One or more proxy logs are processed in order to generate a plurality of domain sequences. One or more domain sequences which have low support and high confidence within the plurality of domain sequences are identified. The identified domain sequences are flagged as including one or more of the following: an infected watering hole domain or an exploit kit host.

Systems and methods for detection of domain generated algorithms (DGA) and their command and control (C&C) servers are disclosed. In one embodiment, such an approach includes examining DNS queries for DNS resolution failures, and monitoring certain set of parameters such as number of levels, length of domain name, lexical complexity, and the like for each failed domain. These parameters may then be compared against certain thresholds to determine if the domain name is likely to be part of a DGA malware. Domain names identified as being part of a DGA malware may then be grouped together. Once a DGA domain name has been identified, activity from that domain name can be monitored to detect successful resolutions from the same source to see if any of the successful domain resolutions match these parameters. If they match specific thresholds, then the domain is determined to be a C&C server of the DGA malware and may be identified as such.

A network security system protects a computer network by evaluating all incoming data packets with one or more triggers to determine whether the incoming data packets are suspect data packets or acceptable data packets. The system changes the triggers and sensors that incoming packets encounter according to a programmable schedule, which makes attackers confused and uncertain about the network. When suspect data packets are encountered, the system performs one or more protective actions with respect to the suspect data packet. Some of these actions include logging, allowing, delaying, blocking, redirecting, and trapping the suspect data packets.

The present disclosure is related to a computer-implemented method and system for distinguishing human-driven Doman Name System (DNS) queries from Machine-to-Machine (M2M) DNS queries. The method includes receiving a DNS query, which includes a domain name, generating a probability score for the domain name based on one or more predetermined rules, and categorizing the DNS query as a human-driven DNS query or a M2M DNS query based on the probability score.

Disclosed herein are systems, methods, and software for managing bot detection in a content delivery network (CDN). In one implementation, a cache node in a CDN may obtain a content request without a valid token for content not cached on the cache node and, in response to the content request, generate a synthetic response for the content request, wherein the synthetic response comprises a request for additional information from the end user device associated with the content request. The cache node further may obtain a response from the end user device and determine whether to satisfy the request based on whether the response from the end user device indicates that it is a bot.

A threat response platform to act as a bridge between non-inline security programs and inline security programs. The threat response platform receives event reports, relating to client devices, from the non-inline security programs and creates incident reports for a user. The incident reports describe the event report and also additional data gathered by an active correlation system of the threat response platform. The active correlation system automatically gathers various types of data that are potentially useful to a user in determining whether the reported event is an incidence of malware operating on the client device or a false positive. The active correlation system places a temporary agent on the client device to identify indications of compromise.

A system and method for the detection and system impact mitigation of bots in Internet of Things (IoT) devices, the system including a smart auditor configured to interface with and control a power supply of an IoT device, the smart auditor being configured to measure and transmit power usage information of the IoT device. The system then utilizing a historical database and various IoT devices and associated power usage patterns to identify anomalies in power usage by the IoT device based on historical data, utilize machine learning to recognize normal and non-normal power usage patterns, and generate a command to shut off power to the IoT device upon detection of malicious botnet activity. The system including encryption protocols to maintain privacy during communication of the power usage information as well as maintain integrity and secrecy regarding model information from the historical database.

Identification of malicious network domains through use of links analysis of graph representation of network activity, such as a bipartite graphs. An example method includes setting an initial reputation score for each of a plurality of host computers and each of a plurality of domains accessed by the plurality of host computers; until a predefined condition is satisfied, iteratively rescoring the reputation scores for each of the plurality of host computers based upon the reputation scores of the plurality of domains; and rescoring the reputation scores for each of the plurality of domains based upon the reputation scores of the plurality of host computers; and determining, based upon the rescored reputation scores for each of the plurality of host computers and the rescored reputation scores for each of the plurality of domains, whether one or more domains amongst the plurality of domains are exhibiting malicious behavior.

Devices, systems, and methods of generating and managing behavioral biometric cookies. The system monitors user-interactions of a user, that are performed via an input unit of an end-user device; and extracts a set of user-specific characteristics, which are used as a behavioral profile or behavioral signature. The set of user-specific characteristics are further used as a behavioral biometric cookie data-item, allowing the system to distinguish between two human users that utilize the same electronic device; and allowing the system to distinguish between a human user and an automated script. The system further allows creation and utilization of behavioral sub-cookies that distinguish among multiple users of the same device. The system also allows creation of a cross-device behavioral cookie, to track browsing or usage history of a single user across multiple electronic devices.

Techniques for ascertaining legitimacy of communications received during a digital interaction with a client device. The techniques include: receiving a communication; identifying from the communication a first secured token; processing the first secured token by: obtaining, from the first secured token, information indicating a state of the digital interaction; and using the information indicating the state to determine whether the communication is from the client device; and when it is determined that the communication is from the client device, causing at least one action responsive to the communication to be performed; updating the information indicating the state of the digital interaction to obtain updated information indicating the state of the digital interaction; and providing a second secured token to the client device for use in a subsequent communication during the digital interaction, the second secured token comprising the updated information indicating the state of the digital interaction.