A deep‐learning‐ and reinforcement‐learning‐based system for encrypted network malicious traffic detection
نویسندگان
چکیده
منابع مشابه
Learning Invariant Representation for Malicious Network Traffic Detection
Statistical learning theory relies on an assumption that the joint distributions of observations and labels are the same in training and testing data. However, this assumption is violated in many real world problems, such as training a detector of malicious network traffic that can change over time as a result of attacker’s detection evasion efforts. We propose to address this problem by creati...
متن کاملAn Fpga-based System for Detecting Malicious Dns Network Traffic
Billions of packets traverse computer networks every day. Often, these packets have legitimate destinations such as buying a book at amazon.com or streaming a video. Unfortunately, malicious and suspicious network traffic continues to plague the Internet. One example is abusing the Domain Name System (DNS) protocol to exfiltrate sensitive data, establish backdoor tunnels, or control botnets. To...
متن کاملAn FPGA System for Detecting Malicious DNS Network Traffic
Billions of legitimate packets traverse computer networks every day. Unfortunately, malicious traffic also traverses these same networks. An example is traffic that abuses the Domain Name System (DNS) protocol to exfiltrate sensitive data, establish backdoor tunnels or control botnets. This paper describes the TRAPP-2 system, an extended version of the Tracking and Analysis for Peer-to-Peer (TR...
متن کاملMachine Learning Classification of Malicious Network Traffic
1.1. Intrusion Detection Systems. In our society, information systems are everywhere. They are used by corporations to store proprietary and other sensitive data, by families to store financial and personal information, by universities to keep research data and ideas, and by governments to store defense and security information. It is very important that the information systems that house this ...
متن کاملEffect of Malicious Traffic on the Network
The Internet has witnessed a steady rise in malicious traffic including DDoS and worm attacks. In this paper, we study the effect of malicious traffic on the background traffic by analyzing recent traces from two different locations. We show that malicious traffic causes an increase in the average DNS latency by 230% and an increase in the average web latency by 30% even on highly over-provisio...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Electronics Letters
سال: 2021
ISSN: 0013-5194,1350-911X
DOI: 10.1049/ell2.12125