An autonomous cybersecurity device for anomaly prediction

Abstract

An autonomous cybersecurity device for anomaly prediction, including: a. a data acquisition module for capturing network traffic data; b. a processing unit for analyzing the collected data; c. an anomaly detection algorithm for identifying anomalous patterns in the data; d. a warning system to react to detected anomalies.

Description

TECHNICAL AREA OF INVENTION

[0001] The present invention relates to the field of cybersecurity and in particular to devices for detecting and predicting anomalies in network traffic in order to prevent security breaches. BACKGROUND OF THE INVENTION

[0002] The following background information relates to the present disclosure, but does not necessarily represent the state of the art.

[0003] In the digital age, cybersecurity has become a critical issue for businesses worldwide. The increasing interconnectedness of devices and the expansion of network infrastructure have significantly enlarged the attack surface for cyber threats. Anomaly detection is a fundamental aspect of cybersecurity. It aims to identify unusual patterns or behaviors that may indicate potential security breaches such as malware intrusion, unauthorized access, or data exfiltration. Despite its importance, current anomaly detection approaches struggle to keep pace with the evolution of cyber threats and pose a persistent challenge to network protection.

[0004] A major problem with anomaly detection is the frequency of false alarms, where harmless activities are mistakenly flagged as threats. This overwhelms security personnel, leads to resource inefficiencies, and increases the risk of missing genuine attacks amidst the noise. Traditional systems often rely on static, rule-based methods based on predefined attack signatures. While these systems are effective against known threats, they fail against novel or zero-day attacks without established patterns, leaving networks vulnerable to sophisticated attackers.

[0005] The situation is further complicated by the fact that many existing solutions rely heavily on human intervention. Security teams often have to manually configure systems, update detection rules, and respond to alerts, leading to delays and the risk of human error. While machine learning-based systems show promise, they frequently require large, labeled datasets for training, which are costly and time-consuming to acquire. Furthermore, these systems can be resource-intensive, making them unsuitable for real-time deployment in large or resource-constrained environments.

[0006] The cumulative effect of these shortcomings highlights a critical gap in cybersecurity technology. Organizations need a solution that addresses the dynamic nature of threats without overburdening human operators or consuming unavailable data resources. A system that efficiently detects anomalies in real time, minimizes false alarms, and adapts to new attack vectors remains elusive but is increasingly needed given the growing complexity and frequency of cyber threats. The objective of the present invention is therefore to provide an autonomous cybersecurity device that addresses these challenges through enhanced anomaly prediction capabilities.

[0007] The current state of the art / existing technology has several disadvantages. Therefore, there has long been a need in this area. Objective of the invention

[0008] The main objective of the present invention is to provide an autonomous cybersecurity device for anomaly prediction.

[0009] Provision of a device that can autonomously detect and predict anomalies in network traffic with high accuracy and minimal false alarms.

[0010] To reduce the need for human intervention in the anomaly detection process, thereby increasing efficiency and reducing the risk of errors.

[0011] These and other objectives and features of the present invention will become clear from the further disclosure in the detailed description below. Summary of the invention

[0012] The present invention provides an autonomous cybersecurity device for anomaly prediction. The device is designed to continuously monitor network traffic, analyze data patterns, and predict potential security anomalies using advanced algorithms. By integrating state-of-the-art machine learning techniques, it offers a self-contained solution that adapts to new threats and operates with minimal human intervention. The main objectives are to improve the accuracy of anomaly detection and optimize the security process. Key advantages include a reduction in false alarms, real-time functionality, and improved adaptability compared to conventional systems. DETAILED DESCRIPTION OF THE INVENTION

[0013] In the present description and the following claims, the meanings of "ein", "eine", and "der, die, das" each include the plural, unless the context clearly indicates otherwise. In the present description, the meaning of "in" also includes "in" and "auf", unless the context clearly indicates otherwise.

[0014] The present invention relates to an autonomous cybersecurity device for anomaly prediction.

[0015] The autonomous cybersecurity device was developed to overcome the shortcomings of traditional anomaly detection systems. It offers a standalone solution that accurately predicts security threats with minimal human intervention. It utilizes sophisticated machine learning algorithms to monitor network traffic in real time, detect deviations from normal behavior, and initiate appropriate countermeasures, thus ensuring robust protection against cyber threats. STRUCTURE AND COMPONENTS

[0016] Data acquisition module: This component collects network traffic data from various sources such as routers, switches and firewalls and provides a comprehensive dataset for analysis.

[0017] Processing unit: This unit serves as the core of the device and processes incoming data using high-performance processors and sufficient memory to efficiently handle large data traffic.

[0018] Anomaly detection algorithm: A proprietary machine learning algorithm that uses unsupervised learning to detect anomalies. It continuously refines its model based on incoming data to identify emerging threat patterns.

[0019] Warning system: If this system detects an anomaly, it either notifies security personnel or performs automated responses, such as isolating compromised network segments. WORK

[0020] Data acquisition: The data acquisition module captures network traffic and transmits it to the processing unit.

[0021] Data preprocessing: The processing unit normalizes and filters the data to ensure compatibility with the anomaly detection algorithm.

[0022] Anomaly detection: The algorithm analyzes the pre-processed data and identifies deviations that indicate potential threats.

[0023] Response: The warning system is activated and either issues notifications or implements predefined corrective actions.

[0024] This process runs continuously to ensure real-time network protection. EMBODIMENT

[0025] In one embodiment, the device is deployed at the gateway of a corporate network and monitors all incoming and outgoing data traffic. It detects anomalies such as unexpected data spikes that might indicate a DDoS attack and responds by throttling the suspicious traffic. In another embodiment, the device operates within a cloud infrastructure and protects virtualized data traffic from threats such as unauthorized access attempts by analyzing patterns across distributed nodes. TECHNICAL ADVANTAGES COMPARED TO THE STATE OF THE ART

[0026] The device offers significant improvements over existing technologies: • Autonomy: It functions independently, so no constant human monitoring is required. • Adaptability: The algorithm evolves with new data, thus ensuring effectiveness against novel threats. • Efficiency: Optimized for real-time performance, it minimizes latency and resource usage. • Accuracy: Enhanced detection reduces false alarms and improves the reliability of threat identification.

Claims

[1] An autonomous cybersecurity anomaly prediction device comprising: a. a data acquisition module for capturing network traffic data; b. a processing unit for analyzing the collected data; c. an anomaly detection algorithm for identifying anomalous patterns in the data; d. a warning system to react to detected anomalies. [2] Device according to claim 1, wherein the anomaly detection algorithm uses machine learning to adapt to evolving threats. [3] Device according to claim 1, wherein the processing unit comprises high-performance processors and memory for real-time analysis. [4] Device according to claim 1, wherein the warning system initiates automated responses to contain detected threats.