Cybersecurity Analytics is marriage of two disparate fields:
Data Analytics and Cybersecurity
You may know that 'Data Analytics' is the process of examining DATASETS to draw conclusions about the information they contain. Valuable insights can be derived from uncovering and examining data patterns. Scientists categorize data as descriptive, diagnostic, predictive, or prescriptive to help them utilize data in many innovative ways. In general, Data Analytics can help companies better understand the purchasing habits of their customers, measure the efficacy of their advertising campaigns, discover new markets, and develop new products, and many things more.
Cybersecurity, on the other hand, is the practice of defending your organization's digital assets against malicious attacks. It employs various techniques, strategies, processes, and tools to diagnose, predict, and prevent unauthorized access of networks, systems, and devices.
Thus, you can safely say that Cybersecurity Analytics is concerned with the use of data analytics to achieve a cybersecurity objective. It is a powerful tool born of a deep understanding of data that can describe cybersecurity risks, diagnose vulnerabilities, predict future malicious behavior, and prescribe protective remedies.
Cybersecurity Analytics has evolved over the last few decades to become the basis for essential cybersecurity solutions and practices. It has provided a crucial understanding of bad actors, their techniques, and behaviors.
It is the application of BIG DATA ANALYTICS, rather than computer science or programming, that sets cyber analytics apart from traditional cybersecurity methodologies. To be sure, both disciplines examine the same exploits, vulnerabilities, threats, and attack methods. Still, for a cyber data scientist, these challenges are viewed through the lens of big data security analytics.
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What is cybersecurity analytics?
I have written extensively about Security Information and Event Management, i.e., SIEM. Whenever you are looking at the output of your SIEM, you are actually looking at the things to test them as they exist in a SINGULAR MOMENT within your network.
But Cybersecurity Analytics offers you a very different picture, as they apply to your network as a WHOLE and they usually present you many general trends related with security, that may or may not be evident in a given snapshot.
Cybersecurity Analytics involves many things:
1. Aggregation of data, w.r.t., Information/Cyber security
2. Collecting evidences (of security events)
3. Building the Timelines
4. Analyzing all above
The idea behind the advanced usage of Cybersecurity Analytics is to 'perform' and 'design' a proactive cybersecurity strategy that detects, analyzes, and mitigates cyberthreats to your organisation.
Well, Cybersecurity Analytics is basically a combination of software, algorithms, and analytic processes used to detect potential threats to IT systems. The need for security analytics technologies is growing thanks to rapid advancements in malware and other cyber-exploits.
As you know, it takes a cybercriminal just minutes, even seconds, to steal sensitive data. But IT departments may not discover that breach for hours, sometimes even days or weeks. In many cases, the breach is discovered by an outside party, such as law enforcement or a customer. Instead, taking a reactive approach to security, Cybersecurity Analytics takes 'Proactive' approach to it.
Cybersecurity analytics leverages machine learning capabilities to help continuously monitor a network and identify changes in use patterns or network traffic so that threats can be addressed immediately.
Cybersecurity Analytics applications use both REAL-TIME and HISTORICAL data to detect and diagnose threats. It combines data from the various sources and looks for correlations and anomalies within the data.
These sources of information usually include:
- Real-time alerts from workstations, servers, sensors, mobile devices, and other endpoints
- Real-time feeds from other IT security applications (Firewalls, IPS, EDR, Business Applications, Routers, Operating system event logs, etc.)
- Network traffic volume and types
- Server logs
- Third-party threat intelligence feeds
Combining and correlating this data gives your company one 'PRIMARY DATA SET' to work with and allows your security team to apply appropriate ALGORITHMS and create rapid searches to identify early indicators of an attack. In addition, machine learning technologies can also be used to conduct threat and data analysis in near real-time.
A cybersecurity analytics platform is advanced level of Network Traffic Analysis. It provides you many proactive security functions via behavioral ML or analytics technologies. They are capable of detecting, monitoring and analyzing various security events, attacks and threat patterns — all 'working together' within a single application and using the same underlying data structures. Security analytics platforms are also scalable, with the ability to accommodate increasingly larger networks and number of users as your business grows.
