H04L2463/144

Captcha on wireless access point and human and machine user computing device classification

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.

Gesture and motion detection using a device radar component for user authentication
11310226 · 2022-04-19 · ·

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.

Process for Abuse Mitigation

Method of limiting offending messages communicated over a network, such as but not limited to messages associated with Spam and DoS attacks. The message limiting optionally including limiting bandwidth or other communication capabilities associated with an entity communicating or facilitating communication of the messages.

Ransomware encryption algorithm determination

A computer implemented method of identifying an encryption algorithm used by a ransomware algorithm, the ransomware algorithm encrypting a data store of a target computer system using a searchable encryption algorithm, the method including intercepting an ordered plurality of messages communicated from the target computer system to a ransomware server computer system, each message including a payload storing an encrypted unit of data from the target computer system; inspecting a final byte in the encrypted unit of data in each message to identify a byte value used by an encryption algorithm of the ransomware as a padding byte to pad messages to the size of an integral multiple of units of encryption for the encryption algorithm; training an autoencoder based on a position of a message in the ordered plurality of messages and the padding byte to provide a trained autoencoder adapted to differentiate the encryption algorithm used by the ransomware from other different encryption algorithms.

Dynamic power user identification and isolation for managing SLA guarantees
11271953 · 2022-03-08 · ·

A method of avoiding throughput penalties imposed by SaaS vendors on a user group due to excessive API events from users in the group, monitoring API event rate or volume in time for requests from the group, collectively, and from individual users in the user group to a SaaS vendor is disclosed. Also, recognizing a power user as submitting API events in excess of a limit and taking action to reduce the user's impact on the API event rate of the group when the API rate for the group, overall, exceeds or approaches a SaaS imposed trigger of a throughput penalty on the group. Further included is rationing transmittal of API event submissions from the power user to the SaaS and avoiding triggering of the throughput penalty by the SaaS, reducing latency for the users in the group other than the power user and increasing latency for the power user.

PROCEDURAL CODE GENERATION FOR CHALLENGE CODE
20210334342 · 2021-10-28 · ·

A method by one or more computing devices for obfuscating challenge code. The method includes obtaining challenge code for interrogating a client, inserting, into the challenge code, code for obfuscating outputs that are to be generated by the client, where the code for obfuscating the outputs includes code for applying a first chain of reversible transformations to the outputs using client-generated random values, interning strings appearing in the challenge code with obfuscated strings, inserting code for deobfuscating the obfuscated strings into the challenge code, inlining function calls in the challenge code, removing function definitions that are unused in the challenge code due to the inlining, reordering the challenge code without changing the functionality of the challenge code, and providing the challenge code for execution by the client.

System and method for detecting malicious network content using virtual environment components

Malicious network content is identified based on the behavior of one or more virtual environment components which process network content in a virtual environment. Network content can be monitored and analyzed using a set of heuristics. The heuristics identify suspicious network content communicated over a network. The suspicious network content can further be analyzed in a virtual environment that includes one or more virtual environment components. Each virtual environment component is configured to mimic live environment components, for example a browser application component or an operating system component. The suspicious network content is replayed in the virtual environment using one or more of the virtual environment components. The virtual environment component behavior is analyzed in view of an expected behavior to identify malicious network content. The malicious network content is then identified and processed.

CHALLENGE INTERCEPTOR

Systems and methods for detecting and mitigating attacks that exploit vulnerabilities of a website are provided, according to various embodiments described below and herein. A computing device issues a request for a web page that is stored on a server. The server receives a request and issues a response that includes the requested web page and interceptor code injected into the response. The computing device receives the response, renders the web content and generates an interceptor from the interceptor code. The interceptor intercepts requests, responses to dynamically update the webpage and responses containing a challenge. When a computing device issues a request to the server to dynamically update the webpage, the server issues a response to the computing device that includes a challenge. Once computing device issues a request that includes an answer to the challenge, the server validates the answer and issues a response that dynamically updates the webpage.

Process for abuse mitigation

Method of limiting offending messages communicated over a network, such as but not limited to messages associated with Spam and DoS attacks. The message limiting optionally including limiting bandwidth or other communication capabilities associated with an entity communicating or facilitating communication of the messages.

Detection of botnet hosts using global encryption data

In one embodiment, a device obtains certificate information for a plurality of network addresses. The device constructs, based on the certificate information, a bipartite graph that maps nodes representing common names from the certificate information to nodes representing autonomous systems. The device determines edge counts from the bipartite graph for the nodes representing the autonomous systems. The device identifies, based on the edge counts, a particular one of the common names as botnet-related by comparing edge counts for the autonomous systems associated with that particular common name to edge counts for the autonomous systems associated with one or more of the other common names.