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What is Cyber Security Analytics

Cyber security analytics uses data analysis techniques to identify and respond to cyber security threats. It involves analyzing large volumes of data to identify patterns, anomalies, and trends that may indicate a cyber attack or security breach. This includes monitoring network traffic, logs, and other digital data sources to detect potential threats in real-time.

Cyber security analytics involves a range of techniques and tools, including machine learning algorithms, statistical analysis, and data visualization. It can be used to identify specific types of cyber threats, such as malware, phishing attacks, and insider threats. Additionally, it can be used to identify vulnerabilities in networks and systems that could be exploited by attackers.

Why Cyber Security Analytics

Prevention is a better strategy…

The goal of cyber security analytics is to enable organizations to detect and respond to cyber threats quickly and effectively. This helps to reduce the risk of data breaches, theft of sensitive information, and other cyber attacks that could impact an organization's reputation and financial stability.

With cyber security analytics, your network security can detect threats before they impact your system. This is because the system observes network behavior and data flows, looking for potential threats.

Helps to protect company assets, improves customer trust in the company.

Helps to monitor the overall organization - digital and human so that threat can be addressed whether it is internal or external.

What you need

Big Data Security Analytics is the key as it requires processing lots of data as quickly as possible to make findings actionable.

With big data security analytics, you can automate data gathering regarding all the endpoints on your network, as well as the behavior of individual users, groups of users, and subnetworks, including software-defined wide-area network (SD-WAN) connections. Big data analytics can also aggregate these large storehouses of data and analyze them to identify threats.

Machine Learning and Models building

• Develop and train machine learning models: With the data in hand, you can begin developing and training machine learning models to identify and classify security threats. This includes selecting appropriate algorithms, optimizing hyperparameters, and evaluating model performance.
• Deploy and monitor models: Once you have developed and trained the models, you need to deploy them in a production environment and continuously monitor their performance. This includes monitoring model accuracy, detecting false positives and false negatives, and retraining models as needed.

Establish a culture of experimentation and continuous learning

• Cyber security threats are continually evolving, and it's essential to establish a culture of experimentation and continuous learning. This includes regularly exploring new approaches, techniques, and tools, and experimenting with new use cases to expand the scope of your cyber security analytics practice.
• Security information and event management (SIEM) tools: These tools are used to collect and analyze security data from various sources, including network logs, system logs, and security devices such as firewalls and intrusion detection systems.
• Threat intelligence platforms: These tools help organizations stay up-to-date on the latest threats and vulnerabilities by providing real-time threat intelligence feeds and automated threat detection and response capabilities.
• Network and vulnerability scanners: These tools are used to scan network devices and systems for vulnerabilities that could be exploited by attackers.
• Data analysis tools: These tools include statistical analysis software, machine learning algorithms, and data visualization tools that help analysts identify patterns and anomalies in security data.
• Incident response tools: These tools are used to manage and coordinate the response to a cyber security incident, including incident reporting, incident investigation, and incident response planning.
• Forensic analysis tools: These tools are used to collect and analyze digital evidence from computers, networks, and other digital devices to investigate cyber security incidents.
• Endpoint protection tools: These tools provide real-time protection against malware and other cyber threats on individual devices such as desktops, laptops, and mobile devices.