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Artificial Intelligence in CyberSecurity | The Advanced Guide

Dr. Jagreet Kaur Gill | 18 April 2023

Artificial Intelligence in Cyber Security | The Advanced Guide

Overview of AI in Cybersecurity

Current Technologies put the organization's cyber security at risk. Even with the new security strategy advancements, security professionals sometimes fail. Combining the strength of Artificial Intelligence in cyber security with the skills of security professionals from vulnerability checks to defense becomes very effective. Organizations get instant insights, in turn, get reduced response time. The type of attacks we are prone to currently being -Advanced Malware
  • Insider threats
  • Transaction frauds
  • Encrypted attacks
  • Data exfiltration
  • The exploitation of run-time application
  • Acquisition of accounts
  • Network Lateral Movement
Machines and equipment that may have been attacked to predict, identify and prevent the potential new threats this all can be possible with cyber security analytics. Taken From Article, Challenges and Solutions of Cyber Security Analytics

The primary targets of listed cyber attacks put at risk enterprises, government, military, or other infrastructural assets of a nation or its citizens. The volume and advanced cyber-attacks have increased, as mentioned earlier. These reasons require the incorporation of Artificial Intelligence with existing methods of cybersecurity to appropriately analyze and reduce the occurrence of cyber-attacks.

Why do we need AI Cyber-Security Detection systems?

  • The Rule-based detection systems for handling false positive results while handling attacks.
  • Hunting of threats efficiently.
  • Complete analysis of threat incidents and investigation.
  • Threat forecasting
  • Retrieve the affected systems, examine the attack's root causes, and improve the security system.
  • Monitoring of security.

Core capabilities of the AI based Cyber Security System

The organizations should make sure their AI Cyber Security Tools should have the below-defined core capabilities;

System Security

  • Network Security
  • Cloud Security
  • IoT Security
  • Malware
  • Autonomous Security

Data Security

  • Security Analytics
  • Threat Prediction
  • ML for Cyber
  • Social Network Security
  • Insider Attack Detection

Application Security

  • FinTech and Blockchain
  • Risk and Decision making
  • Trustworthiness
  • Data Privacy
  • Spam Detection

Cyber Security Analytics Solutions for Enterprises

Cybersecurity Analytics involves aggregating data to collect evidence, build timelines, and analyze capabilities to perform and design a farsighted cybersecurity strategy that detects, analyzes, and mitigates cyber threats.

The Below defined are the AI-Enabled cyber security Analytics for Enterprises.

  1. Perspective Analytics: Determination of the actions required for analysis or response.
  2. Diagnostic Analytics: Evaluation of root cause analysis and modus operandi of the incidents and attacks.
  3. Predictive Analytics: Determination of higher-risk users and assets in the future and the likelihood of upcoming threats.
  4. Detective Analytics: Recognition of hidden, unknown threats, bypassed threats, advanced malware, and lateral movement.
  5. Descriptive Analytics: For obtaining the current status and performance of the metrics and trends.

AI-powered Risk Management Approach to Cyber-security

  • Right Collection of Data.
  • Representation Learning Application.
  • Machine Learning Customization.
  • Cyber Threat Analysis.
  • Model Security Problem.
Machines and equipment that may have been attacked to predict, identify and prevent the potential new threats this all can be possible with  cyber security analytics. Taken From Article, Application Security – Vulnerabilities Checklist | Tools | Strategy

How Machine Learning and Deep Learning is helping in Cyber Security?

With ML and Deep Learning, cybersecurity systems can analyze patterns and learn from them to contain similar attacks and respond to altering behavior. It can help cybersecurity teams be more aggressive in preventing threats and responding to active attacks in real-time.

Technique Description Algorithm
Classification For determining whether the security event is reliable or not and belongs to the group or not. Probabilistic Algorithms such as Naive Bayesian and HMM Instance-based algorithms such as KNN, SVM, and SOM. Neural Networks Decision Trees
Pattern Matching Detection of malicious patterns and indicators in large datasets. Boyer Moore KMP Entropy Function
Regression Determination of trends in security events as well as prediction of the behavior of machines and users Linear Regression Logistic Regression Multivariate Regression
Deep Learning Creating automated playbooks based on past actions for hunting attacks. Deep Boltzmann Machine Deep Belief Networks
Association Rules Alerting after detecting similar attackers and attacks. Apriori Eclat
Clustering Determination of outlier and anomaly. Creation of peer groups of machines and users. K-means Clustering Hierarchical Clustering
AI using Neuroscience Augmentation of human intelligence, learning with each interaction to proactively detect, analyze, and provide actionable insights into threats. Cognitive security
Therefore, some primary techniques need to be implemented for performing security analytics.

