This is a complete comprehensive tutorial on Cybersecurity Analytics. Understand the types, benefits and review the top Cybersecurity Analytics tools:
What is Cybersecurity Analytics?
Cybersecurity analytics is a proactive way of using data collection, aggregation, and analysis capabilities to perform important security functions that detect, analyze, and mitigate cyber threats.
It’s a process of aggregating data from numerous sources like endpoints, user behavior data, several business applications, operating system event logs, firewall log, routers logs, virus scanners log, and many more to guide against any potential threats.
What You Will Learn:
- Guide to Cybersecurity Analytics
- Cybersecurity Analytics Tools
- Types of Cybersecurity Analytics Tools
- Useful Benefits
- Top 5 Cybersecurity Analytics Tools
- SIEM vs. Cybersecurity Analytics
- Some Biggest Security Threats
- Cybersecurity Threat Analytics Platform
- Frequently Asked Questions
Guide to Cybersecurity Analytics
Why is Cybersecurity analytics important?
This process will help detect threats before it causes harm to your system by aggregating all the data collected and applying appropriate algorithms. This will identify early signs of an attack. The system will check and observe any network behavior and data flows, looking for any potential threats.
How is cybersecurity analytics collected?
There are various tools and techniques you can use to collect information from different sources, like system logs, device logs, and various endpoints that are available. We can also collect data from external sources to prevent any security attack from happening.
These tools help gather information that will provide valuable insights into the efficiency and effectiveness of current security measures already implemented and identify other areas that need improvements. There are various cyber analytics companies that you can also consult to help with the collection of this vital information.
What is Data Analytics in Cybersecurity
Data analytics helps individuals and organizations make use of data collected through different cybersecurity tools. Every organization makes use of Data Analysts to analyze the raw data for insights into what is going on within. They use these tools to help organizations make urgent decisions on cybersecurity incidence.
Both cybersecurity data analysts and cybersecurity analysts work together on the cybersecurity analytics process.
Data Analysts and Cybersecurity Analysts: Job Description
The below shows the Data analyst job description and the Cybersecurity analyst job description.
What does a Cybersecurity Data Analyst do?
Cybersecurity Data Analysts use data analysis techniques to create valuable intelligence to boost and improve an organization’s security system.
Below are some of the major responsibilities of a Cybersecurity Data Analyst:
- Data Mining: A cybersecurity data analyst uses data mining to extract and structure very large raw datasets and identify different patterns in the data through mathematical and computational algorithms.
- Analyzing Data: A cybersecurity data analyst analyzes network traffic to detect patterns that will suggest a potential attack on the pipeline. Cyber Security Data Analyst makes use of standard Security Analytics Tools to analyze and interpret different patterns of data.
- Behavioral Analytics: A cybersecurity data analyst makes use of Behavioral Analytics to check the patterns and behavioral tracks of users and systems to identify and detect any suspicious behavior that will show an imminent security attack.
- Real-Time Intrusion Detection: A cybersecurity data analyst will make use of Security Information and Event Management (SIEM) tools to fetch data and carry out real-time analysis of security alerts generated by network devices and applications connected to the SIEM device.
- Forensics: They also make use of various forensic tools to get digital evidence from a system that was compromised and use the data collected to retrieve information and identify the root cause.
- Visualization and Reporting: A cybersecurity data analyst must know how to report results in an understandable form to the stakeholders. Using charts and graphs to clearly display and present their findings and observations.
What does a Cybersecurity Analyst do?
A cybersecurity analyst’s main role is to protect company resources like the organization’s data, IT Infrastructure (hardware, software, and networks) from cybercriminals.
Every security analyst must understand the company’s network topography and IT infrastructure very well and be able to monitor the network to detect any potential threat to the network.
Below are some of the major responsibilities of a Cybersecurity Analyst:
- Configuring Security Tools: A cybersecurity analyst will always use different security tools to monitor and protect the network. They use vulnerability management software to detect and mitigate malicious activity within the network. A security analyst will evaluate the security needs of a company and use these tools to protect its data.
- Reporting: Every cybersecurity analyst must know how to understand all details on a report, which gives a general overview of events going across the network. This is one skill they need to have, the ability to read these reports and suggest an area that is attack-prone due to some unusual activity going on there and areas that is well-protected.
- Continuous Evaluation: One major role of a security analyst is to continuously test for weaknesses within a network before a malicious actor will compromise the network.
What is Predictive Analytics in Cybersecurity
Hackers are constantly changing their cyberspace outlook with attacks that are more complex and intelligent. To keep up with their pace, cybersecurity experts and firms need to also churn out innovative and sophisticated cybersecurity applications constantly.
One important function of some of these cybersecurity tools is the usage of predictive analytics, which gives them the intelligent ability to anticipate an attack before it occurs and helps companies to prevent cyber-attacks.
