Data collection, aggregation, and analysis capabilities are used by Security Analytics to perform critical security tasks such as detecting, analyzing, and mitigating cyber threats proactively.
The objective of cybersecurity analytics solutions such as threat detection and security monitoring is to discover and examine security breaches or possible dangers such as external malware, targeted assaults, and malicious insiders.
With the capacity to detect these dangers at an early stage, security experts have a chance to prevent them from reaching network infrastructure, stealing critical data and assets, or causing damage to the company.
Table of Contents
What is Cybersecurity Analytics?
Cybersecurity analytics is an advanced analytics to identify, monitor, and protect an organization’s digital environment.
Organizations are struggling more than ever with the challenges of defending themselves against cyberattacks due to increasingly sophisticated adversaries and pressure to do more with less.
No longer can organizations rely on traditional software-based protection tools/methods alone because they simply cannot keep up with fast-moving threats.
They need better ways to understand what is happening inside their networks, endpoints, and other systems.
This new approach involves using multiple sources of data (event logs, network packets, user behavior) together with big-data technologies to develop insights that can be used to create relevant alerts that can then be manually investigated or automated responses taken – depending on the severity of the alert.
Threats are becoming increasingly complex and difficult to identify using rules-based or signature-based security products, as adversaries use widely available penetration testing & hacking tools such as Metasploit and scripts written in PowerShell (which is enabled by default on all modern Windows operating systems).
Types of Cybersecurity Analytics
There are several types of cybersecurity analytics that organizations use to enhance their cybersecurity posture:
- This type of analytics involves the examination of historical data to understand past events and trends.
- Descriptive Analytics helps in providing context for understanding the current state of cybersecurity.
- Diagnostic analytics goes beyond descriptive analytics by attempting to identify the root causes of cybersecurity incidents and anomalies.
- It involves investigating why certain events occurred and helps in pinpointing vulnerabilities and weaknesses.
- Predictive analytics uses statistical and machine learning models to forecast future cybersecurity threats and incidents.
- By analyzing historical data and patterns, organizations can make informed decisions to prevent or mitigate potential attacks.
- Advanced security solutions use predictive analytics to find hidden relationships within large amounts of diverse endpoint and user behavior data collected from multiple sources.
- (e.g., network traffic, log files, and web proxies) and present discoveries and insights through visualizations such as heat maps and chronological timelines.
- Features that help analysts understand complex connections among various types of events.
- Prescriptive analytics takes the insights from descriptive, diagnostic, and predictive analytics to recommend specific actions or countermeasures to address cybersecurity threats.
- It helps organizations proactively manage their security posture.
- It focuses on analyzing the behavior of users and entities within a network to detect unusual or suspicious activities.
- Behavioral Analytics can identify insider threats and advanced persistent threats (APTs) by recognizing deviations from normal behavior.
Threat Intelligence Analytics
- This type of analytics involves the collection and analysis of external threat intelligence feeds, such as indicators of compromise (IoCs) and tactics, techniques, and procedures (TTPs) used by threat actors.
- Threat Intelligence Analytics helps organizations stay updated on emerging threats.
Cyber Risk Analytics
- Cyber risk analytics is the use of sophisticated algorithms and techniques to discover, characterize and assess cybersecurity risks.
- The first step in cyber risk analytics is to identify all entities involved (for example, human beings such as users or employees, organizations such as companies or institutions, and IT systems such as computer networks or network devices).
- Then we can build a database containing information about these entities. The next step consists of developing mathematical models that quantify different aspects related to those entities.
Security Information and Event Management (SIEM) Analytics
- SIEM solutions collect and analyze log and event data from various sources within an organization’s IT infrastructure. SIEM analytics can identify security incidents and correlate events to detect potential threats.
User and Entity Behavior Analytics (UEBA)
- UEBA solutions focus on monitoring and analyzing the behavior of users and entities, such as devices and applications, to detect anomalies and potential security threats. UEBA helps identify insider threats and compromised accounts.
Network Traffic Analytics
- This type of analytics involves the analysis of network traffic patterns and anomalies to detect intrusions, malware activity, and other network-based threats.
Network Security Analytics
- Also known as NSA is a new technology that tries to detect network attacks and security incidents in real-time.
- In contrast to most other IDS/IPS solutions that mostly rely on signatures or vulnerability assessment, the NSA approach is to learn models that capture normal behavior patterns based on big data sets of logged traffic.
