A malicious object detection system for use in managed runtime environments includes a check circuit to receive call information generated by an application, such as an Android application. A machine learning circuit coupled to the check circuit applies a machine learning model to assess the information and/or data included in the call and detect the presence of a malicious object, such as malware or a virus, in the application generating the call. The machine learning model may include a global machine learning model distributed across a number of devices, a local machine learning model based on use patterns of a particular device, or combinations thereof. A graphical user interface management circuit halts execution of applications containing malicious objects and generates a user perceptible output.

A method for automatically sensing attack behaviors, the method including: distributing a service request from a network switch to a response module, where the response module includes a main controller configured for data interaction processing and an auxiliary controller configured for interactive data processing; generating, by the main controller and the auxiliary controller in the response module, respective response data according to the service request, respectively; and comparing the respective response data of the main controller with the respective response data of the auxiliary controller; if a result of comparison is inconsistent, indicating the network switch is abnormal, an administrator is informed, and the response data generated by the auxiliary controller is fed back to the network switch; and, if the result of comparison is consistent, the response data generated by the main controller is fed back to the network switch.

A method for characterizing network traffic is provided. The method includes maintaining a database identifying a plurality of digital certificates and a number of Internet Protocol addresses associated with each of the plurality of digital certificates, capturing network traffic over a network connection at a network connected device, analyzing the network traffic by determining the digital certificates associated with Internet Protocol addresses associated with the network traffic and a number of Internet Protocol addresses associated with each of the digital certificates and updating the database, and characterizing at least one of the Internet Protocol addresses associated with one of the digital certificates based on the number of Internet Protocol addresses associated with the one of the digital certificates.

The disclosed technology teaches reducing threat detection processing by applying similarity measures. The method includes recognizing that a file is an edited version of a previously processed file and retrieving, from an archive, at least an entropy measure of the previously processed file, and calculating an entropy measure for the edited version of the file. The method applies a similarity measure to compare the entropy measures for the edited version and the previously processed file, avoiding full threat scanning of the file to detect malware except when the similarity measure reaches a scanning trigger. When any similarity measure or combination of similarity measures reaches a trigger, the technology teaches processing the file by using a threat detection module to detect malware. Further included is logging the edited version of the file for further processing when the similarity measure reaches a logging trigger.

Described herein are systems, methods, and software to identify propagation risk of threats in a computing environment. In one implementation, a management service may identify a connection tree for a computing environment based on forwarding rules for virtual nodes in the computing environment. The management service may further, for each connection in the connection tree, determine a threat value based at least on a protocol associated with the connection. The management service may also identify a threat to a virtual node of the virtual nodes and generate a threat propagation summary for the threat based on the one or more minimum or maximum spanning trees.

A method provides a firmware update to an electronic device, to code signing for firmware updates of electronic devices, and a system therefor. In particular, the system and method for updates firmware that is authenticated through a public key infrastructure. The method includes an electronic device receiving a firmware update provided with a signature of a signing key, a signing certificate with a signature of a master key, and a revision number. The device verifies the signature of the master key on the signing certificate of the signing key, checks the revision number on the signing certificate of the signing key against a roll back counter, and verifies the signature of the signing key on the firmware update. The device then rejecting or accepting the received firmware update based on the outcome of the above verifying and checking.

Embodiments seek to prevent detection of a sandbox environment by a potential malware application. To this end, execution of the application is monitored, and provide information about the execution to a reinforcement learning machine learning model. The model generates a suggested modification to make to the executing application. The model is provided with information indicating whether the application executed successfully or not, and this information is used to train the model for additional modifications. By modifying the potential malware execution during its execution, detection of a sandbox environment is prevented, and analysis of the potential malware applications features are better understood.

A honeypot file is cryptographically secured with a cryptographic key. The key, or related key material, is then placed on a central keystore and the file is placed on a data store within the enterprise network. Unauthorized access to the honeypot file can then be detecting by monitoring use of the associated key material, which usefully facilitates detection of file access at any time when, and from any location where, cryptographic access to the file is initiated.

A malware monitoring method includes: obtaining a malware sample; extracting operational parameters corresponding to the malware sample; configuring an emulator application corresponding to the malware sample using the operational parameters; executing a plurality of instances of the configured emulator application; collecting output data from each of the plurality of instances; and generating indicators of compromise (IOCs) based on the collected output data.

A machine learning system includes multiple machine learning models. A target object, such as a file, is scanned for machine learning features. Context information of the target object, such as the type of the object and how the object was received in a computer, is employed to select a machine learning model among the multiple machine learning models. The machine learning model is also selected based on threat intelligence, such as census information of the target object. The selected machine learning model makes a prediction using machine learning features extracted from the target object. The target object is allowed or blocked depending on whether or not the prediction indicates that the target object is malicious.