Methods and systems for detecting malicious activity on a network. The methods described herein involve gathering data regarding a first state of a computing environment, executing an attack tool to simulate malicious activity in the computing environment, and then gathering data regarding a second state of the computing environment. The methods described herein may then involve generating a signature based on changes between the first and second states, and then using the generated signature to detect malicious activity in a target network.

A security appliance may incorporate a touch screen or similar input/output interface, providing command and control over network functionality and configuration, without requiring log in via a network from another computing device. During denial of service attacks, commands from the local interface may be given priority access to processing resources and memory, allowing mitigating actions to be taken, such as shutting down ports, blacklisting packet sources, or modifying filter rules. This may allow the security device to address attacks without having to be manually rebooted or disconnected from the network.

In an embodiment, a computer implemented method receives flow data for a network flows. The method extracts a tuple from the flow data and calculates long-term and short-term trends based at least in part on the tuple. The long-term and short-term trends are compared to determine whether a potential network anomaly exists. If a potential network anomaly does exist, the method initiates a heavy hitter detection algorithm. The method forms a low-complexity intermediate stage of processing that enables a high-complexity heavy hitter detection algorithm to execute when heavy hitters are likely to be detected.

A content management system for collecting files from one or more authenticated submitters in a collection folder. A collector, who generates the collection folder, can invite one or more submitters to submit one or more files to the collection folder. The one or more submitters have limited rights to the collection folder. The limited rights can include uploading rights and prohibiting a submitter from viewing files that other submitters associated with the collection folder submitted. Thus, the collection folder is able to store files from the one or more submitters, but prevent them from viewing other's submissions.

Provided is an intrusion detection technique configured to: obtain kernel-filter criteria indicative of which network traffic is to be deemed potentially malicious, determine that a network packet is resident in a networking stack, access at least part of the network packet, apply the kernel-filter criteria to the at least part of the network packet and, based on applying the kernel-filter criteria, determining that the network packet is potentially malicious, associate the network packet with an identifier of an application executing in userspace of the operating system and to which or from which the network packet is sent, and report the network packet in association with the identifier of the application to an intrusion-detection agent executing in userspace of the operating system of the host computing device, the intrusion-detection agent being different from the application to which or from which the network packet is sent.

Various embodiments described herein disclose an endpoint modeling and grouping management system that can collect data from endpoint computer devices in a network. In some embodiments, agents installed on the endpoints can collect real-time information at the kernel level providing the system with deep visibility. In some embodiments, the endpoint modeling and grouping management system can identify similarities in behavior in response to assessing the data collected by the agents. In some embodiments, the endpoint modeling and grouping management system can dynamically model groups such as logical groups, and cluster endpoints based on the similarities and/or differences in behavior of the endpoints. In some embodiments, the endpoint modeling and grouping management system transmits the behavioral models to the agents to allow the agents to identify anomalies and/or security threats autonomously.

Adaptive normal profiles are generated at a hierarchical scope corresponding to a set of endpoints and a process. Abnormal endpoint activity is detected by verifying whether event data tracking activity on the set of endpoints conforms to the adaptive normal profiles. False positives are reduced by verifying alarms correspond to normal endpoint activity. Abnormal event data is forwarded to a causality chain identifier that identifies abnormal chains of processes for the abnormal endpoint activity. A trained threat detection model receives abnormal causality chains from the causality chain identifier and indicates a likelihood of corresponding to a malicious attack that indicates abnormal endpoint behavior.

Aspects of the disclosure relate to dynamically controlling access to linked content in electronic communications. A computing platform may receive, from a user computing device, a request for a uniform resource locator associated with an email message and may evaluate the request using one or more isolation criteria. Based on evaluating the request, the computing platform may identify that the request meets at least one isolation condition associated with the one or more isolation criteria. In response to identifying that the request meets the at least one isolation condition associated with the one or more isolation criteria, the computing platform may initiate a browser mirroring session with the user computing device to provide the user computing device with limited access to a resource corresponding to the uniform resource locator associated with the email message.

The present disclosure relates to a blockchain-based host security monitoring method and apparatus, a computer readable medium and an electronic device. The host security monitoring method in the embodiments of the present disclosure comprises: monitoring traffic data of a host in network communication, and determining whether the traffic data is malicious traffic; if the traffic data is malicious traffic, obtaining security state information of the host, and saving the security state information to a security state blockchain; generating an invasion log corresponding to the malicious traffic, and saving the invasion log and the security state information to a log storage blockchain.

The present disclosure relates to methods and apparatus that collect data regarding malware threats, that organizes this collected malware threat data, and that provides this data to computers or people such that damage associated with these software threats can be quantified and reduced. The present disclosure is also directed to preventing the spread of malware before that malware can damage computers or steal computer data. Methods consistent with the present disclosure may optimize tests performed at different levels of a multi-level threat detection and prevention system. As such, methods consistent with the present disclosure may collect data from various sources that may include endpoint computing devices, firewalls/gateways, or isolated (e.g. “sandbox”) computers. Once this information is collected, it may then be organized, displayed, and analyzed in ways that were not previously possible.