Understanding Threat Modeling: A Guide for Junior Cybersecurity Researchers

Keeping one step ahead of potential threats is critical in the ever-changing cybersecurity arena. Threat modeling is a systematic method for cybersecurity professionals to identify, prioritize, and mitigate possible threats to a system or organization. In this blog article, we will delve into the world of threat modeling and its delicate relationship to threat intelligence, offering junior cyber security researchers valuable insights on improving their defense systems.

Understanding Threat Modelling

Threat modeling is a proactive method that identifies, classifies, and prioritizes potential system threats. It enables cybersecurity specialists to assess a system’s security status and apply appropriate remedies.

In the first stage, define the system’s scope and learn about its architecture, components, and data flows. Identify the assets and resources that require protection, such as sensitive data, servers, programs, or user accounts. Enumerate potential threats and vulnerabilities, considering both internal and external variables that could affect the system.

After identifying threats, evaluate their significance and likelihood. Prioritize risks according to severity and likely consequences. Finally, devise ways to mitigate the identified risks, put in place security measures, and regularly monitor the system for emerging threats.

Connection with Threat Intelligence:

Threat intelligence is critical for improving the effectiveness of threat modeling. It entails obtaining and analyzing data about prospective threats, including their tactics, methods, and procedures (TTPs).

Incorporate threat intelligence feeds to help detect threats, as they provide real-time data on cyber threats. Stay current on the newest attack vectors and vulnerabilities affecting your system. Improve risk assessment by using threat intelligence to determine the likelihood and impact of individual threats. Use threat indicators and context from threat intelligence to refine risk assessments continuously.

With threat intelligence, mitigation techniques can be more effectively tailored. Create plans based on threat intelligence to counter particular risks, and be flexible enough to adjust plans as the threat environment changes. Incident response is enhanced when threat intelligence is incorporated into incident response strategies. Use threat intelligence indicators of compromise (IoCs) to identify and address security events quickly.

To sum up:

For novice cyber security researchers, threat modeling becomes an even more potent tool when paired with threat intelligence. Researchers can create robust defenses by comprehending the workings of their systems, seeing possible attacks, and utilizing timely threat intelligence. In a time when cyber threats are constantly changing, protecting digital assets and upholding a robust cyber security posture need a proactive and knowledgeable approach.


Discovering the Promise of Threat Hunting: A Practical Guide for Cyber Security Professionals

Today, we’ll look at threat hunting, a dynamic and proactive approach to cybersecurity that allows experts to discover and neutralize any dangers lurking within their networks actively. This post will explore the intricacies of threat hunting, providing practical insights and strategies to help you identify and mitigate cyber threats.

Understanding Threat Hunting:

Threat hunting looks for indications of compromise (IoCs), anomalies, and unusual activity that could circumvent typical security procedures. It’s similar to being a detective in the digital realm, continuously looking for indicators of criminal activity.

Threat Hunting Process:

Preparation: Effective threat hunting begins with adequate planning. Understand your network architecture, identify essential assets, and set specific objectives for your hunting trips.

Data Acquisition and Analysis: Collecting and analyzing information are critical components of threat hunting. Logs from many sources, including network devices, endpoints, and security systems, are crucial. Tools like Splunk and Elasticsearch can assist in aggregating and analyzing massive amounts of data.

Hypothesis Generation: Develop assumptions regarding potential dangers or suspicious actions based on collected facts. These hypotheses serve as a foundation for further inquiry.

Investigation and Triage: Conduct specific investigations to support or reject hypotheses. This could include reviewing network traffic, analyzing log files, and performing forensic analysis on suspect endpoints.

*Response and remediation: In the case of a confirmed threat, work with incident response teams to launch prompt and efficient responses. This could include isolating hacked systems, preventing malicious traffic, and applying patches or updates to susceptible assets.

Tools and Techniques:

While the human aspect is critical to threat hunting, using the correct tools and procedures can significantly improve performance.

  • The SIEM Platforms: Platforms such as LogRhythm and QRadar offer centralized log management and extensive analytics features.
  • Endpoint Detection and Response (EDR) Systems: Solutions like CrowdStrike Falcon and Carbon Black provide real-time visibility into endpoint activity.
  • Threat Intelligence Feeds: Subscribing to feeds from credible sources can provide helpful background for recognizing emerging risks and indicators of compromise.


To summarize, threat hunting is a proactive strategy for cybersecurity that allows professionals to keep one step ahead of cyber adversaries. Adopting a digital detective attitude and utilizing the appropriate tools and tactics may strengthen your organization’s resilience to emerging cyber threats.

Stay tuned for future postings where we’ll go over advanced threat-hunting tactics, real-world case studies, and hands-on exercises to help you improve your skills even further.

Also please check the open-source threat intelligence tools with links:

MISP: Malware Information Sharing Platform & Threat Sharing

MISP is a comprehensive platform for sharing, storing, and correlating structured threat information.

OpenCTI: OpenCTI

OpenCTI is an open-source platform for managing cyber threat intelligence knowledge and observables.