Specialized Knowledge

Security analytics is a complex task that requires specialized knowledge of risk management systems, log files, network systems, and analytics techniques.


Statistics, machine learning, and mathematics behind every technique and the reasons for choosing a specific technology over others are lost or forgotten once a choice is made. With rules-based systems, the sheer quantity of rules generates a cognitive burden that makes blocks comprehensive understanding. Finally, these outputs in systems are hard to capture and improve only incrementally over time.

How is Analytics with Artificial Intelligence supporting Cybersecurity?

Analytics of any kind starts with Data collection. Below are the various data sources from where data is collected and then analyzed.

Type of Data Category Description
User Data UBA Products Collection and analyzing user access and activities from AD, Proxy, VPN, and applications.
Application Data RASP Products Collection and analysis of calls, data exchange, commands along with the WAF data for installing the agents on the application.
Endpoint Data EDR Products Analyzing the internal endpoints such as files, processes, memory, registry, connections, and many more by installing agents.
Network Data Network Forensics and Analytics Products Collecting and analyzing the packets, net flows, DNS, and IPS data by installing the network appliance.


Performance Attributes Solutions for Cyber Security

Cyber Security Analytics Solutions - XenonstackIt relates to the performance quality attributes

Unnecessary Data Removal

The subset of event data that is not useful for the detection process is taken as redundant data. Therefore, data is removed so that performance could be increased. Data Cleaning Process - XenonStack As shown in the figure, after the removal of unnecessary data, the data is forwarded to the data analytics component to detect cyber attacks. Finally, the results are visualized using visualization components.

Feature Extraction and Selection

The feature extraction and feature selection processes allow parallel processing abilities to increase the speed of the selection and extraction process. Then, the extracted feature dataset is forwarded onto the data analysis module that performs a different operation to analyzes the decrease in the size of the dataset to identify cyber-attacks. In the situation of an attack, alerts are provoked that can be visualized by the user (e.g., network administrator or security expert) using the visualization component. Once these attack alerts come under notice, an enterprise or user can take significant steps to mitigate or prevent the effects of the attack. Feature Extraction and Selection Process - XenonStackData Cutoff

The data cutoff component imposes the cutoff by neglecting security events that emerge after the connection of a network or process has reached its already defined limit. Any security event that emerges after the predefined limit does not contribute undoubtedly to the attack detection process, therefore, analyzing these types of security events implies an extra burden on data processing resources without any recognizable gain. The data storage entity can store the security event data left after cutoff. The data analysis module reads the stored data to analyze it for detecting cyber attacks. In the end, the results of the analysis are visualized to a user through a visualization entity, which allows a user to take vital action upon the arrival of every outstanding alert. Securing the data -XenonStackParallel Processing

The data collector entity captures security event data from different resources depending on the different types of security analytics and security requirements of a specific enterprise. The data collector delivers the captured data to a data storage entity, which stores the data. There are many ways to store data such as Hadoop Distributed File System ( HDFS), Relational Database Management System (RDBMS), and HBase. To apply parallel processing, the stored data needs to be distributed into fixed-size blocks (e.g., 128MB or 64 MB). After partitioning, data is imported in the data analysis component through different nodes working in parallel based on the guidelines of a distributed framework such as Spark or Hadoop. The result received by the analysis is shared with the user through the visualization component.

Parallel Processing Solutions - XenonStack

ML and DL algorithms for Enabling Artificial Intelligence in Cybersecurity

The data collection entity captures security event data for the training process of a security analytics system. The training data can be grabbed from sources within the enterprise where an order is supposed to be deployed.
Today’s customer wants to get detailed insights about the sectors with the most number of attacks, their cost, yearly analysis of security incidents. Taken From Article, Automating AI and ML models in Cyber Security
After gathering the data for training, the data preparation component starts the process of preparing the data for model training by applying various filters. After that, the selected ML algorithm is implemented in the prepared training data to train an attack detection model. The time which is taken by the algorithm to train a model (i.e., training time) alters from algorithm to algorithm. Overview of ML and DL Algorithms - XenonStack After the training of the model, it is tested to investigate whether the model can detect cyber attacks. For model testing, data is collected from the enterprise. The data which is for testing is filtered through the data preparation module and imported into the attack detection model, which is used to analyze the data for identifying the attacks on the basis of the rules which are learned during the phase of the training. The time is taken by an attack detecting model to conclude whether a specific stream of data relates to an attack (i.e., decision time) depends upon the implemented algorithm. The result received by the data analysis is visualized to the user through a visualization component.