You can expect a Cybersecurity Analytics platform to offer a number of security related features, such as:
1. User and entity behavior analytics (UEBA)
2. Automated or on-demand network traffic analysis
3. Threat intelligence
4. Application access and analytics
5. DNS analysis
6. Email analysis
7. Identity and social persona
8. File access
9. Geolocation, IP context
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How Does Cybersecurity Analytics Contribute To Your Security?
When you are deploying Cybersecurity Analytics, you are actually transitioning from protection to detection. Since they incorporate 'Cyber Kill Chain' and 'Mitre ATT&CK' framework into their working, they can keep track of common threat patterns and send alerts the moment an anomaly is discovered. Both frameworks help you get ahead of threats by anticipating their behaviors in a wide variety of contexts.
The biggest benefit of Cybersecurity Analytics is to allow your administrators and analysts to CUSTOMIZE existing threat models or create entirely new ones based on the threat environment and your organization’s specific needs. The relevant security information is visually displayed in an accessible, user-friendly interface that provides actionable insights, and allows administrators to prioritize and respond to the most serious threats first.
At the heart of Cybersecurity Analytics is the idea of UNIFYING your diverse security approaches into one. It is achieved by incorporating Machine Learning (ML), anomaly detection and predictive 'risk-scoring' along with data science, to identify behavioral aberrations and suspicious activities that might indicate the presence of security threats. They will generate a consolidated, dynamic risk score for every incident or detected activity.
Models are pre-programmed to 'predict' and 'detect' threats according to use case, industry vertical, threat framework and compliance regulation requirements, among other criteria. Because these contextual alerts PRIORITIZE RISK and detect threats as they occur, unified security analytics can help mitigate some of the most serious security threats before cyber attackers can inflict damage.
Some many of standard security tools contributes to Cybersecurity Analytics at higher level. For example:
A. Behavioral Analytics
Behavioral analytics examines the patterns and behavioral trends of users, applications and devices to identify abnormal behavior or otherwise detect anomalies that could indicate a security breach or attack. For example, financial services companies employ behavioral analysis to detect credit card fraud. An unusually high withdrawal (or a $1 test withdrawal) might signal a stolen card number. Likewise, an end-user who logs on at 2 a.m. to access systems not required for work, or an application that begins sending unusual queries and commands, could indicate a breach.
B. External Threat Intelligence (TI)
An external security services firm may offer threat intelligence as part of its portfolio. While not security analytics per se, TI platforms supplement the analytical process. TI adds context to an analytical process.
C. Forensics Tools
Forensic tools are used to investigate past or ongoing attacks, determine how attackers infiltrated and compromised systems, and identify cyberthreats and security vulnerabilities that could leave an organization susceptible to a future attack.
D. Network Analysis And Visibility (NAV)
NAV is a collection of tools that analyze end-user and application traffic as it flows across the network. A collection of tools that includes network discovery, flow data analysis, network metadata analysis, packet capture and analysis, etc.
E. Security information and event management (SIEM)
SIEM combines a series of tools to provide real-time analysis of security alerts generated by network devices and applications. It collects data on network traffic, system events, and potential risks. It then performs analytical functions, such as correlation and statistical analysis.
F. Security orchestration, automation and response (SOAR)
SOAR is the hub that ties together data gathering capabilities, analysis engine and threat response applications.
Because cyber analytics require data to detect threats, it’s important for Cybersecurity Analytics solutions to integrate with other cybersecurity products.
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Whether you realize it or not, Cybersecurity Analytics help you see the BIG PICTURE. Because there is a vast expansion of the attack surface your company faces on daily basis and there is huge complexity ingrained in your threat environment, your organisation is sure to face more hurdles in managing your data. So many doors remain opened for attackers to sneak in and operate under the radar. Cybersecurity analytics answers this problem.
By aggregating, correlating and analyzing the ENTIRETY of your data, it gives you a clear and comprehensive window into your threat environment that will let you SEE — and PREVENT — emerging attacks well before they compromise your data and harm your organization.
This provides a unified view of threats and security breaches from a central console and allows for smarter planning, faster resolution and better decision making.
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This Article Was Written & published by Meena R, Senior Manager - IT, at Luminis Consulting Services Pvt. Ltd, India.
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