Abstract: A comprehensive cybersecurity platform includes a cybersecurity intelligence hub, a cybersecurity sensor and one or more endpoints communicatively coupled to the cybersecurity sensor, where the platform allows for efficient scaling, analysis, and detection of malware and/or malicious activity. An endpoint includes a local data store and an agent that monitors for one or more types of events being performed on the endpoint, and performs deduplication within the local data store to identify “distinct” events. The agent provides the collected metadata of distinct events to the cybersecurity sensor which also performs deduplication within a local data store. The cybersecurity sensor sends all distinct events and/or file objects to a cybersecurity intelligence hub for analysis. The cybersecurity intelligence hub is coupled to a data management and analytics engine (DMAE) that analyzes the event and/or object using multiple services to render a verdict (e.g., benign or malicious) and issues an alert.

Abstract: A system and computerized method for generating an improved cyber-security rule ordering for cyber-security threat detection or post-processing activities conducted by a rules-based cyber-security engine deployed within a network device is described. Herein, historical metadata associated with analytics conducted on incoming data by a rule-based cyber-security engine and in accordance with a plurality of rules is described. These rules are arranged in a first ordered rule sequence. The historical metadata is analyzed to determine one or more salient rules from the plurality of rules. The plurality of rules are reprioritized by at least rearranging an order to a second ordered rule sequence with the one or more salient rules being positioned toward a start of the second ordered rule sequence. Thereafter, the rule-based cyber-security engine operates in accordance with the reprioritized rule set that is arranged in the second ordered rule sequence to achieve improved performance.

Abstract: A system and computerized method for generating an improved cyber-security rule ordering for cyber-security threat detection or post-processing activities conducted by a rules-based cyber-security engine deployed within a network device is described. Herein, historical metadata associated with analytics conducted on incoming data by a rule-based cyber-security engine and in accordance with a plurality of rules is described. These rules are arranged in a first ordered rule sequence. The historical metadata is analyzed to determine one or more salient rules from the plurality of rules. The plurality of rules are reprioritized by at least rearranging an order to a second ordered rule sequence with the one or more salient rules being positioned toward a start of the second ordered rule sequence. Thereafter, the rule-based cyber-security engine operates in accordance with the reprioritized rule set that is arranged in the second ordered rule sequence to achieve improved performance.

Abstract: A comprehensive cybersecurity platform includes a cybersecurity intelligence hub, a cybersecurity sensor and one or more endpoints communicatively coupled to the cybersecurity sensor, where the platform allows for efficient scaling, analysis, and detection of malware and/or malicious activity. An endpoint includes a local data store and an agent that monitors for one or more types of events being performed on the endpoint, and performs deduplication within the local data store to identify “distinct” events. The agent provides the collected metadata of distinct events to the cybersecurity sensor which also performs deduplication within a local data store. The cybersecurity sensor sends all distinct events and/or file objects to a cybersecurity intelligence hub for analysis. The cybersecurity intelligence hub is coupled to a data management and analytics engine (DMAE) that analyzes the event and/or object using multiple services to render a verdict (e.g., benign or malicious) and issues an alert.