Role of Accuracy in Security Models

This section includes accuracy quality attribute:

Alert Correlation

The data collection component grabs security event data from different resources after that; collected data is then stored in the data storage and copied to the data per-processor module for applying pre-processing techniques on the raw data. The data which is pre-processed is ingested into the alert analysis module, which performs analysis on the data f or identifying attacks. It is necessary to signify here that the Alert analysis module analyzes the data in a deserted fashion (without seeing any contextual information) anomaly-based or either using misuse-based analysis or both. The generated alerts are forwarded to the alert verification module, which uses different techniques to identify whether an alert is falsely positive. The warnings identified as false positives are neglected at this level.

The bright and well-arranged alerts are then forwarded to the alert correlation module for further analysis. After that, the alerts are correlated (i.e., logically linked) using different techniques and algorithms such as rule-based correlation, scenario-based correlation, temporal correlation, and statistical correlation. The Alert correlation module synchronizes with data storage for taking the required contextual information about alerts. The results of the correlation are liberated through the visualization module. Finally, either an automated response is developed, or a security administrator performs the analysis of the threat and responds accordingly.

Attacks can originate internally due to malicious intent or negligent actions or externally by malware, target attacks, and APT. Taken From Article, Anomaly Detection for Cyber Network Security

Signature Based Anomaly Detection 

The data collection component collects security-relevant data from different resources. After that, the collected data is stored by the data storage module. Next, data is imported into the signature-based detection component that performs the analysis of the data to detect patterns of the attack. For such analysis, this component provides the advantage of the pre-designed rules from the database of the states that identify patterns of the attack. If any match is detected, an alert is directly generated through a visualization module. Signature Based Anomaly Detection - XenonStack If the signature-based detection component does not identify any pattern of attack in the data, the data is passed to the anomaly-based detection component for detecting unknown attacks that the signature-based detection component cannot identify. An anomaly is defined as the unusual behavior or pattern of the data. This particular indicates the presence of the error in the system. Taken from Article, Log Analytics, Log Mining and Anomaly Detection with Deep Learning

The anomaly-based detection module analyzes the data using algorithms of machine learning to identify deviations from normal behavior. When an anomaly (deviation) is identified, an alert is produced through the visualization module. At the same instance of time, the anomaly is defined in the form of an attack pattern or rule and forwarded to the database of the rules. Using this way, the rules database is continuously updated to enable the signature-based detection component to detect a variety of attacks.

Attack Detection Algorithm

The data collection module grabs security event data for training the security analytic system for detecting cyber attacks. The training data can be collected from different resources within an enterprise where an order is supposed to be deployed. After the process of data collection related to the training data, the data preparation module prepares the data for training the model by implying different filters and techniques of feature extraction. Attack Detection Algorithm - XenonStack Next, the prepared training data initialize training the attack detection module. Once the module is prepared, it is validated to investigate whether the model can identify cyber-attacks. For validating the model, the data is collected from an enterprise. The test data is prepared for forwarding into the attack detection module. The prepared test data is imported to the attack detection model, which performs the analysis based on the rules learned during the phase of the training. Here, the imported test data instances are classified as either malicious or legitimate. The analysis results are visualized to a user through the visualization module. In the situation of malicious or attack situation, a user can take immediate required actions that may include blocking a few ports or slicing off the affected components from the network to stop further damage.

Combining Multiple Detection Methods

Security event data is grabbed from different resources. It is important to note that the resources from where security event data can be grabbed are not limited to what is demonstrated in the image. Attack Detection Techniques - XenonStack The choice of data resources differentiates from organization to organization relies upon their exact security requirements. After completing the process of the collection, the resulting data is stored in a data storage component. Then the data is passed to the data analysis component where different attack detection methods and techniques are implemented to analyze the data. The choices and number of attack detection methods and techniques rely upon some factors.

These factors comprise the processing ability of an organization, the data resources, security requirements, and finally, the security expertise of the organization. For example, an immensely security-sensitive organization (for example, National Security Agency) having a high budget as well as the tools of high computational power may incorporate several attack detection methods and techniques to secure their data and infrastructure from attacks related to cyber technologies. The attack detection methods and techniques are imposed on the whole dataset in a parallel manner. The visualization component immediately informs about any outstanding anomalies to users or administrators, who are expected to respond to security alerts.

