Data collection, aggregation and analysis capabilities are used by Security Analytics to perform critical security tasks such as detecting, analyzing, and mitigating cyber threats in a proactive manner.
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.
What is Cybersecurity Analytics all about?
Cybersecurity analytics is the use of 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 took – 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).
Endpoint and user behavior data, business applications, operating system event logs, firewalls, routers, virus scanners, external threat intelligence sources, and contextual analysis are just a few of the data sources that data analytics in cybersecurity solutions gather.
Combining and comparing this data creates a single data set for organizations to work with, enabling security experts to perform appropriate algorithms and create fast searches to identify early signs of an attack.
Machine learning methods can also be used to conduct threat and data analysis in near real-time, allowing for more accurate detection.
This post examines the characteristics and advantages of a security analytics platform, the most serious threats to your business, several security solutions, and how security analytics may assist you to prevent assaults and keep your environment secure.
Predictive analytics is the process of using advanced statistical algorithms to predict future events based on current data, by identifying hidden patterns.
The main reason for its increased usage in cybersecurity is because it helps security professionals predict threats, identify high-risk users or devices, prioritize alerts and so much more.
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 new discoveries and insights through visualizations such as heat maps and chronological timelines;
Features that help analysts understand complex connections among various types of events.
Another important benefit of applying machine learning methods to big-data security centers (SOCs) is the ability to automatically identify new attacks and create visualizations for more effective investigation.
Cybersecurity analytics technology and automation is a branch of cybersecurity that offers an automated approach to identifying, prioritizing and resolving security incidents – all with the goal of keeping 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 for 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.
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.
The Cybersecurity Threat Analytics platform is a predictive analysis system that consolidates various cyber threat data sources in order 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.
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.
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 Cyber Security 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 cyber security as it relates 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.
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.
NSA is built up of several components: A collector (that gets and stores big data sets), a classifier (which learns models), and an analyzer (which detects deviations from the models).
The analyzer is a core component of NSA. It runs regular queries on the classifier to detect anomalies in the network traffic.
This process can be separated into three steps:
1) Data collection and normalization: When analyzing big data, it is crucial to de-duplicate (or in general, normalize) the data in order to minimize false positives which can occur by looking at repetitive strings (for example, 404 errors).
For normalized traffic logs the classifier uses clustering algorithms that group together similar patterns;
2) Model building: The actual learning step of the classifier is done using machine learning algorithms like, for example, unsupervised clustering. This process uses regular expressions to learn normal behavior profiles from the normalized data;
3) Data analysis: Once the classifier has learned the network’s normal behavior profile, it can be used as a query engine against live traffic data to detect anomalies.
The analyzer regularly queries the classifier and calculates the confidence of each anomaly.
These three components are all tightly integrated into the FortiSIEM platform, which is used to manage and monitor the different NSA components.
Network Security Analytics provides great value for network security teams that want to use big data technology in their IDS/IPS solution.
While most existing solutions tend to rely on signature and vulnerability assessment, NSA brings an entirely new approach.
This allows network security teams to detect zero-day attacks and unknown threats even before the first signature has been released.
Network security is an important issue in the field of computational network science. There is a lot of information to be discovered from the traffic that flows through a computer network.
In this paper, we propose a solution of using data analysis to discover such information and use it to secure networks from possible security breaches.
The proposed network security system will be able to monitor the different connections that travel across a computer network and learn from these connections.
The system will use the learned information to identify potential security breaches in the network and notify an administrator about them.
This is done by identifying patterns in the connections, nodes with suspicious behavior are then identified. Thereafter it uses data analysis tools to extract knowledge from these patterns that can be used to detect possible security breaches over time.
Big Data Analytics in Cybersecurity
Big data is all the rage. Big data is purported to be the next big thing in information technology, and it promises to solve problems that have defied solutions until now.
According to IBM’s definition of big data, it’s about capturing every bit of unstructured information flowing over computer networks, organizing it in ways that make sense for you through analytics, then performing actions based on the results (IBM).
Perhaps no industry needs big data more than cybersecurity.
The volume of attacks is growing exponentially; in fact, the latest research shows that malware attacks grew by 650% between 2010-2012 (McAfee).
Furthermore, companies are getting better at catching cyber security threats but this has led malicious actors to innovate even faster (Marsh).
There is a pressing need, therefore, to use big data analytics in cyber security. However before exploring how big data analytics can be used in cyber security, here is a primer on what actually constitutes big data and the technologies that make it possible.
The definition of ‘big’ varies by context but there’s no doubt that we’re dealing with ‘bigger than before types of information.
For example, the World Wide Web Consortium (W3C) says that Big Data refers to datasets whose size or type is beyond the ability of commonly-used software tools to capture, store and process them easily (W3C).
They add: “However, there are many other dimensions along which large datasets differ from smaller ones; for example, the rate of data generation and the diversity of data formats (W3C).
The two broad categories of big data technologies are Hadoop-based technologies and non-Hadoop-based technologies. A sub-segment within Hadoop is Apache Spark which provides real-time insights on hot data.
There are several components that make up these technologies but I will focus on Apache Hadoop since it is an open-source framework for storing and processing large datasets in a distributed computing environment.
The fact that it’s open-source also means that there’s excellent community support for building upon it or tweaking it to suit specific requirements; this further adds to its popularity.
An important concept in big data analytics is Map Reduce which provides parallel processing of maps and reduces functions.
Map functions are applied to each input record (key-value pair) while the reduce function aggregates the results into a single output for that key-value pair; this is then repeated until all data has been processed (Apache).
