Introduction to AI for Cybersecurity
With the growth of the IT field, the data increases at a fast pace. Moreover, the dependency of information is more on infrastructure, which makes it highly vulnerable to the cybersecurity attacks on computer systems, networks, and data. The primary targets of cyber attackers are enterprise, government, military, or other infrastructural assets of a nation or its citizens. The volume and advanced cyber-attacks are increasing monotonically. These reasons lead to the requirement of the development of a system where the cyber attacks could be appropriately analyzed to reduce the occurrence of cyber-attacks.This blog will give an overview of role and advantages of enabling AI in cybersecurity
Building AI Cybersecurity Detection System
- The Rule-based detection systems for the handling of false positives results while handling attacks.
- Hunting of threats efficiently.
- Complete analysis of threat incidents and investigation.
- Threat forecasting
- Retrieve the affected systems, examine the root causes of the attack, and improving the security system.
- Monitoring of security.
Categories of threats
- Advanced Malware.
- Insider threats.
- Transaction frauds.
- Encrypted attacks.
- Data exfiltration.
- The exploitation of run-time application.
- Acquisition of accounts.
- Lateral movement
Core Capabilities of the AI Cybersecurity System
- Network Security
- Cloud Security
- IoT Security
- Autonomous Security
- Security Analytics
- Threat Prediction
- ML for Cyber
- Social Network Security
- Insider Attack Detection
- FinTech and Blockchain
- Risk and Decision making
- Data Privacy
- Spam Detection
AI Cybersecurity Analytics Solutions
Determination of the actions required for analysis or response.
Evaluation of root cause analysis and modus operandi of the incidents and attacks.
Determination of higher risk users and assets in the future and the likelihood of upcoming threats.
Recognition of hidden, unknown threats, bypassed threats, advanced malware, and lateral movement.
For obtaining the current status and performance of the metrics and trends.
AI based Risk Management Approach to Cybersecurity
- Right Collection of Data.
- Representation Learning Application.
- Machine Learning Customization.
- Cyber Threat Analysis.
- Model Security Problem.
Enabling Machine Learning and Deep Learning
|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.
|Pattern Matching||Detection of malicious patterns and indicators in large dataset.|
|Regression||Determination of trends in security events as well as prediction of the behavior of machines and users|
|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.|
|Clustering||Determination of outlier and anomaly. Creation of peer groups of machines and users.|
|AI using Neuroscience||Augmentation of human intelligence, learning with each interaction to proactively detect, analyze, and provide actionable insights into threats.||Cognitive security|
The algorithms mentioned above have some limitations due to this, they are not able to work appropriately for security analytics. Therefore, some primary techniques need to be implemented for performing security analytics.
Security analytics is a complex task that requires specialized knowledge for 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 block comprehensive understanding. Finally, these outputs in systems that are hard to capture and improve only incrementally over time.
Overview of different data sources
|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 analyzing 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.|
Big Data and AI Cybersecurity Analytics Solutions
Performance Attributes Solutions for Security
It relates to the performance quality attributes
Unnecessary Data Removal
The subset of event data which is not useful for the detection process is taken as redundant data. Therefore, data is removed so that performance could be increased.
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 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 this attack alerts come under notice, an enterprise or user can take significant steps to mitigate or prevent the effects of the attack.
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 read 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 the vital action upon the arrival of every outstanding alert.
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 the 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.
ML and DL algorithms for Enabling AI 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.
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.
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.
Accuracy in Security Models
This section includes accuracy quality attribute
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 per-processed is ingested into the alert analysis module, which performs analysis on the data for 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.
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 on 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.
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 cannot be identified by the signature-based detection component.
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.
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 perform 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.
The choice of data resources differentiate 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.
AI 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, NetFlow sequence monitor module is monitoring the sequence numbers which are embedded (by design) into the NetFlow.
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 about identifying an attack.
At the same instance of time, a warning is visualized to a security administrator through the visualization module. Then a security administrator may take immediate actions for solving the issue due to which some NetFlows may get dropped.
Guide to AI Cybersecurity Measures
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 data collection module to the data storage and analysis module. The security measures incorporated differentiate from system to system.
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.
AI based 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.
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.
Holistic AI Security 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.
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