Predictive analytics in cybersecurity can help your organization predict a data breach before it happens. It’s like showing you where an attack is probably going to happen and giving you the sense of urgency for switching to a combative and protective mode before it occurs.
What are Proactive Cybersecurity and Real-Time Threat Detection
Once you anticipate some attacks soon, your next approach is to be proactive enough to take immediate action by integrating real-time threat detection into your network. Once you prepare beforehand, then you will never risk the “is too late syndrome” that has put so many organizations into trouble.
Big Data Analytics in Cybersecurity
When we talk about big data analytics in cyber security, it simply means the ability to collect big amounts of important information. This process extracts, visualizes, and analyzes this enormous data to predict well in advance when it’s likely for a cyber-attack to occur.
Big Data Analytics is a part of science that can build an effective and efficient cyber threat intelligence system to fight and counter any possible cyber attacks. Many Organizations are now developing different applications to effectively analyze large-scale data for complex threat intelligence.
Why use Big Data Analytics in Cybersecurity?
Using big data analytics in cybersecurity has many benefits in terms of removing all the challenges faced by cybersecurity professionals and data scientists. This has brought great improvement in terms of the speed of identifying anomalies by visualizing and analyzing very large data to protect our environment.
The combination of Big data analytics and Machine Learning has addressed challenges earlier faced by cyber experts and provided them with more insights into what the future holds while taking care of the present.
Cybersecurity Analytics Tools
Cybersecurity analytics tools are designed to help organizations detect and rapidly respond to cyber-attacks and reduce any impact on their business operations and reputations. This tool collects, processes, and analyzes very large data coming from different sources, which could be devices log, network logs, and endpoints.
Every organization, be it corporate or a health institution, cherishes and wants to protect its sensitive data, which includes Personal Identifiable Information (Customers or Patients) and the company’s intellectual property. The use of cybersecurity analytics tools is essential for any organization looking to protect all of these.
How do cybersecurity analytics tools work?
Cybersecurity analytics tools make use of machine learning (ML) and behavioral analytics technology to monitor a network. These two technologies will spot any changes in the traffic on the network and once there is a change, there will be a trigger to allow you to address the threats immediately.
Machine learning will help in dealing with threats and attacks now and help in predicting any future threats and identifying vulnerabilities that the security team needs to mitigate. Behavioral analytics will help check all historical breaches and behaviors within the network and all endpoints.
Types of Cybersecurity Analytics Tools
We have several cybersecurity data analytics tools available which can offer organizations different functionalities in detecting and protecting against threats. They also have different strategies, like behavioral analysis, in place that create auto-response whenever a threat is found.
- SIEM Tools: Security Information and Event Management (SIEM) combines different tools that provide real-time analysis of your corporate network and supply you with security alerts.
- SOAR Tools: Security Orchestration, Automation, and Response (SOAR) is a centralized hub for data gathering, analysis, and threat response.
- External Threat Intelligence Tools: This kind of tool is provided by external companies, they usually come in the form of a portfolio of analytical processes that can support cybersecurity data analytics.
- Forensic Tools: This tool is used to investigate previous attacks and current threats. It identifies how attackers have breached a company’s security system and made the network vulnerable.
- Behavioral Analytics Tools: This tool analyzes the behavior of users, applications, devices, and different endpoints on the network. Behavioral analytics tools will check for patterns that show anomalies that may indicate a security threat within the network.
- NAV Tools: This tool analyzes the traffic coming from the user and application using multiple tools. NAV checks this data in real time as it passes through the network and logs all observations.
- Prioritized Alerts: One important feature of cybersecurity analytics tools is that you can configure them in such a way that you are not bombarded with different alerts. This tool will allow you to prioritize the most important alerts instead of your security team chasing after false or less-than-critical alerts and creating more time for them to face important issues.
- Automated Threat Intelligence: It can automate your threat intelligence using machine learning tools to detect threats automatically without any interference.
- Proactive Incident Detection: Cybersecurity analytics tools will provide you with a proactive strategy to help identify and address threats immediately. This will provide you with a full overview of any threats currently on the network and a proactive approach to mitigate them.
- Forensic Investigation: Cybersecurity analytics tools will help you know where an attack came from, how it got into your network, and the resources that were affected. The timeline for this attack will also be captured by this forensic tool.
Top 5 Cybersecurity Analytics Tools
#1) Splunk Enterprise Security
This is a cybersecurity analytics tool that is ML-powered that can monitor, detect and investigate threats with speed and accuracy. Making use of data to drive full insights for large-scale visibility and rapid detection across your entire network.
