With the advancement of technology in today's digitally evolving world, Cyber threats are also getting more advanced, sophisticated, and persistent than ever before. Cyber threat hunting proactively searches for hidden and dormant threats that can bypass standard defense systems. Cyber threat hunting is an indispensable technique to identify and neutralize threats even before they can cause damage.

Traditional reactive methods basically depend on alerts, signature-based defenses, or automated systems. Advanced threats such as Zero-day exploits, advanced persistent threats (APTs), and insider attacks can easily bypass traditional security approaches. Cybercriminals use advanced tactics, move slowly within a network, and make it difficult to catch them by using traditional security tools.

Cyber threat hunters work to detect advanced threats by detecting anomalous behavior and actively searching for potential dangers. Check out our latest tutorial on basic setup for cyber threat hunting using open-source tools.

Cyberhunting can enhance the overall security posture of any organization and reduce the chances of upcoming potential breaches. Here are some essential techniques used for cyber threat hunting:

Key Technique 1: Network Traffic Analysis

Network traffic analysis is the data monitoring process that helps to understand network behavior and performance. It helps to detect and prevent the malware from spreading into the network. Security professionals can use tools like WireShark to analyze network packets. Wireshark sniffs and captures all data packets and helps cyber threat hunters review the traffic for any malicious activity.

Wireshark for Anomaly Detection:

Wireshark captures, filters, and analyzes network packets to spot anomalous traffic patterns. Wireshark helps to spot irregular patterns that could indicate security issues. We can detect anomalous traffic patterns by checking for Unusual IP Traffic e.g. suspicious IP addresses, Unusual Port Activity like non-standard ports for known services, and Unusual Protocols.

Malformed Packets or Unusual Packet Sizes can indicate anomalous network traffic. Malformed packets indicate attacks such as unexpected flags in TCP headers, while unusual-sized packets indicate denial-of-service (DoS) attacks or data exfiltration.

Protip: Using Malwarebytes can help protect your systems during threat detection. In case of any suspicious activity, it can scan and mitigate any potential malware before it damages the systems. NordVPN can safeguard the network traffic from monitoring; using these together provides robust security solutions against cyber threats.

Key Technique 2: Log Analysis

The process of reviewing, interpreting, and analyzing logs generated by systems, networks, or applications is called log analysis. This is a key technique used in cyber threat hunting. These logs or digital footprints contain valuable information about our IT environment from identifying threats to performance issues. For example, multiple failed login attempts from a single IP can indicate a cyber threat. By using log analysis we can detect these threats early and mitigate any potential damages before they can occur.

Log Analysis using Graylog:

Log analysis can be done manually or by using tools like Graylog. Using automated tools can increase the efficiency of the process and help to manage a large number of logs in a short time. Graylog is an open-source log management tool that provides real-time log analysis. It collects and analyzes log data from a wide range of network devices, and servers. It monitors log data in real-time and triggers alerts. Graylog detects security breaches and suspicious activities by analyzing logs for anomalies.

Key Technique 3: Behavioral Analytics

Behavioral analytics is a real-time evaluation of a user and system’s actions to detect anomalies or suspicious activities. It uses Network traffic, Database activity, system events, and user activity to analyze deviations from the baseline that if present indicate a security threat or vulnerability.

Splunk for Behavioral Analytics:

Splunk is an advanced tool for peer group analysis, devising behavioral baselines, uncovering APT, and detecting malware infections. No human intervention is needed during the process. It uses Machine learning algorithms and advanced analytics techniques to provide real-time behavioral analytics. This can detect any unusual activity in the system or user behavior that can indicate any APT or insider threat, helping you to mitigate the problem before it causes any potential damage.

Key Technique 4: Machine Learning & AI

Machine learning algorithms and AI can identify the threat patterns that can easily evade traditional defenses. ML models can be used to identify zero-day attacks or previously unknown threats that lack recognizable signatures used by traditional threat detection methods.

Machine Learning & AI vs Traditional Threat Detection:

Machine Learning & AI models can detect unusual activities from a vast amount of network data in real-time. By learning normal behavior patterns it flags anomalies that could indicate potential threats. Unlike traditional defenses AI powered systems allow dynamic threat detection by learning from past threats and adapting new patterns.

Machine learning and AI models can even detect and flag small subtle changes in user or system behavior that can indicate a potential threat. These small changes can be overlooked by traditional threat detection methods and can cause large damage.

ML and AI help us to stay ahead of the cyber adversaries by reducing the detection time and improving response to advanced threats.

Online Learning Resources:

Continuous learning in the field of cyber security can help you to stay ahead of the emerging threats. Here are a few online courses from reliable platforms that can help you in the learning journey:

1.    The Complete Cyber Security Course (StationX)

2.    Harvard’s CS50’s Introduction to Artificial Intelligence with Python(edX)

Conclusion:

With the advancement of technology cyber attackers have become more advanced and sophisticated. Traditional threat detection methods are insufficient to detect these advanced threats. Cyber threat hunting combines advanced techniques like network traffic analysis, log analysis, behavioral analytics, and machine learning & AI for a more proactive approach.

All these techniques combined provide robust protection by enabling threat hunters to detect anomalies, and hidden threats and respond to new emerging threats more efficiently.  With the help of these cyber threat-hunting methods, organizations can stay ahead of cyber adversaries by quickly identifying potential threats before they can cause significa