Breaking the cyber kill chain with AI
We’ve finished our two series on the role of AI in malicious attacks and sophisticated defenses. In this blog, we’ll explore the cyber kill chain framework and the power of adding AI to the mix.
What is the cyber kill chain framework?
People often use the terms ‘cyber kill chain’ and ‘cyber kill chain framework’ interchangeably, but there are subtle differences between the two. The Cyber Kill Chain® is a model developed by Lockheed Martin in 2011. It describes the structure and action sequence of an attack, from identification of the target to the execution of the mission. It is inspired by the military ‘kill chain’ doctrine, which outlines the steps involved in identifying and neutralizing a target.
This model provides a high-level overview of the attack process in seven distinct stages:
- Reconnaissance: Gathering information about the target.
- Weaponization: Creating a malicious payload.
- Delivery: Transmitting the payload to the target.
- Exploitation: Exploiting a vulnerability to execute the payload.
- Installation: Installing malware on the target system.
- Command and Control (C2): Establishing communication with the compromised system.
- Actions on Objectives: Performing the intended malicious activities, such as data theft or system disruption.
We can describe the ‘cyber kill chain framework’ as a living, operationalized version of the cyber kill chain. This is a collaborative effort by the cybersecurity community to expand the Lockheed Martin model with more details and aspects of modern cyberattacks. In order to fully defend each stage of the Cyber Kill Chain, the expanded framework adds four components to each:
- Detection Mechanisms: Tools and technologies to identify threats at each stage of the kill chain, such as intrusion detection systems (IDS), endpoint detection and response (EDR), and threat intelligence platforms.
- Analysis Processes: Methods for analyzing threats and understanding attacker behavior, including forensic analysis, behavioral analytics, and machine learning models.
- Response Strategies: Defined procedures for responding to threats, such as incident response plans, automated response mechanisms, and threat hunting activities.
- Continuous Improvement: Ongoing efforts to refine and enhance the framework based on emerging threats and lessons learned from previous incidents.
Companies can customize a framework to the environment, risk factors, attack type, and so on. Let’s illustrate this by applying the framework to two ransomware attack scenarios. In the table below, we compare an attack through a firewall intrusion with an attack through a malicious email attachment:
Firewall intrusion |
Malicious email attachment |
Stage 1: Reconnaissance |
|
Detection: Identifying scanning activities targeting firewall vulnerabilities. Analysis: Determining that the scanning is aimed at finding open ports and weaknesses in the firewall. Response: Blocking IP addresses associated with the scanning and updating firewall rules to close vulnerabilities. Continuous Improvement: Enhancing threat intelligence to recognize similar scanning patterns in the future. |
Detection: Identifying phishing emails and suspicious email activity. Analysis: Determining that the emails are aimed at delivering malicious attachments. Response: Blocking suspicious email addresses and domains. Continuous Improvement: Enhancing email filtering and threat intelligence. |
Stage 2: Weaponization |
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Detection: Noticing the creation of a malicious payload designed to exploit the firewall vulnerability. Analysis: Understanding that the payload is designed to deliver ransomware once inside the network. Response: Developing countermeasures for the identified payload and sharing intelligence with security teams. Continuous Improvement: Updating malware signatures and defense strategies based on new payload information. |
Detection: Noticing the creation of a malicious attachment. Analysis: Understanding the attachment is designed to deliver ransomware. Response: Developing countermeasures for identified attachments. Continuous Improvement: Updating malware signatures and defense strategies. |
Stage 3: Delivery |
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Detection: Detecting the transmission of the malicious payload through an open port in the firewall. Analysis: Analyzing the delivery method, which could be through spear-phishing emails or direct network connection. Response: Blocking the delivery attempt and notifying the affected users or systems. Continuous Improvement: Strengthening email security and network monitoring to detect future delivery attempts. |
Detection: Detecting the email with the malicious attachment. Analysis: Analyzing the email's delivery method and its phishing characteristics. Response: Blocking the email and quarantining the attachment. Continuous Improvement: Strengthening email security and phishing awareness training. |
Stage 4: Exploitation |
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Detection: Identifying exploitation attempts targeting the firewall vulnerability. Analysis: Assessing the nature of the exploit and its impact on the firewall and network. Response: Patching the firewall vulnerability and isolating affected systems. Continuous Improvement: Improving vulnerability management and patching processes to reduce exposure. |
Detection: Identifying the exploitation attempt when the attachment is opened. Analysis: Assessing the exploit and how it compromises the user's system. Response: Isolating the affected system and notifying the user. Continuous Improvement: Improving user training and patching processes to reduce vulnerabilities. |
Stage 5: Installation |
