What are the most common types of network attacks and anomalies that machine learning can detect and prevent?

1 Denial-of-service attacks

A denial-of-service (DoS) attack is an attempt to disrupt the normal functioning of a network or a server by overwhelming it with traffic or requests. A distributed denial-of-service (DDoS) attack is a more advanced form of DoS that involves multiple compromised devices or sources. Machine learning can help detect and prevent DoS and DDoS attacks by analyzing network traffic and identifying abnormal patterns, such as spikes, floods, or spoofed packets. Machine learning can also help filter out malicious traffic and prioritize legitimate requests, reducing the impact of the attack.

2 Malware infections

Malware is any software that is designed to harm or compromise a network, a device, or a user. Malware can include viruses, worms, trojans, ransomware, spyware, adware, and more. Machine learning can help detect and prevent malware infections by scanning files and applications for suspicious behavior, signatures, or anomalies. Machine learning can also help classify and isolate malware samples, and update the security systems with new information and rules. Machine learning can also help prevent malware from spreading or communicating with external servers, by blocking or restricting network connections.

3 Intrusion detection and prevention

An intrusion is any unauthorized or malicious access to a network or a system. An intrusion can result in data theft, damage, or sabotage. An intrusion detection system (IDS) is a tool that monitors network traffic and alerts the administrators of any potential or actual intrusions. An intrusion prevention system (IPS) is a tool that not only detects but also blocks or mitigates intrusions. Machine learning can help improve the accuracy and efficiency of IDS and IPS by learning from historical and real-time data, and adapting to new or evolving threats. Machine learning can also help reduce false positives and negatives, and provide more context and insights into the nature and source of the intrusion.

4 Insider threats

An insider threat is a threat that originates from within the organization, such as an employee, a contractor, a partner, or a customer. An insider threat can be intentional or unintentional, and can involve data leakage, fraud, sabotage, or espionage. Machine learning can help detect and prevent insider threats by analyzing user behavior and activity, and identifying deviations from normal or expected patterns. Machine learning can also help flag or alert suspicious or risky actions, such as accessing sensitive information, transferring large files, or using unauthorized devices or applications.

5 Network anomalies

A network anomaly is any deviation from the normal or expected behavior or performance of a network or a system. A network anomaly can indicate a potential or existing problem, such as a misconfiguration, a malfunction, a bottleneck, or an attack. Machine learning can help detect and prevent network anomalies by establishing a baseline of normal network activity and performance, and monitoring for any changes or variations. Machine learning can also help diagnose and troubleshoot network anomalies, by providing root cause analysis, recommendations, or solutions.

Machine learning is a powerful and versatile tool that can enhance your network security capabilities and resilience. By applying machine learning to network traffic analysis, you can detect and prevent various types of network attacks and anomalies, and protect your network assets and data from cyber threats.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?