Why use Big Data analytics in Cybersecurity?
Cyber attacks are an increasing menace to companies, governments, militaries and even individuals around the world. The statistics speak for themselves: there were over 1 million cyber attacks every day in 2012.
These incidents cost organizations hundreds of millions of dollars annually (Akamai). With so much at stake, it would be foolish to rely on traditional methods for catching malicious actors.
Big data offers greater insight into system behavior and can therefore be used to catch threats. If big data analytics were at the forefront of cyber security, malicious actors could be caught before they do damage; here’s how:
Imitating users: One of the biggest challenges in cyber security is that attackers use common user behavior.
For example, sneaky emails and clicking on suspicious links are some of the most common tactics for tricking people into revealing sensitive information or installing malware.
Big data offers a way around this challenge; if we had access to massive amounts of information such as web links visited, logins, timestamps and device IDs, etc., it might become possible to create profiles of individual users.
This would mean that hackers would have a harder time mimicking different types of user behavior, making it easier to catch them.
Imitating devices: In addition to user behavior, attackers also mimic specific types of device behavior. For example, a smart TV is a very different type of device from say a smartphone or a tablet.
Since each device has unique characteristics such as screen size and processing power, features such as camera resolution may be completely missed by traditional security methods which focus on known malicious signatures for individual types of remotes (e.g. ransomware).
Big data could thus provide insights into commonalities across different devices even if they have never been exposed to the same threat before thereby helping patch up any weak points in security that could otherwise be exploited by hackers.
Increasing productivity: According to the World Economic Forum, big data is expected to increase productivity by around $300 billion per year.
With so much at stake, it’s surprising how few companies today are using big data analytics despite its far-reaching benefits such as reduced operating costs and increased revenue (Scribe Software).
While the security industry tends to lag behind other industries when it comes to adopting new technologies, I expect this field will soon catch up with trends such as cloud computing, blockchain technology and edge devices which are already being used in many sectors today.
Using big data for responsible disclosure of cyber vulnerabilities: Using human power alone, researchers would take decades or more to find out about every single vulnerability that exists within a large piece of software.
This is why information on vulnerabilities must be shared responsibility among relevant parties.
Because big data offers unprecedented insight into system behavior, it could be used for responsible disclosure by researchers to quickly pinpoint areas of the software that are more at risk for cyber attacks due to vulnerabilities.
Reduce overall cost of cyber security: At the moment there is no consensus on how much money exactly is being spent globally on cyber security each year; however, reports suggest that the number exceeds hundreds of billions of dollars (NetDiligence).
One main reason why it is so difficult to get an accurate estimate is that products on the market are constantly changing and developers use different tools and techniques to build them. Big data could help overcome this challenge in two ways:
First, collecting basic information such as device IDs and timestamps for all access logs and files would enable any organization to record and analyze all changes made to their systems over the years.
This could help pinpoint malicious activity much faster than traditional methods such as searching through thousands of different apps and environments for signs of compromise.
Second, big data analysis may assist in discovering commonalities across different types of cyberattacks enabling researchers to create signatures that can be used by security products on the market today (e.g. Fortinet FortiGate ).
Provide real-time visibility into network traffic: At the moment there exist very few ways to know what is going on inside a system; unknown threats are detected almost exclusively after an attack has taken place, giving hackers time to cover their tracks thus making it difficult to identify and stop them.
Big data techniques such as machine learning (ML) and artificial intelligence (AI) could change this by helping security companies to detect unknown threats in real-time even if they have never been seen before.
For example, AI could be used to scan network traffic for unique patterns that are characteristic of an attack.
Real-time mapping of cyber threats: Companies that provide computer security services face the daunting task of constantly adapting to an ever-changing landscape of threats and it is becoming increasingly difficult to stay a step ahead.
Big data could help by providing a real-time map of all cyber threats in the world. This would allow security analysts to quickly create intelligence reports that accurately reflect what is going on in the cyber landscape today.
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!
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 automatically detect botnets using machine learning methods.
Few challenges in bot detection:
1- 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 that 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 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. For doing 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 hard 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 have found this library quite useful as it has got 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!
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 useful for us when we have a history of long-term interactions/history between 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.
(3) 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 with!
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!
(4) 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 case of SVM & Naive Bayes etc.
What is 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, 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;
For example, their monetary value – a typical aspect of a financial institution -their technical features – a typical aspect of an IT system – their connections with other entities – a typical aspect of social networking – or any other relevant feature that might have an impact on privacy
Cloud security 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. Cloud services use this information to detect unusual or suspicious behavior early on.
It offers various benefits such as a significant decrease in time for detecting cyber-attacks (from months to minutes), an increase of detection rate (the number of cyber-attacks reported compared with those that were not) and rapid reaction times (minutes or hours).
Thanks to its ability to monitor many different parameters, cloud security analytics permits detection of attacks based only on the user side: even if malware was involved, it will be discovered and blocked before damage occurs. This can also help prevent breaches leading up
Cloud services, such as email and collaboration applications, are widely used by enterprises; however, their use also presents a unique challenge to security professionals: they require data to be shared across multiple enterprises or departments.
The same is true for big data analytics: large amounts of raw data must be analyzed and interpreted – but this volume of information does not match well with how we create networks and compute systems today.
We need an approach that allows the processing of very large datasets in parallel over many servers – ideally on-premise – without having to pay for expensive cloud infrastructures. At Symantec, our solution is called cloud security analytics (CSA).
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 in the area.
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.
Best Cybersecurity Analyst Certifications
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
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!