- NSA is based on the hypothesis that given enough aggregated data, attacks can be detected by looking for specific deviations from the expected network activity profile.
Endpoint Detection and Response (EDR) Analytics
- EDR solutions analyze the behavior of endpoints (e.g., computers, servers) to detect and respond to security incidents, including malware infections and suspicious activities.
Cloud Security Analytics
- CS Analytics is the ability of a cloud service to understand and report on individual behaviors and patterns, as well as particular actions taken by users.
- With the increasing adoption of cloud services, cloud security analytics focuses on monitoring and securing cloud environments, identifying misconfigurations, and detecting cloud-specific threats.
- Cloud security analytics permits the detection of attacks based only on the user side: even if malware is involved, it will be discovered and blocked before damage occurs.
- This can also help prevent breaches leading up to Cloud services, such as email and collaboration applications, which are widely used by enterprises.
IoT Security Analytics
- IoT security analytics is designed to monitor and protect Internet of Things (IoT) devices and networks, identifying vulnerabilities and unusual device behavior.
Incident Response Analytics
- These analytics are used during and after a security incident to analyze the scope of the breach, identify the attack vectors, and develop strategies for containment and recovery.
Vulnerability Management Analytics
- Vulnerability management solutions use analytics to assess the organization’s systems and applications for known vulnerabilities and prioritize remediation efforts.
What are the Benefits of Cybersecurity Analytics?
The benefits of cybersecurity analytics are numerous and essential in today’s digital landscape:
Threat Detection: Cybersecurity analytics helps organizations identify and recognize various types of threats, including malware, phishing attacks, insider threats, and more. By analyzing network traffic and system logs, it can pinpoint anomalies and suspicious activities.
Data Loss Prevention: By monitoring data flows and user interactions, cybersecurity analytics can help prevent data breaches and the unauthorized exfiltration of sensitive information.
Early Warning: Advanced analytics can provide early warning signs of potential threats, allowing security teams to take proactive measures before an attack escalates or causes damage.
Reduced False Positives: By using machine learning algorithms, cybersecurity analytics can reduce the number of false positive alerts, enabling security teams to focus their efforts on genuine threats instead of wasting time on benign events.
Efficient Incident Response: Analytics tools can help security teams respond to incidents more efficiently by providing contextual information about the attack, affected systems, and potential impact, enabling faster and more informed decision-making.
Compliance and Reporting: Analytics tools can help organizations meet regulatory requirements by providing detailed reports and logs of security events and activities, making compliance audits more manageable.
Continuous Monitoring: Analytics solutions provide continuous monitoring capabilities, allowing organizations to maintain a vigilant stance against evolving threats.
Behavioral Analysis: Cybersecurity analytics can analyze user and system behavior over time, creating baselines for “normal” activity. Any deviations from these baselines can trigger alerts, helping to identify insider threats or compromised accounts.
Cost Reduction: Through automation and the identification of inefficiencies, cybersecurity analytics can help organizations reduce the overall cost of security operations while improving their effectiveness.
Scalability: As organizations grow and their digital footprint expands, cybersecurity analytics can scale to handle increasing amounts of data and security threats, ensuring ongoing protection.
Predictive Analysis: Some cybersecurity analytics solutions use predictive analytics to anticipate future threats based on historical data and current trends, allowing organizations to proactively strengthen their defenses.
User and Entity Behavior Analytics (UEBA): UEBA is a subset of cybersecurity analytics that focuses on analyzing user and entity behavior to detect insider threats and compromised accounts. It can identify unusual patterns of behavior that may indicate malicious activity.
Improved Vulnerability Management: By analyzing vulnerabilities in real-time and prioritizing them based on potential impact, organizations can better allocate resources for patching and reducing their attack surface.
Customization: Organizations can tailor cybersecurity analytics solutions to their specific needs and requirements, making them adaptable to unique security challenges and environments.
Cybersecurity analytics plays a crucial role in enhancing an organization’s security posture by providing real-time threat detection, incident response capabilities, and valuable insights into security events and vulnerabilities.
These benefits are essential for protecting sensitive data, maintaining the trust of customers and stakeholders, and ensuring the overall resilience of an organization’s digital infrastructure.
What is Cybersecurity Analytics Technology and Automation?
Cybersecurity analytics technology and automation is a branch of cybersecurity that offers an automated approach to identifying, prioritizing, and resolving security incidents – all to keep your network environment under continuous, real-time observation.
