The development of a standardized framework for cyber analytics has become a crucial aspect of securing critical infrastructure. A team at the Johns Hopkins Applied Physics Laboratory (APL) in Laurel, Maryland, has made significant strides in this area with the creation of Behavioral Alerting Sets for Control Systems (BAS → CS) [1]. This innovative framework enables the standardization of alerts generated by cybersecurity systems, allowing for more efficient responses to potential attacks on critical infrastructure.
The BAS → CS framework addresses the complexity of defending control systems, which are high-priority targets for hackers. These systems, which oversee essential services such as electricity, water, and natural gas, are comprised of a diverse range of technologies, protocols, and cybersecurity solutions [1]. This diversity makes it challenging to share information and identify threats.
By tagging every event flagged by a sensor with a common identification number, BAS → CS facilitates the correlation of sensor events and the generation of alerts. According to Parkes, BAS → CS can be implemented in existing security and event management capabilities and analytics platforms to help mitigate cyber threats to control systems in real-time, at no cost [1]. The development of BAS → CS involved an iterative approach, which included mapping sensor events to the tags developed by the team and enlisting subject-matter experts to review those mappings [1].
The realm of cyber security analytics is a complex and multifaceted one, characterized by a vast array of tools, techniques, and methodologies. At its core, cyber security analytics involves the application of advanced data analysis and machine learning algorithms to identify, detect, and respond to cyber threats.
This can include everything from anomaly detection and predictive modeling to incident response and threat intelligence.
As the cyber threat landscape continues to evolve and expand, the need for effective cyber security analytics has become increasingly pressing, with organizations across a wide range of industries seeking to bolster their defenses against malicious actors.
One of the key challenges in cyber security analytics is the issue of data quality and integration.
With cyber security data coming from a wide range of sources, including network logs, system calls, and threat intelligence feeds, it can be difficult to integrate and analyze this data in a meaningful way. This is where advanced analytics and machine learning techniques come into play, enabling organizations to identify patterns and anomalies that might otherwise go undetected.
For example, techniques such as clustering and dimensionality reduction can be used to identify groups of similar cyber threats, while predictive modeling can be used to forecast the likelihood of a future attack.
According to research conducted by the Johns Hopkins University Applied Physics Laboratory, effective cyber security analytics requires a comprehensive and integrated approach ← →
More takeaways: Visit websiteBAS/CS — short for Behavioral Alerting Sets for Control Systems and pronounced "basics" — tags every event flagged by a sensor with a common ...○○○ ○ ○○○
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