Website: Splunk Enterprise Security
#2) Splunk User Behavior Analytics
This tool helps to detect threats and anomalous behavior within the system and network using machine learning. This cybersecurity analytics too will enhance visibility within your network and improve the detection of known, unknown, and hidden threats.
Website: Splunk User Behavior Analytics
#3) Trend Micro Deep Discovery
This tool is designed to immediately detect any advanced malicious software that will bypass any of the security defenses you put in place in other to exfiltrate sensitive data from your environment. It has features like specialized detection engines and custom sandbox analysis that can detect and prevent breaches.
Website: Trend Micro Deep Discovery
#4) Microsoft Sentinel
This tool collects data of all users, devices, applications, and infrastructure, both on-premises and in the cloud. It helps detect any hidden threats that were initially not discovered, and it makes use of analytics and threat intelligence from Microsoft, which helps in minimizing the number of false positives the tool sends out.
It makes use of AI technology to hunt for any suspicious activities within your network. Its super-fast response to incidents is second to none with its built-in orchestration and automation of the execution of tasks.
Website: Microsoft Sentinel
#5) SolarWinds Security Event Manager (SEM)
The Security Event Manager comes with hundreds of pre-built connectors to help in collecting, normalizing, and analyzing logs from various sources.
This data is collected and transformed into a readable format and accessible from a central location where the security team can use it to easily investigate potential threats and the stored logs can be used for security audits.
SIEM vs. Cybersecurity Analytics
Both SIEM and Cybersecurity analytics have the same security goals. Several companies digitalizing all their business processes has really brought an increase in terms of the amount of security event data generated due to different actions performed by different users.
Actions like failed logins, unusual behavior by users, or data flowing in a pattern different from the defined pattern can indicate a security threat. These actions have generated megabytes and if not terabytes of security event data a month.
SIEM and Cybersecurity analytics now work alike in improving the speed of accuracy of threat detection. They both conduct what is called a security event correlation and analysis automatically. They both reduce the mean time to detect (MTTD) and the mean time to respond (MTTR) to cyber threats within the network.
Both make use of AI technology by using machine learning to sort through the terabytes of data in real-time much easier than what was obtainable before. Both still require human factors and involvement at some level in their operations.
The most common use cases of cybersecurity analytics include:
- Analyzes data to detect and identify any patterns that indicate security threats.
- Monitors the user’s behavior.
- It is used to detect and identify any insider threats.
- Monitors the activity of both remote and internal employees on the company’s network.
- Detects accounts that have been compromised or improper use of an account.
- It can be used for forensic purposes to investigate an incident.
- Identifies any attempts at data exfiltration
- Complies with different security standards, like the Health Insurance Portability and Accountability Act (HIPAA) and the Payment Card Industry Data Security Standard (PCI DSS)
What is the security analytics approach in a hybrid or multi-cloud environment?
Securing a hybrid or multi-cloud environment is challenging due to several components and distributed nature. It is always good to follow an approach that is widely accepted and follows the best security practice.
Approaches such as:
- The security team needs to review the current network topology and check the best cybersecurity tool that best fits.
- Implementing a tool that can manage all your cloud secrets, like passwords, keys, certificates, and credentials.
- Enforcing a tool that can scan your cloud environment regularly for vulnerabilities.
- Implementing a tool that can perform continuous audits for real-time visibility and compliance checks of your cloud environment.
- Implement a cybersecurity analytics tool that captures all the data traffic flowing inside your cloud environment.
Some Biggest Security Threats
#1) Malware Attack
Malicious software can be a hard nut to crack once it finds its way into your device. Malware usually starts from you being deceived to take an improper action of clicking a link or opening an attachment. This malicious software can get itself installed on your system without your knowledge and has the ability to replicate itself across your entire network.
Once malware is installed on your device, it will start monitoring all your activities. It’s even capable of sending confidential data to an attacker. Once an attacker penetrates your system, the attacker can use your system as a botnet.
Botnet is a group of Internet-connected devices that can perform Distributed Denial-of-Service (DDoS) attacks. Malware can be a virus, Trojans, Worms, ransomware, and spyware.
#2) Social Engineering Attacks
Social engineering attacks work by psychologically deceiving or manipulating a user into performing an action that is catastrophic to the user but to the benefit of an attacker. Some examples of these social engineering attacks are Phishing, Spear Phishing, Vishing, Honey Traps, and many more.
#3) Software Supply Chain Attacks
A software supply chain attack is an attack against an organization that acts as a vendor to another organization that makes use of its third-party software solution. Once the Vendor is attacked, then there will be easy access to other organizations that make use of the same third-party solution.
Examples of where this software supply chain attack can come from: Software build tools, Malware pre-installed on devices, Malicious code deployed on hardware, updates, patches, third-party libraries, and components. This attack can also manifest in open-source applications that some organizations make use of in their own product.