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Detection: Detecting the installation of ransomware on a compromised system. Analysis: Understanding the ransomware's behavior and how it spreads within the network. Response: Removing the ransomware, restoring affected systems from backups, and using sandboxing to analyze the malware. Continuous Improvement: Enhancing endpoint protection and monitoring for similar installation attempts in the future. |
Detection: Detecting the installation of ransomware on the user's system. Analysis: Understanding the ransomware's behavior and spread within the network. Response: Removing ransomware and restoring systems from backups. Continuous Improvement: Enhancing endpoint protection and monitoring. |
Stage 6: Command and Control (C2) |
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Detection: Identifying C2 communications between the compromised system and the attacker's server. Analysis: Deciphering the C2 protocols and commands used by the attacker. Response: Blocking C2 traffic, disrupting the communication channel, and notifying relevant stakeholders. Continuous Improvement: Strengthening network monitoring and C2 detection capabilities to prevent future incidents. |
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Stage 7: Actions and Objectives |
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Detection: Detecting the ransomware's attempts to encrypt critical data and files. Analysis: Understanding the ransomware's encryption methods and identifying the affected data. Response: Containing the attack, stopping the encryption process, and recovering data from secure backups. Continuous Improvement: Implementing enhanced data protection measures like regular backups and improved access controls. |
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Our recent e-book on the role of AI in cybersecurity provides an example of a ransomware cyber kill chain framework.
In this high-level overview, the four framework components increasingly overlap as the attack proceeds, and there are no significant differences in the last two stages. Frameworks make it easier to visualize attacks and defenses, so you can build a comprehensive, company-wide security strategy.
AI and the cyber kill chain framework
Now that we’ve broken down each component in each stage of the framework, we can see how AI capabilities map directly to each function. We’ll limit this example to the first two stages of the framework:
Stage 1: Reconnaissance
- Detection: AI-Powered Intrusion Detection Systems (IDS) use machine learning to identify unusual scanning activities and network traffic anomalies.
- Analysis: AI Models are used to classify and prioritize potential reconnaissance threats. These models are informed by integrated threat intelligence that correlates threat data from multiple sources.
- Response: AI-driven automation implements countermeasures, like blocking suspicious IP addresses and updating firewall rules in real-time
- Continuous Improvement: Machine learning algorithms and adaptive models refine detection capabilities and update the models based on feedback from prior reconnaissance attempts.
Stage 2: Weaponization
- Detection: AI-Enhanced Malware Analysis Sandboxes detect suspicious files and analyze their behavior. Code analysis capabilities use machine learning to identify malicious code patterns.
- Analysis: Deep learning models simulate attacks to predict payload behaviors, and threat intelligence platforms update data across all stages in real-time.
- Response: Automated Systems distribute countermeasures across the network and threat intelligence capabilities support the updates to security protocols..
- Continuous Improvement: AI Feedback Loops learn from new malware samples to refine detection and prevention algorithms. Threat intelligence is used to update Adaptive Security Measures.
Shifting Left
Integrating AI with a comprehensive cyber kill chain framework improves the odds of earlier detection and disruption. An attack that is detected at stage 6 in the framework is going to be more difficult and expensive to mitigate than an attack detected at stage 3.
Ireneusz Tarnowski, How to use cyber kill chain model to build cybersecurity
The goal of cybersecurity professionals and the security industry is to “shift left” or to identify and stop attacks as early in the chain as possible.
Ireneusz Tarnowski, How to use cyber kill chain model to build cybersecurity
Shifting left involves strengthening security in the reconnaissance and weaponization stages of the chain. This can include scanning for application vulnerabilities, deploying strong access controls and authentication mechanisms, and even monitoring the dark web for potential threats. Threat intelligence capabilities facilitate these early detection mechanisms and the cross-stage learning necessary for ongoing updates to the framework components in each stage.
Shifting left is an important practice because it reduces the attack surface exposed to threat actors. It also facilitates faster response times to threats, and it results in a more robust security infrastructure.
AI brings transformative capabilities to each stage of the cyber kill chain, significantly enhancing a company’s defenses against advanced attacks and zero-day threats. With threat actors using AI to accelerate and multiply their attacks, security professionals have no choice but to use AI-powered defenses. With a framework in place, IT teams can identify security gaps and focus their efforts on certain stages of an attack sequence.
Barracuda has a new e-book titled Securing tomorrow: A CISO’s guide to the role of AI in cybersecurity. This e-book explores security risks and exposes the vulnerabilities that cybercriminals exploit with AI to scale up their attacks and improve their success rates. Get your free copy of the e-book here.
