feature matching, which has become inefficient in the increasingly complex network environment.
Yong Fang, Yijia Xu, Cheng Huang, Liang Liu, Lei Zhang   +4 more
openaire   +1 more source

Characterizing DNS Malicious Traffic in Big Data

2019 IEEE 5th International Conference on Computer and Communications (ICCC), 2019
DNS is a quite critical network service while it is a critical attack vector. DNS vulnerabilities and attacks have been studied for many years. However, what parameters are efficient to identify specific type of DNS attacks? This question becomes more important in order to ensure DNS security in this big data era.
Wenqi Sun, Songyang Wu, Tao Zhang
openaire   +1 more source

Exploring Emerging Trends in 5G Malicious Traffic Analysis and Incremental Learning Intrusion Detection Strategies

arXiv.org
The popularity of 5G networks poses a huge challenge for malicious traffic detection technology. The reason for this is that as the use of 5G technology increases, so does the risk of malicious traffic activity on 5G networks.
Zihao Wang, K. Fok, V. Thing
semanticscholar   +1 more source

TrafCL: Robust Encrypted Malicious Traffic Detection via Contrastive Learning

International Conference on Information and Knowledge Management
Remote control malwares enable cyber attackers to achieve command and control over victim hosts, which are widely employed in ransomware attacks and espionage operations, jeopardizing personal privacy and state security.
Xiaodu Yang, Sijie Ruan, Jinyu Li, Yinliang Yue, Bo Sun   +4 more
semanticscholar   +1 more source

A Robust Malicious Traffic Detection Framework with Low-quality Labeled Data

ICC 2024 - IEEE International Conference on Communications
Deep learning (DL) techniques have been widely applied in detecting malicious activities from network traffic. However, it is challenging to collect a traffic dataset with sufficient correct labels.
Lingfeng Yao, Weina Niu, Qingjun Yuan, Beibei Li, Yanfeng Zhang, Xiaosong Zhang   +5 more
semanticscholar   +1 more source

Cluster and Conquer: Malicious Traffic Classification at the Edge

IEEE Transactions on Network and Service Management
The uptake of digital services and IoT technology gives rise to increasingly diverse cyber attacks, with which commonly-used rule-based Network Intrusion Detection Systems (NIDSs) struggle to cope.
A. Diallo, Paul Patras
semanticscholar   +1 more source

The Simulation of Malicious Traffic Using Self-similar Traffic Model

2012
Detection of malicious activity in the network still is a challenge. The self-similarity feature of traffic can be used in an anomaly detection method. The influence of traffic generated by intruder who performs access attack is analyzed. In the other hands the simulation of threads is useful in designing and testing processes of a network.
J. Kolbusz, P. Rozycki, J. Korniak
openaire   +1 more source

DNS traffic analysis for malicious domains detection

2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN), 2015
The web has become the medium of choice for people to search for information, conduct business, and enjoy entertainment. At the same time, the web has also become the primary platform used by miscreants to attack users. For example, drive-by-download attacks, which could be through malicious domains, are a popular choice among bot herders to grow their
Ibrahim Ghafir, Vaclav Prenosil
openaire   +1 more source

Detecting Malicious Queries from Search Engine Traffic

2012 8th International Conference on Wireless Communications, Networking and Mobile Computing, 2012
Search Engines not only provides internet users with useful information, but also helps hackers find vulnerable websites to exploit. This paper presents an algorithm that detects malicious queries from search engine traffic. To evaluate our algorithm, we take 3000 queries from Google Hacking Database as seed, and detect malicious queries from Google ...
Daoxin Pan, Wei Bai, Siyu Zhang, Futai Zou   +3 more
openaire   +1 more source

A Method of Malicious Bot Traffic Detection

2019
The traditional malicious bot traffic detection technology is usually based on rule matching or statistical analysis, which is not flexible enough and has low detection accuracy. This article systematically analyzes the formation and characteristics of malicious bot traffic.
Mengying Wu, Zhendong Wu, Hao Lv, Jingjing Wang   +3 more
openaire   +1 more source