Table of Contents
What is Network Security Analytics?
Network Security Analytics, often referred to as Network Security Monitoring (NSM) or Network Traffic Analysis (NTA), is a field of cybersecurity focused on the collection, analysis, and interpretation of network traffic and data to detect and respond to security threats and incidents.
It plays a crucial role in identifying and mitigating cyber threats, as well as understanding and improving an organization’s overall security posture.
How does it work?
NSA is built up of several components: A collector (which 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.
Key Aspects of Network Security Analytics
1) Data collection and normalization: When analyzing big data, it is crucial to de-duplicate (or in general, normalize) the data to minimize false positives that can occur by looking at repetitive strings (for example, 404 errors).
For normalized traffic logs the classifier uses clustering algorithms that group 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.
4) Traffic Analysis: Network traffic is analyzed to identify patterns and anomalies that may indicate malicious activity. This includes monitoring network flows, packet captures, and payload analysis to detect unusual or suspicious behavior.
5) Behavioral Analysis: Network Security Analytics tools often employ behavioral analysis techniques to establish a baseline of normal network behavior. Any deviations from this baseline can be flagged as potential security threats.
6) Alert Generation: When suspicious activity is detected, Network Security Analytics systems generate alerts and notifications for security personnel to investigate further. These alerts may be based on predefined rules or machine learning algorithms that can identify novel threats.
7) Incident Response: Network Security Analytics is an integral part of an organization’s incident response process. It helps security teams quickly identify and respond to security incidents, minimizing the impact of potential breaches.
8) Forensics and Investigation: When an incident occurs, Network Security Analytics can provide valuable data for post-incident analysis and forensics.
This information can help security teams understand the scope and impact of a breach and develop strategies to prevent similar incidents in the future.
9) Threat Intelligence Integration: Many Network Security Analytics solutions integrate with threat intelligence feeds to enhance their ability to detect known threats and indicators of compromise.
10) Compliance and Reporting: Network Security Analytics can also assist organizations in meeting regulatory compliance requirements by providing detailed logs and reports of network activity.
11) Machine Learning and AI: Advanced Network Security Analytics solutions leverage machine learning and artificial intelligence to improve threat detection accuracy.
These technologies can identify complex and evolving threats that may go unnoticed by traditional rule-based systems.
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, the 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 through Data Analysis
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.