Almost all organizations are reliable on the Internet for their work. So it becomes a major concern to develop a Unified Cyber Security Monitoring and Management Framework. Taken From Article, Monitoring and Management Framework

Artificial Intelligence Cybersecurity Solutions for Scalability

This section relates to the Reliability quality attribute

Dropped Netflow Detection

The network traffic is fleeting through the router demonstrated in the figure. A NetFlow grabber is attached to the router, which grabs the NetFlow and stores them into the NetFlow storage. During the NetFlow collection procedure, the NetFlow sequence monitor module is monitoring the sequence numbers which are embedded (by design) into the NetFlow. Netflow Detection and Security - XenonStack In the condition of sequence numbers are found out of order at any stage, the NetFlow sequence monitor sends a warning message representing the missing flow in the particular stream of NetFlow. The warning message is then logged alongside the exact stream in the NetFlow storage module to point out that the stream of NetFlow has some flows missing that might be crucial for identifying an attack. At the same instance time, a warning is visualized to a security administrator through the visualization module. Then a security administrator may take immediate action for solving the issue due to which some NetFlows may get dropped.

What are the measures of Artificial Intelligence in Cybersecurity?

The nodes are used for collecting security event data are placed in different sectors for collecting different types of data. Some collect data related to network traffic, and others collect database access information, and so on. Security measures are implemented to the data which is collected to ensure its secure transfer process from the data collection module to the data storage and analysis module. The security measures incorporated differentiate from system to system. Data Security Process - XenonStack Some systems give preference to encrypt the collected data and then perform the transfer process of the data in encrypted form. Other systems prefer to use Public Key Infrastructure (PKI) to ensure a secure transfer process of data and verification of the party transferring the data. As soon as the data is received by the data storage module and analysis module in a secure mode, the data analytic operations are applied to perform analysis processes on the data for detecting attacks. The results which are generated from the analysis are presented to users through the visualization component.

Artificial Intelligence Cybersecurity Alert Ranking Modules

The data collection module grabs security event data from different resources, which is then pre-processed by the pre-processing data module. The pre-processed security event data is passed to the data analysis component, which performs different analytical procedures on the data for identifying cyber attacks. The results exported from the analysis (i.e., alerts) are passed to the alert ranking module, which ranks the alerts based on predefined rules to assess the impact of the alert on the whole organization’s infrastructure. The criterion for ranking the alerts relies on the organization. Alert Ranking Process - XenonStack For example, the ranking rules for an organization vulnerable to DoS attacks will rely on an organization vulnerable to brute force attacks. Finally, the ranked list of easy-to-interpret and straightforward alerts is shared with security administrators using the visualization module, which eases the task of a security administrator to first give a response to the alerts on the utmost of the rank list as these alerts are foreseen to be more consequential and dangerous.

What are the Best Tools for Artificial Intelligence in Cybersecurity?

These are some of the tools that are using the various algorithm of AI to get the best security to organizations. 

  • Symantec's Targeted Attack Analytics
  • Sophos' Intercept X tool
  • IBM QRadar Advisor
  • Vectra's Cognito
  • Darktrace Antigena
Let us talk about it, above tools in length.  
  1. Symantec's Targeted Attack Analytics - This tool is used to uncover private and targeted attacks. It applies Artificial intelligence and machine learning to the processes, knowledge, and capabilities of Symantec's security experts and researchers. Symantec used the Targeted Attack analytics tool to counter the Dragonfly 2.0 attack. This attack targeted multiple energy companies in The USA and tried to gain access to operational networks.
  2. Sophos' Intercept X tool - Sophos is a British software and hardware security company. Intercept X uses a deep learning neural network that functions like a human brain. Before a file is performed, Intercept X will retrieve millions of features from a file, perform an in-depth review and decide whether a file is benevolent or harmful within 20 milliseconds
  3. IBM QRadar Advisor - IBM's QRadar Advisor is utilizing IBM Watson technologies to counter cyber-attacks. This utilizes AI to auto-examine signs of any vulnerability or exploitation. QRadar Advisor utilizes cognitive reasoning to provide valuable feedback and speeds up the response process.
  4. Vectra's Cognito - Vectra's Cognito detects attackers in real-time using AI. Threat detection and identification of attackers are automated in this tool. Cognito collects logs, cloud events, network usage data, and behavioral detection algorithms to reveal hidden attackers in workloads and IOT devices.   
  5. Darktrace Antigena - Darktrace is an effective method of self-defense. Antigena extends the critical functionality of Darktrace to recognize and duplicate the role of digital antibodies that recognize and neutralize threats and viruses. Antigena utilizes the Enterprise Immune System of Darktrace to recognize and react to malicious behavior in real-time based on the nature of the danger.

Artificial Intelligence Cybersecurity Strategy

Effective network security analytics is not a function of applying just one technique. To stay ahead of evolving threats, a network visibility and analytics solution needs to be able to use a combination of methods. This begins by collecting the right data for comprehensive visibility and using analytical techniques such as behavioral modeling and machine learning. All this is supplemented by global threat intelligence that is aware of the malicious campaigns and maps the suspicious behavior to an identified threat for increased fidelity of detection.