With big data platforms to manage large amounts of streams from multiple devices and applications in a single location, you can monitor suspicious activity without spending too much time on manual analysis.
Security professionals now have access to machine learning technologies that enable them to analyze multi-dimensional datasets within seconds with intelligent algorithms that help them discover hidden relationships among diverse types of events (e.g., endpoint/user behaviors).
These advanced techniques allow organizations to find malicious activities quickly so they can respond faster than before by automatically blocking detected threats or sending alerts to on-duty security analysts.
What is Proactive Cybersecurity and Real-time Threat Detection?
Security solutions combine behavioral analytics and machine learning with contextual analysis to identify both known and unknown threats before they can cause damage, minimizing the window of exposure to zero.
This “active” or “proactive” approach enables companies of all sizes to better understand their current threat landscape, prioritize alerts, and prevent attacks by applying contextual analysis.
Cybersecurity analytics enables security teams to proactively identify suspicious activity before it can harm your business, including targeted malware or social engineering campaigns, stolen/corrupted credentials, access from sources with high-risk scores (e.g., denied locations), and even the use of revoked or expired account credentials.
In addition, machine learning algorithms can help security teams identify anomalies in your environment to determine which sessions should be allowed and which should be blocked.
By using the output from these machine learning models, organizations can block specific types of attacks automatically before they reach their targets.
What is a Cybersecurity Threat Analytics platform?
The Cybersecurity Threat Analytics platform is a predictive analysis system that consolidates various cyber threat data sources to provide the most insightful reports about your organization’s current risk state, dynamically identifying new risks and vulnerabilities in real-time.
With this information, you can immediately take action on the latest bots, exploits, malware, malicious websites, or ransomware that are being used by hackers to target your organization specifically.
How does it work?
The Cybersecurity Threat Analytics platform works by monitoring our partner network of over 1 million global sensors comprising internet infrastructure servers running WAFs (Web Application Firewalls), IDS/IPS (Intrusion Detection/Prevention Systems), Web Servers & database systems.
These sensors constantly capture and quantify billions of events that take place daily on the internet and report them to our real-time analysis systems.
The system captures all this data, correlates it with our database of known cyber threats and trends, and produces a complete picture of your organization’s cyber risks.
What issues does it solve?
The Cybersecurity Threat Analytics platform is a solution to a variety of problems faced by organizations today. From a security perspective, it provides insight into your organization’s cyber risks from hackers, advanced malware, and exploits.
It also helps in mitigating enterprise-wide Ransomware outbreaks when they happen at your locations or where you have important assets. In addition, the Cybersecurity Threat Analytics platform allows organizations to:
- Eliminate false positives and reduce alert fatigue from their security teams.
- Prioritize cyber risk mitigation efforts by identifying the most critical vulnerabilities across your organization.
- Quickly understand emerging trends in cybersecurity as they relate to your organization’s specific business areas.
- Identify compromised systems without having to wait for a SOC (Security Operations Center) or IT department to investigate.
Why is Machine Learning Necessary in Cybersecurity Analytics?
Machine learning is defined as “a type of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.”
The applications of machine learning range from computer vision to advanced robotics systems. Machine learning has also found its place in the cyber world!
For example, hackers are trying different web vulnerabilities like SQL injection or LFI (Local File Inclusion).
Hackers are becoming more intelligent and they find new vulnerabilities often via automated vulnerability scanners. We can’t keep up with how fast hackers evolve, but we can use machine learning to learn faster than them!
Existing Solutions Used in Botnet Analysis?
Some researchers have previously applied classification algorithms for performing automatic detection of bots on IRC channels, which helps identify malicious IRC users by their previous activity patterns.
Bonsai.io is another project that aims to detect botnets using machine learning methods automatically.
Few challenges in bot detection:
1- The manual method of bot detection is tedious and time-consuming. Also, it can be easily biased by a single sample or by an unqualified person’s opinion.
2- It may also require us to monitor the whole IRC traffic and assign trust values to each and every message exchanged between network peers for long-term monitoring in order to catch malicious bots in action.
This might not be feasible if we need to detect bots on multiple networks at different places with different protocols!
3- We will need to maintain a huge database just like spam emails do (could be Spamhaus), but how can we collect such huge data? Would you want someone to learn about all your activities on IRC?
