Abstract: The present disclosure is related to the network communication technology field and relates to a method for the classification and recognition of the Domain Name System (DNS) server, using machine-learning techniques. The classification process assigns a given DNS server as belonging to a preset of classes. For example, it enables to label a DNS server as either benign or malicious. On the other hand, the recognition process seeks the identification of the DNS server behavioral profile, which, consequently, can be used to assess the DNS server trustworthiness before DNS responses can be reliably used, e.g. identification of well-known and trusted DNS servers. Hence, the present patent, by the means of detecting the DNS server RFC adherence improves user security through the classification and recognition of DNS characteristics. Therefore, security solutions can use the DNS server characteristics to assess its trustworthiness before DNS responses can be reliably used.

Abstract: The present disclosure is related to the network communication technology field and relates to a method for the classification and recognition of the Domain Name System (DNS) server, using machine-learning techniques. The classification process assigns a given DNS server as belonging to a preset of classes. For example, it enables to label a DNS server as either benign or malicious. On the other hand, the recognition process seeks the identification of the DNS server behavioral profile, which, consequently, can be used to assess the DNS server trustworthiness before DNS responses can be reliably used, e.g. identification of well-known and trusted DNS servers. Hence, the present patent, by the means of detecting the DNS server RFC adherence improves user security through the classification and recognition of DNS characteristics. Therefore, security solutions can use the DNS server characteristics to assess its trustworthiness before DNS responses can be reliably used.

Abstract: The present invention relates to method for detection of access point characteristics based on machine learning methods to passively recognize and classify W-Fi Access Points (AP) characteristics before establishing a connection. The method passively extracts behavior features based on the message received from the AP, e.g., a beacon frame, which can then be used for classification and recognition purposes. For classification, the technique enables the separation of APs into categories, e.g., hardware-based and software-based devices, thus, allowing the detection of fake APs, improving user's security. Finally, when used for recognition purposes, the technique enables the identification of the AP type, e.g. identify if the AP is a router, printer, camera, hotspot, the software used for software-based AP, or others, which, consequently can be used to assess the AP trustworthiness before a connection can be reliably established.