This attack can be made against the vendor’s continuous integration and continuous delivery (CI/CD) in the software development lifecycle and also a software supply chain attack causes distrust from an organization against their third-party vendors.
#4) Advanced Persistent Threats (APT)
This is when an individual or group gains unauthorized access to a network and remains on that network without being discovered for a very long time. While inside, they exfiltrate sensitive data and wreak major havoc on the attacked environment.
APT is a sophisticated attack that requires a sophisticated attacker who launches this attack against nation-states, large corporations, or some important targets.
What a Denial of service (DoS) attack will do is cause a service disruption on a resource of a target system which will make the service stop working and not be accessible to users.
Distributed denial of service (DDoS) on the other is a more sophisticated approach by the attackers to compromise a large number of connected computers to use them in a coordinated attack against a target.
DDoS attacks are often used in combination with other attack vectors to carry out attacks against a target. This approach is used in other to create commotion and confusion among the security team and IT team, which draws their attention while the main attack goes on without their notice.
#6) Man-in-the-Middle Attack
When you send data from one point to another point and it gets intercepted before getting to the receiver, then a man-in-the-middle attack has occurred.
When an attacker intercepts your communication, they can make use of the payload request sent to compromise users’ credentials, steal sensitive data, and can even maneuver the response for their own benefit.
#7) Password Attacks
This attack can be actualized by the use of any of these methods: man-in-the-middle social engineering, password guessing, or unauthorized access to a password manager which can grant a hacker access to a user’s password.
Cybersecurity Threat Analytics Platform
A Cybersecurity analytics platform is a network traffic analytics platform that can function as a proactive tool with the help of behavioral machine learning or cybersecurity analytics technologies. This analytics platform is designed to detect, monitor, and analyze different security event logs.
Cybersecurity analytics platforms are very scalable, it can accommodate a very large network and large number of users as the business grows. There are different platforms that every organization that set security priority can acquire.
Some expanding attack surfaces that present the most risk:
#1) Internet of Things (IoT)
IoT refers to devices like sensors in vehicles, cameras, household gadgets, and many more connected to the internet and you can connect to it remotely and collect data from it. This technology can pose one of the most risks due to the expanded attack surface IoT has because of its global internetwork nature.
#2) Increase in the usage of Cloud
Many organizations have moved their IT resources and sensitive data to the cloud and some have failed to configure proper and adequate security on their cloud infrastructure thereby exposing their environment and data to attackers.
#3) Work From Home
When Corona Virus struck so many people did not know that the emergence of this sickness would kill some organizations operationally because they could not keep up with their usual business operations.
While those that have a business continuity policy survived because they implement a work-from-home policy which in itself opens many facets to cybersecurity attacks. So many organizations were attacked because of back doors that were created from employees’ endpoints.
Frequently Asked Questions
Q #1) How analytics is used in cyber security?
Answer: Cybersecurity analytics uses machine learning (ML) and behavioral analytics to monitor your entire network and when any changes are spotted it triggers an alert for emergency attention towards the threat.
Q #2) How does data analytics help in cybersecurity?
Answer: Data analytics help to detect and respond swiftly to cyber threats. Data analytics tools help the security team with detailed alerts and reports showing the area that is threat-prone and the severity of the threat and provide guidelines on how to mitigate any future threat attempts.
Q #3) What is cyber analytics also known as?
Answer: Cyber analytics is also known as security analytics and can be expanded to include data analytics, as it involves the use of real-time and historical data to detect threats.
Q #4) What is the difference between a data analyst and a cyber security analyst?
Answer: A cybersecurity analyst’s main role is to protect company resources like the organization’s data, IT Infrastructure (hardware, software, and networks) from cyber criminals. Cybersecurity Data Analysts use data analysis techniques to create valuable intelligence to boost and improve an organization’s security system.
Q #5) Why is Cybersecurity analytics important?
Answer: It is a process that will help detect threats before havoc on the network. This is done through the help of aggregated data collected and applying appropriate algorithms that will help to identify early signs of an attack.
Q #6) What do cybersecurity analysts do all day?
Answer: Cybersecurity analysts protect computer networks from cyberattacks and unauthorized access. They defend and protect their organization’s valuable and sensitive data from getting to the bad actors whose aim is to have absolute access to your network and full custody of your entire data.
They perform this role brilliantly with the help of different security tools available for corporate and enterprise environments.
My candid advice for any organization is to prioritize security and put more effort into implementing cybersecurity analytics to improve their cybersecurity posture. Attackers are always innovating different attacking vectors that will always escape your notice if cybersecurity analytics is not present within your network.
Let your security team review your operations and recommend the best tool that will be predictive, proactive, detective, and preventive in nature, with fewer false positives.