Machine learning can help us in this process with its prediction capabilities! Let’s look at how it works.
(1) We will first need to train the machine by giving known samples of good/bad bots. This training can be done using supervised methods.
Afterward, we will give new unknown samples of either good or bad bots for making predictions based on what has already been learned by the model. The more data you feed into the system, the more accurate it gets over time.
(2) Let’s say there is an IRC server where 80% of its users are misbehaving and 20% are following all the rules. If a new user joins the network without proper authentication, this new user is most likely to be misbehaving.
This method could help us stop spam or banned users by understanding their previous activity patterns.
(3) We want to limit the number of queries that need to be analyzed to either validate the presence of a malicious attacker or track its activities.
The first step is to pre-process our data so as not to send uninteresting events to the machine learning/data mining process for making predictions.
For example, an event “Chat message” can be reduced into a few meaningful attributes like the sender’s nick, message’s text, etc., which are easy for the algorithm to digest and learn from them!
(4) We also need to choose which features we should use for extraction via dimensionality reduction techniques. To do so, we can use either Principal Component Analysis (PCA) or Factor Analysis.
(5) Now it’s time for the most crucial part of the machine learning process, which is training! To train our model, we need to write a prediction function that takes an input vector x = <x1, x2,…, xn> and gives us an output value y = <y1, y2,… ,yn>.
Now all the hrd work done by our model will be y = f (x). We can create decision boundaries and assign thresholds in order to classify samples into two categories: good and bad.
These thresholds should be placed in such a way that they capture maximum valuable information from the input features while eliminating unimportant features. For this, we can use the K-nearest neighbor algorithm.
(6) Finally, it’s time to test our model by feeding new unseen data into it and see how well it performs on them! We’ll need to tune up parameters like training/validation sets so that our model is always learning.
This process continues until we get enough confidence in our system and start applying it to find bad bots at scale!
Machine Learning Tools:
There are many machine learning & data mining tools available online that will help us to quickly build such a model using supervised or unsupervised algorithms other than the custom ones that we may develop ourselves.
Here I am listing some open-source tools that can be used for building such a system:
* MLPACK (includes various implementations of SVM, k-NN, Naive Bayes, etc.)
* Weka Data Mining Workbench – a bit more updated tool compared to MLPACK! It covers most of the data mining algorithms along with very good GUI features.
And same as above, it supports different kinds of machine learning models. * Orange Machine Learning Library – While working on the SpamAssassin project at Yahoo Labs I found this library quite useful as it has great documentation and open-source code samples to help me kick-start my work faster!
In addition, it also includes powerful functionalities like data preprocessing steps before starting your modeling process from raw log files.
It also has support for web mining (collection of extracted features from web pages or HTML content) and different machine learning techniques like SVM, Naive Bayes, etc. *
Pattern – developed by CERN open lab is available under a BSD license which can be used to build your own spam classifier using various tools!
A brief explanation of each method:
(1) This approach can help us find new bad bots by showing us how a good bot behaves on a particular IRC network.
In this way, we will have a dataset containing many normal user activities as well as several unknown malicious samples that were not seen/annotated before!
We will train our model with the dataset containing both normal users’ activity patterns as well as malicious samples. Now, we can use this model to predict any unseen activity if it is normal or malicious!
(2) This approach will be helpful for us when we have a history of long-term interactions/history between the user and bot (like via private messages).
In this case, one way of detecting bots is by finding out very similar patterns in their communications! We can easily do so by analyzing conversations between them over time, which will allow us to find out the same kind of questions that they’ve asked each other at different points in the conversation.
The difference here is instead of making predictions based on unseen interaction data like in the Supervised learning algorithm approach method.
(1), The main purpose behind using this method is to detect bad bots with high confidence after some bad examples were seen once and thus allow us to predict new unseen interactions.
(2) This approach is based on an unsupervised algorithm and it will be helpful in finding unknown malicious bots that we might not have any previous interaction history!
The gist of this method is that the more time a user spends online, the more familiar he becomes with other users’ activities & behavior patterns.
Our model can be trained on such a data set containing only the bot’s activity patterns (and normal users’ too) without knowing their real class labels i.e. good vs. malicious!
We can use all kinds of clustering algorithms like k-means to cluster different kinds of samples into different clusters/groups which allows us to find out bot-like behavior patterns (or bot-like groups) and can be used to predict unseen bots!
