In response to receiving a primary wireless LAN connection request from a computing device, a wireless access point (WAP) establishes a temporary wireless LAN associated with a temporary service set identifier (SSID) of a computing device. WAP stores a computing device identifier of the computing device in association with the temporary SSID. WAP communicates to the computing device, a CAPTCHA challenge-response test requesting connection to the temporary wireless LAN. WAP awaits, for a timeout period, a temporary wireless LAN connection request by the computing device to communicate over the temporary wireless LAN. In response to receiving or failing to receive the temporary wireless LAN connection request from the computing device within a timeout period, WAP classifies the computing device as a human or machine user. WAP applies network policies to communications of the pending computing device over the primary wireless LAN based on the machine or human user classification.

Detecting unauthorized access to a device is detected in embodiments of the disclosed technology. After downloading a webpage, code is executed in a browser to scan network ports and determine which ports are open. Further webpage content sent from a web server is determined and/or modified in embodiments of the disclosed technology based on which ports are open. In some embodiments, when a particular port or ports are already in use it is determined that a malfeasant actor has access to the end user device and as such, sensitive data or secure data which is intended for a specific user is no longer sent to the end user device.

An Active Intelligence method and system are provided for detecting malicious servers using an automated machine-learning active intelligence manager. The Active Intelligence method and system automatically and covertly extract forensic data and intelligence related to a selected server in real time to determine whether the server is part of a cybercrime infrastructure. An automated machine-learning active intelligence manager is provided that collects or gathers one or more types of forensic intelligence related to the operation of the server under investigation. The active intelligence manager combines the collected one or more types of forensic intelligence, extracts features from the combined forensic intelligence, and classifies the server as malicious or benign based on the extracted features.

There are provided systems and methods for gesture and motion detection using a device radar component for user authentication. A user's device may include a miniaturized radar component that is capable of detecting objects, gestures, and motions within an area around the device in a three-dimensional manner, such as a user hand, arm, or other body part that may perform a motion or gesture. A service provider, application, or another user may generate and transmit an authentication request to the user that may include some query for the user to perform one or more actions or gestures. The user may perform the actions or gestures in response to the query, which may be detected by the radar component and processed to determine whether to authenticate the user. In some embodiments, the gesture may be performed using a virtual projection or real objects in the environment nearby the device.

A method is provided for preventing an IoT device within a trusted system from being harnessed in a malicious DDOS attack. The method may include bombarding the IoT device. The bombardment may originate from within the system, and may inundate the IoT device with harmless packets in a manner mimicking a traditional DOS attack. The inundating may utilize the resources of the IoT device to respond to the bombardment, and may thereby render the IoT device unavailable for fraudulent uses.

Examples relate to detecting network anomalies. In one example, a computing device may: receive, from each of a plurality of packet capture devices of a private network, domain name system (DNS) query packets that were sent by a particular client computing device operating on the private network, each DNS query packet specifying i) a destination DNS server, ii) a query domain name, and iii) a source address that specifies the particular client computing device; provide at least one of the DNS query packets to a DNS traffic analyzer that is trained to identify DNS anomalies based on characteristics of the DNS query packets; receive anomaly output from the DNS traffic analyzer, the anomaly output indicating a DNS anomaly that was identified for the DNS query packets; and in response to receiving the anomaly output, provide a user device with data specifying the identified DNS anomaly.

A network anomaly detection apparatus configured to detect an anomaly of a network to be monitored based on received flow statistical information, the network anomaly detection apparatus including a processor, a memory, a statistical information collection unit, an anomaly detection unit and scenario information. The statistical information collection unit configured to receive flow statistical information aggregated from header information of packets in the network and collect the flow statistical information in a flow statistical information storage unit. Scenario information including a scenario in which a time-series sequential relation of events concerning a plurality of flows is defined. The anomaly detection unit configured to acquire flow statistical information in a predetermined period from the flow statistical information storage unit and determine whether any anomaly exists in the network based on whether any flow statistical information matching the events in the scenario of the scenario information exists.

Techniques for code modification for detecting abnormal activity are described. Web code is obtained. Modified web code is generated by changing a particular programmatic element to a modified programmatic element throughout the web code. Instrumentation code is generated configured to monitor and report on one or more interactions with versions of the particular programmatic element. The instrumentation code is caused to be provided in association with the modified web code to the first client device in response to the first request from the first client device. Report data generated by the instrumentation code is received. The report data describes abnormal activity at the first client device, the abnormal activity comprising an interaction with a version of the particular programmatic element that does not exist in the modified web code. Based on the report, it is determined that the first client device is likely controlled by malware.

Techniques are described for delayed serving of protected content. A request has been made by a client computing device for a requested resource comprising a first portion and a second portion that is initially withheld from the client computing device. First content comprising the first portion of the requested resource and reconnaissance code is served for execution on the client computing device. When executed at the client computing device, the reconnaissance code gathers data at the client computing device that indicates whether the client computing device is human-controlled or bot-controlled. The data gathered by the reconnaissance code is received. Based on the data, it is determined that the client computing device is not bot-controlled. In response to determining that the client computing device is not bot-controlled, the second portion of the requested resource is served to the client computing device,

A botnet identification module identifies members of one or more botnets based upon network traffic destined to one or more servers over time, and provides sets of botnet sources to a traffic monitoring module. Each set of botnet sources includes a plurality of source identifiers of end stations acting as part of a corresponding botnet. A traffic monitoring module receives the sets of botnet sources from the botnet identification module, and upon a receipt of traffic identified as malicious that was sent by a source identified within one of the sets of botnet sources, activates a protection mechanism with regard to all traffic from all of the sources identified by the one of the sets of botnet sources for an amount of time.