About this Research Topic
Today most organizations are exposed to serious cybersecurity threats. Data leaks, Breach of Trust and Privacy, and Ransomware are some of the most common attacks reported every day around the world by many well-known organizations. The application of advanced techniques is making these cyber-attacks stealthy and undetectable. The AI-Powered technique used for reconnaissance and identifying the target is a common approach used by the attacker. Some of the attacks like Advanced Persistent Threats (APTs) remain undetected for an extended period. Moreover, with the proliferation of the Internet, Cellular networks, Smartphones, IoT, social media, etc., today we are generating more data than ever before. The increased number of users, devices, and mammoth size of data poses many challenges for real-time threat identification and prediction.
Defense against some of the stealthy cyberattacks such as APT which goes undetected for longer periods requires extensive logs analysis from various sources. Data sources such as Server Logs, IDS Logs, Firewall logs, etc. hold vital information which can be used for the identification and prediction of complex cyber-attacks. However, it’s a challenging task for any large organizations (particularly multinational companies and govt. organizations) to collect and analyze the log data for an extended period (6 months to 1 year) from thousands and thousands of hosts spread across the geographical region. The amount of information stored in these logs is extensively large.
Big data analytical techniques are one of the promising solutions which can help organizations to analyze these logs in an effective manner to defend against complex cyber-attack and APT. Big Data Analytics is a technological breakthrough to build an effective cyber threat intelligence system to combat with advance cyber threats. Organizations across the world are building a system to effectively analyze large-scale data for complex threat intelligence. However, there are many challenges.
All the security devices such as IDS, Firewalls, etc., have different log formats. There is no standard log format universally followed by all the devices. Collection of the logs in different formats and analysis is a challenging task. The application of Big-Data with Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) approaches seems to be a very promising solution but it’s a resource-intensive technique. Heterogeneous Log formats, Log storage, real-time processing, and scalability are some of the major hurdles to building an effective cyber threat intelligence system to combat advanced persistent threats for the large-scale network. Developing a cyber threat intelligence system using big data for large scale networks and organizations, requires extensive research work on various topics such as:
• Automation of the Entire Workflow using AI and ML Techniques
• Relevant Incident Identification
• Real-Time Process and Incident Identification
• Data Security and Privacy
• Statutory Compliance
• Resource Constraint and Scalability
• Heterogeneous Log Format etc.
Defense against some of the stealthy cyberattacks such as APT which goes undetected for longer periods requires extensive logs analysis from various sources. Data sources such as Server Logs, IDS Logs, Firewall logs, etc. hold vital information which can be used for the identification and prediction of complex cyber-attacks. However, it’s a challenging task for any large organizations (particularly multinational companies and govt. organizations) to collect and analyze the log data for an extended period (6 months to 1 year) from thousands and thousands of hosts spread across the geographical region. The amount of information stored in these logs is extensively large.
Big data analytical techniques are one of the promising solutions which can help organizations to analyze these logs in an effective manner to defend against complex cyber-attack and APT. Big Data Analytics is a technological breakthrough to build an effective cyber threat intelligence system to combat with advance cyber threats. Organizations across the world are building a system to effectively analyze large-scale data for complex threat intelligence. However, there are many challenges.
All the security devices such as IDS, Firewalls, etc., have different log formats. There is no standard log format universally followed by all the devices. Collection of the logs in different formats and analysis is a challenging task. The application of Big-Data with Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) approaches seems to be a very promising solution but it’s a resource-intensive technique. Heterogeneous Log formats, Log storage, real-time processing, and scalability are some of the major hurdles to building an effective cyber threat intelligence system to combat advanced persistent threats for the large-scale network. Developing a cyber threat intelligence system using big data for large scale networks and organizations, requires extensive research work on various topics such as:
• Automation of the Entire Workflow using AI and ML Techniques
• Relevant Incident Identification
• Real-Time Process and Incident Identification
• Data Security and Privacy
• Statutory Compliance
• Resource Constraint and Scalability
• Heterogeneous Log Format etc.
Keywords: Big Data, Cybersecurity, Cyber Intelligence, Privacy, Cyber Defense, Big Data Analytics
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