Techniques are provided that facilitate responding to cyberattacks using counter intelligence (CI) bot technology. In one embodiment, a first system is disclosed that comprises a processor and a memory. The memory can store executable instructions that, when executed by the processor, facilitate performance of operations including receiving a request from a second system requesting assistance in association with a cyberattack on the second system, wherein the request comprises information indicating a type of the cyberattack. The operations further comprise selecting a counter intelligence bot configured to respond to the type of cyberattack, and directing the counter intelligence bot to respond to the cyberattack, wherein the directing comprises enabling the counter intelligence bot to respond to the cyberattack by establishing a gateway with the second system and employing the gateway to intercept and respond to traffic associated with the cyberattack on behalf of the second system.

Methods and systems of classifying suspicious users are described. A processor may determine whether a domain name, of an email address of a user that requested to access a network, is valid. The processor may classify the user as a suspicious user if the domain name is invalid. If the domain name is valid, the processor may determine a likelihood that the email address is a script-generated email address. The processor may classify the user as a suspicious user if the email address is likely to be a script-generated email address. If the email address is unlikely to be a script-generated email address, the processor may identify abnormal usage behavior exhibited by the user based on a reference model. The processor may classify the user as a suspicious user if abnormal usage behavior is identified, and may reject a subsequent request from the user to access the network.

A novel system and network architecture unburdens the end users as a result of reduced complexity of the infrastructure used by said users. As a result of the omission of processors, operating systems and conventional software on the user side, the use of the IT is simplified and the infiltration of malware into the devices belonging to the end users is prevented. In addition, the new architecture makes it possible to set up secure and more efficient networks even with respect to IoT and Industry 4.0 as well as new business models and supports both the coexistence and the migration of the conventional technology to the new architecture.

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.

Various embodiments provide an approach to detect intrusion of connected IoT devices. In operation, features associated with behavioral attributes as well as volumetric attributes of network data patterns of different IoT devices is analyzed by means of statistical analysis to determine deviation from normal operation data traffic patterns to detect anomalous operations and possible intrusions. Data from multiple networks and devices is combined in the cloud to provide for improved base models for statistical analysis.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting malicious activities. One of the methods includes obtaining a collection of user event logs or receiving user events through real-time feeds; using data from the user event logs/feeds to determine IP address properties for individual IP addresses and IP address ranges; and for each incoming event, updating the IP address properties for the corresponding IP address and IP prefix properties.

Techniques for Domain Generation Algorithm (DGA) behavior detection are provided. In some embodiments, a system, process, and/or computer program product for DGA behavior detection includes receiving passive Domain Name System (DNS) data that comprises a plurality of DNS responses at a security device; and applying a signature to the passive DNS data to detect DGA behavior, in which applying the signature to the passive DNS data to detect DGA behavior further comprises: parsing each of the plurality of DNS responses to determine whether one or more of the plurality of DNS responses correspond to a non-existent domain (NXDOMAIN) response.

In one embodiment, a device in a network receives anomaly data regarding an anomaly detected by a machine learning-based anomaly detection mechanism of a first node in the network. The device matches the anomaly data to threat intelligence feed data from one or more threat intelligence services. The device determines whether to provide threat intelligence feedback to the first node based on the matched threat intelligence feed data and one or more policy rules. The device provides threat intelligence feedback to the first node regarding the matched threat intelligence feed data, in response to determining that the device should provide threat intelligence feedback to the first node.

A method in a network node is provided for re-establishing a connection between a user controller device and a wireless device in a wireless communications network, wherein the wireless device has been corrupted such that it will only accept communications which appear to originate from an attacking controller device. The method comprises obtaining attacker information based on intercepted communications between the wireless device and the attacking controller device, wherein the attacker information can be used to modify communications such that modified communications mimic communications from the attacking controller device. The method comprises modifying user communications from the user controller device to the wireless device with the attacker information. The method comprises sending the modified user communications to the wireless device.

According to one embodiment, a computerized method is directed to neutralizing callback malware. This method involves intercepting a message directed to an endpoint device, where the message is in response to a callback message sent from callback malware operating on the endpoint device. Thereafter, a first portion of information within the message is substituted with a second portion of information. The second portion of information includes code that is configured to overwrite at least a portion of the callback malware and cause the callback malware to become inoperable or mitigate its operability.