We're going to set out on a mind-blowing tour around network security. Upon considering the nearness and risk posed by cyber threats in this epoch, it is important to prevent the threats so that they do not cause irreversible damage within the network. This three-part article series will take you through the process of developing a network anomaly detection system using the Spring Boot framework in a robust manner. The series is organized as follows:

By the end of this series, you will have acquired quite a lot of knowledge in developing a strong-based network anomaly detection system using Spring Boot and making use of technologies to improve your organization's security level.

As an initial step in the development of our network anomaly detection system, let's develop a Spring Boot project in which we can make use of all the needed dependencies. In this case, Maven will be our build tool and we will add the following major dependencies:

Let's go ahead and add these important dependencies to our pom.xml file.

Let's proceed to define our core data model. This crucial component will serve as the foundation for capturing and structuring essential information from our network traffic.

Let us now direct our attention to building the heart of this system, which is the tool that will handle and analyze network traffic structures. This important unit will use specific algorithms to carry out the normal operations of searching for patterns in the collected network packets and spotting irregular ones that suggest possible threats. Saving this engine in this way will enable it to perform efficiently even when dealing with high data traffic.

The anomaly detection system has been designed with a working module that incorporates a comprehensive function to supervise the whole process of detection. Through this multipronged strategy, a large number of parallelized algorithms are integrated, allowing analysis of the system's real-time security effectiveness against a much wider perspective of even possible attacks.

This function first updates our model with respect to the most recent network data followed by empirical analysis of this data based on several anomaly criteria using boolean logic.

The function returns true if such packet size departs from these limits (i.e., is either too large or too small), signifying a possibility of an anomaly. This simple boolean check eliminates millions of packets that fall outside normal bounds, which can imply all sorts of wrong activities or problems on the network, including DoS or DDoS, data leakage, fragmentation, and configuration errors. In filling this function into our broader anomaly detection scheme, we improve the chances of our system promptly raising an alarm over suspicious network traffic handling, which may be investigated or answered in an automated manner.

This code establishes a functional framework for the network traffic protocol. It also has a procedure to raise alerts where any network communication employs the use of protocols other than those that have been flagged. The application provides a means of filtering management of illegal activities or even attempts to conduct illegal network operations.

The identified threat function is quite important in preventing network flooding or any Denial of Service (DoS) attack, if any. This function assesses the frequency of packets from a particular IP address by accessing historical files of that source IP and checking the dispersion of packets to the given maximum level of packets defined as the maximum possible. When the rate exceeds this threshold, the rate function returns true as an indication that such traffic is anomalous.

It involves a more granular function for evaluating network connections based on the use of malicious ports. It captures the destination port number from the given object and matches it with any of the suspicious ports that are present within a predefined threshold (, , ). These ports have also been linked to malware, backdoors, and hacking software. Therefore this function helps prevent connecting to such ports which might lead to security breaches, invasion, and malware infection.

It carries out this by taking the most likely current time of the operating system and relating it with the time the given object was timestamped. If a packet with a object is stamped for a future temporal instance, then the function returns true as an indicator of a probable anomalous event.

Let's proceed with implementing our data access layer by creating a repository interface. This interface will leverage the powerful capabilities of Spring Data JPA to streamline our database operations. It can perform basic CRUD operations by extending . We've also added a custom query to retrieve the most recent network data entries. The interface includes a custom query method, which uses the annotation to specify a JPQL query. This method retrieves a specified number of entries, ordered by their timestamps in descending order (most recent first).

The method is a key component in our network anomaly detection system, designed to retrieve the most recent network traffic data. It takes an integer parameter to specify the number of entries to fetch.

The method provides a comprehensive retrieval mechanism for our network traffic data. It utilizes the method, which is automatically provided by Spring Data JPA. This method fetches all entries stored in the database, returning them as a List.

The method invokes the operation provided by the , which is part of Spring Data JPA's standard CRUD operations. This method efficiently removes all entries from the database, effectively clearing the entire collection of stored network traffic data.

Now, let's create the monitoring class. This will keep a constant eye on our network, collecting data and raising alarms when needed.

The below method, scheduled to run every 60 seconds, collects network data, checks for anomalies, logs the results, and saves the data.

The below method simulates the collection of network traffic data in a controlled environment. It creates and populates object with mock values for various network attributes such as source and destination IP addresses, packet size, protocol, packet loss, latency, throughput, and timestamp.

The system includes a REST API, allowing us to simulate anomalies easily. This API provides a user-friendly interface for testing and experimentation. However, in real-world applications, such an API is typically unnecessary. Anomaly detection in real-world scenarios focuses on continuous, real-time network monitoring. Rather than relying on external inputs to trigger anomalies, the system automatically detects unusual patterns as they occur. This approach ensures timely detection and response to potential threats. The REST API, while useful for testing, would not play a central role in production environments. In practice, the system would function autonomously to maintain network security.

This function simulates a network anomaly check. It takes network data as input and analyzes it to detect any anomalies. After processing, it logs the result, saves the data, and returns a response. The response includes the status of the anomaly, the data itself, and a message that describes the outcome.

This function retrieves recent network data. It accepts an optional limit parameter (defaulting to 10) to specify the number of recent entries to fetch.

This function clears all stored network data. It calls the method to remove all entries from the database. It returns a confirmation message upon successful deletion.

Now let us proceed in configuring our scheduling component, whose role will be to ensure any monitoring work is done in a timely manner. This configuration component utilizes Spring scheduling infrastructure to perform time-critical task management and execution.

This class is a Spring configuration component that serves the purpose of enabling scheduling. It is annotated with and thus laying down the basic structure of the application for the application's scheduled tasks

To rigorously test our API's capabilities, we'll employ Postman to emulate various network scenarios:

To retrieve the most recent network activity logs, we can utilize the following API endpoint, which fetches data from our in-memory database.

Using the potent features of Spring Boot and the recommended approaches in network security, a base layout can be created with the aim of securing the network against its dangers. In filling this necessary void, we will improve the system so as to:

These improvements will be presented in later parts of the guide.