Latest technologies have posed new cybersecurity risks to organizations. Even after using advanced defense strategies, security professionals are failing to cope with it. By merging the capabilities of AI with the skills of security experts we can effectively tackle this situation. Organizations can get an instant insight into a security issue and the overall response time can be reduced. Blending AI with Cyber Security is a new security trend.
Organizations are prone to type of attacks such as:
- Transaction frauds
- Data exfiltration
- Insider threats
- Advanced malware
- Account acquisition
- Encrypted attacks
- Run-time application exploitation
- Network lateral movement
Benefits of Having an AI Cybersecurity Detection System
- False positives can be effectively handled by rule-based detection systems
- Efficient hunting of threats
- Providing a complete analysis of threat incidents and its investigation
- Threat prediction
- Elevating security systems by examining the root cause of the attacks in affected systems
- Security monitoring
Core Capabilities of an AI-based Cybersecurity Tool
When organizations choose AI-based cybersecurity tools for its operations, it should have 3 core capabilities:
- Network security
- Cloud security
- IoT security
- Autonomous security
- Threat prediction
- Social network security
- ML for cyber
- Insider attack detection
- Security analytics
- FinTech and blockchain
- Risk and decision-making
- Spam detection
- Data privacy
AI Cybersecurity Analytics for Enterprises
- Perspective Analytics: Determining required actions for analysis or response
- Diagnostic Analytics: Evaluating modus operandi and RCA (Root Cause Analysis) of incidents and attacks
- Predictive Analytics: Determining probability of upcoming threats and users/assets are getting impacted
- Detective Analytics: Identifying advanced malware, lateral movement, bypassed threats, hidden and unknown threats
- Descriptive Analytics: Collecting latest status and performance of metrics and trends
AI-based Risk Management Approach
- Collect appropriate data
- Depict a learning application
- Customize machine learning
- Analyze cyber threat
- Blueprint a security problem
Application of Machine Learning in Cybersecurity
- Classification: Determines reliability of a security event and which group it belongs to. e.g. Naive Bayes Classifier, KNN, SVM, SOM, etc.
- Pattern Matching: Identifies whether patterns are malicious or not and detects indicators in large datasets. e.g. Boyer Moore, KMP, Entropy Function, etc.
- Regression: Determines security event trends and behavior prediction of machines and users. e.g. Linear Regression, Multivariate Regression, Logistic Regression, etc.
- Deep Learning: Attacks are hunted with automated playbooks created from the history of actions. e.g. Deep Boltzmann Machine, Deep Belief Networks, etc.
- Association Rules: Generate alerts if any similar attackers and attacks are detected. e.g. Apriori, Eclat, etc.
- Clustering: Determines anomalies and outliers. e.g. K-means Clustering, Hierarchical Clustering, etc.
- AI using Neuroscience: Augmenting human intelligence – by learning from interactions it can proactively detect threats, analyze them, and provide actionable insights. e.g. Cognitive security
Role of Analytics in Cybersecurity
Analytics starts with associated data collection and the effectiveness of the analysis depends on the quality of data collected.
Various data sources are:
- User Data: Collect and analyze user access and activities from Active Directory, Proxy, VPN, and applications
- Application Data: Collect and analyze calls, data exchange, commands along with the WAF data for installing the agents on the application
- Endpoint Data: Analyze internal endpoints such as files, processes, memory, registry, connections, and many more by installing agents
- Network Data: Collect and analyze the packets, net flows, DNS, and IPS data by installing the network appliance
Analytics can contribute to the performance and accuracy quality attributes of a cybersecurity system.
Contribution to Performance
- For removing the subset of event data that is not useful for the detection process
- Feature extraction and feature selection process can enable parallel processing that makes the selection and extraction process faster
- Helps to implement a data cutoff so that security events that emerge after the predefined limit can be avoided from the attack detection process
- Data collector entity collects security events data from various sources and stores it into a data storage entity. Helps to partition the data into fixed-size blocks for parallel processing
- Helps to apply ML and DL algorithms on data to enable AI for cybersecurity
Contribution to Accuracy
- The alert analysis module uses pre-processed security events data for analyzing the attacks and forwards the data to the alert verification module for identifying false positives. Identified false positives are neglected at this stage. And alert correlation module logically links the alerts and the final result will help in responding to the attack
- Signature-based detection modules can analyze imported data to identify patterns of the attack. Pre-designed rules are stored on a database and used to identify patterns of the attack. An alert will be generated if any match is identified
- An anomaly-based detection module analyzes data using ML-based algorithms to detect any deviations from normal behavior. An alert is generated if any anomaly is detected
- Algorithms for attack detection
- Consolidating multiple detection methods
What does a typical AI-based cybersecurity system look like?
The system consists of:
- Nodes: Collect different types of security events data from different sectors. e.g. Network activity, Database Activity, Application Activity, and User Activity
- Security Measures: Ensures a secure transfer process of data from data collection module to data storage and analysis module. Incorporated security measures differentiate from system to system
- Encryption and public key infrastructure can be enabled to secure data transfer
- The data storage and analysis module apply data analytic operations on data to perform analysis processes to detect attacks
- Results will be published through a visualization module
Well-known tools for AI in cybersecurity are Symantec’s Targeted Attack Analytics, Sophos’ Intercept X tool, IBM QRadar Advisor, etc.
Organizations need an effective network security analytics system to quickly identify and resolve evolving threats. The system must be able to use a combination of methods and it starts by collecting the right data for providing comprehensive visibility and using analytical techniques. AI can help us implement such systems based on organizational needs.