(3) This is a supervised learning algorithm approach that has the same idea as method (1).
The only difference here is that we will use some public datasets containing malicious examples for training our model.
Again, we must choose the correct data preprocessing steps to transform raw logs into such a format that can be fed directly into machine learning algorithms!
The trained model will again help us to make predictions about any new unseen events based on their features i.e. distance between vectors in the case of SVM & Naive Bayes etc.
Cybersecurity Analyst Certifications
Having hands-on experience is essential when searching for a cybersecurity analyst job.
Although many businesses prefer applicants to have at least a bachelor’s degree, it is frequently overlooked if the applicant has prior knowledge and expertise.
Earning a cyber security analyst certification is an excellent method to demonstrate and demonstrate your understanding. The following are some of the key advantages of obtaining a cybersecurity analyst certificate:
- You can get specialist and comprehensive expertise.
- It provides evidence to employers that you are still learning new things. It signifies a certain level of skill.
- There are more chances for career growth in your field.
- Your earning potential increases as a result of it.
- It indicates a degree of dedication to your vocation.
- It may give you an edge over your competitors in the job market.
Here’s a list of some of the qualifications that might be useful in your job as a cybersecurity analyst:
- CompTIA’s Network+
- CompTIA’s Security+
- CompTIACybersecurity Analyst
- CompTIA Advanced Security Practitioner
- CompTIA Security Analytics Expert certification
- The EC-Council Certified Ethical Hacker Certification
- Certified Security Analyst Training
- The GIAC Information Security Fundamentals
- The GIAC Security Essentials Certification
- Certified Information Systems Security Professional
Cybersecurity Analytics Master’s Degree in the United States
Several universities and institutions in the United States offered Master’s degrees in Cybersecurity Analytics or related fields. Here is a list of some universities known for their cybersecurity analytics programs:
Carnegie Mellon University – Heinz College of Information Systems and Public Policy
Program: Master of Science in Information Security Policy and Management (MSISPM)
Georgia Institute of Technology – College of Computing
Program: Master of Science in Cybersecurity
University of Maryland, College Park – College of Information Studies
Program: Master of Information Management with a specialization in Cybersecurity
Johns Hopkins University – Whiting School of Engineering
Program: Master of Science in Cybersecurity
Syracuse University – School of Information Studies
Program: Master of Science in Cybersecurity
Boston University – Metropolitan College
Program: Master of Science in Computer Information Systems with a concentration in Security
Northeastern University – College of Computer and Information Science
Program: Master of Science in Cybersecurity
University of Southern California – Viterbi School of Engineering
Program: Master of Science in Cyber Security Engineering
University of San Diego – Shiley-Marcos School of Engineering
Program: Master of Science in Cyber Security Operations and Leadership
Stevens Institute of Technology – School of Business
Program: Master of Science in Cybersecurity
University of Illinois Urbana-Champaign – School of Information Sciences
Program: Master of Science in Information Management with a focus on Cybersecurity
Rochester Institute of Technology – B. Thomas Golisano College of Computing and Information Sciences
Program: Master of Science in Computing Security
Current Job Demand for Cybersecurity Analytics
The demand for cybersecurity analysts and professionals, including those specializing in cybersecurity analytics, was quite high and projected to continue growing in the coming years.
However, please keep in mind that the job market is subject to change, and the demand for specific roles can fluctuate based on various factors, including technological advancements, industry trends, and global events.
To stay competitive in the field of cybersecurity analytics, professionals often need a strong foundation in cybersecurity concepts, data analysis, machine learning, and familiarity with tools and technologies used in the field.
Continuous learning and staying updated on the latest cybersecurity trends and threats are crucial.
We recommend you to go through the current job listings, industry reports, and speaking with professionals in the field or cybersecurity organizations to get the most up-to-date information on job demand in cybersecurity analytics.
The key to understanding how cybercriminals think is knowing what they are after. When you know their goal, it becomes much easier to protect against threats – take a look at our infographic for more information! Not sure where to start?
Contact us today and we’ll be happy to help you get started with cybersecurity analytics that can give your company the protection it needs.
We have experts who specialize in monitoring digital risks, which means no matter what business size or industry type, there’s someone on hand ready to make recommendations based on your specific needs!
There really is no better time than now when businesses need all hands on deck in this fight for security. With so many opportunities available online these days, do not let yourself fall victim by neglecting security. Make sure to follow our blogs for more info!