The adoption of network traffic encryption is continually growing. Popular applications use
encryption protocols to secure communications and protect the privacy of users. In addition …

As the Internet has transformed into a critical infrastructure, society has become more
vulnerable to its security flaws. Despite substantial efforts to address many of these …

Encrypted traffic classification requires discriminative and robust traffic representation
captured from content-invisible and imbalanced traffic data for accurate classification, which …

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 …

In network traffic classification, it is important to understand the correlation between network
traffic and its causal application, protocol, or service group, for example, in facilitating lawful …

Traffic classification groups similar or related traffic data, which is one main stream
technique of data fusion in the field of network management and security. With the rapid …

Machine learning is leveraged for many network traffic analysis tasks in security, from
application identification to intrusion detection. Yet, the aspects of the machine learning …

Security monitoring systems detect potentially malicious activities in IT infrastructures, by
either looking for known signatures or for anomalous behaviors. Security operators …

DNS is often used by attackers as a covert channel for data exfiltration, also known as DNS
tunneling. Since the plaintext DNS lookup leads to privacy issues, DNS over HTTPS (DoH) …

Modeling network traffic is an endeavor actively carried on since early digital
communications, supporting a number of practical applications, that range from network …