Stix-Shifter: Stix-Shifter

Stix-Shifter is a library facilitating integration with the STIX (Structured Threat Information eXpression) standard.


YARA is a tool for identifying and classifying malware samples based on rules created by security researchers.

Yeti: Yeti

Yeti is an open-source threat intelligence platform for aggregating, enriching, and correlating threat data.

IntelMQ: IntelMQ

IntelMQ is a solution for managing and automating the processing of cybersecurity threat intelligence.

ThreatIngestor: ThreatIngestor

ThreatIngestor is an event-driven collection tool for pulling threat intelligence data from various sources.

Mitre ATT&CK Framework: Mitre ATT&CK Framework

The Mitre ATT&CK Framework is a knowledge base describing actions and behaviors of cyber adversaries.

AIS: Aide Information Sharing

AIS is an open-source threat intelligence sharing platform.

Vulners Database: Vulners

Vulners is a comprehensive vulnerability database providing information about software vulnerabilities, exploits, and patches.

cif: Collective Intelligence Framework

cif is an open-source intelligence sharing platform aggregating indicators of compromise.


Mitre CALDERA is an automated adversary emulation system for creating and executing adversary simulations.

Last note for you: please check Mandiant’s Threat Intelligence Reports and SocRadar’s free tools

Feel free to explore these tools to enhance your understanding of threat intelligence and cybersecurity. Always use them responsibly and in compliance with relevant laws and regulations.

Happy hunting!


Decoding the Art of Intelligence Analysis: Insights into the Cybersecurity Landscape

Intelligence analysis is a critical procedure that involves carefully reviewing data to generate insightful and actionable intelligence. Analysts gather information from various sources, including open-source intelligence, human intelligence, signals intelligence, and more. This material can include raw data, reports, or direct accounts.

Intelligence Analysis Management coordinates and oversees the analytical processing of raw intelligence data into final intelligence. The terms “analysis,” “production,” and “processing” are all employed in this phase, which is also known as “connecting the dots.” Creating an “intelligence mosaic” is a colorful description of the process. Analysis, processing, and manufacturing are all used to describe organizing and assessing raw information before disseminating it to various users. The same data set may provide different analytic products with varying security categories, time ranges, and levels of detail.

Intelligence Cycle

When intelligence personnel are allocated a specific project, we use a five-step process known as the Intelligence Cycle. This procedure guarantees that we accomplish our jobs effectively by utilizing a system of checks and balances. The five stages are planning and direction, collection, processing, analysis and production, and dissemination. Let us take a deeper look at each step.

Planning & Direction: When assigned a specific assignment, we begin to plan what we will do and how to accomplish it. We use a particular approach to complete the task, stating what we know about the problem and what we need to learn more about. We explore how to obtain the necessary intelligence.

Collection: We acquire information both overtly (openly) and covertly (secretly). We define “overt” (or open) sources as reading foreign newspapers and magazine articles, listening to foreign radio, and watching abroad television broadcasts. Other information sources can be “covert” (or secret), such as data gathered by listening devices and hidden cameras. We can even utilize space-age technology, such as satellite photography. For example, some analysts could use a satellite image to determine how many planes are at a foreign military facility.

Processing: We compile all the information we have gathered into an intelligence report. This material could range from a translated paper to a description of a satellite photograph.

Analysis and Production: During this step, we examine all of the information and assess how it fits together while focusing on answering the original task. We explore what is happening, why it is occurring, what may happen next, and how it affects US interests.

Dissemination: In this final phase, we present our final written analysis to the policymaker who started the cycle. After reading the final analysis and obtaining the answer to the initial query, the policymaker may return with more inquiries. Then, the entire procedure begins again.

Once acquired, raw data is processed and organized to remove extraneous information and structure key bits in a more readable manner. Analysts then examine and evaluate the data to detect patterns, trends, anomalies, and potential links. They analyze the dependability and credibility of sources, as well as the value of the data acquired in relation to the intelligence task.

To thoroughly understand the situation, integrated analysis is required, which aggravates information from multiple sources and disciplines. This guarantees that analysts explore various perspectives. To have a thorough understanding of the situation, integrated analysis is required, which combines information from multiple sources and disciplines. This guarantees that analysts evaluate many views and dimensions of the intelligence problem.

The next phase is interpretation, which involves analysts creating intelligence assessments or products by interpreting the data’s implications and making informed decisions about expected outcomes or future events. The analysis findings are subsequently presented and communicated to key stakeholders via intelligence reports, briefings, or other forms. This communication is critical for informing decision-makers and those who must take action based on intelligence findings.

Intelligence analysis is an iterative technique that incorporates a continual feedback loop. Analysts frequently obtain feedback on the effectiveness and accuracy of their assessments, which helps them improve their methodology and future analyses.

Cyber Threat Intelligence (CTI) analysts examine data relating to cyber threats, such as malware, vulnerabilities, and threat actors’ methods. This process enables enterprises to better understand the nature of potential cyber attacks and take proactive steps to secure their systems. Analysts may also seek to attribute cyber attacks to individual threat actors or groups by connecting the dots between various indications and known threat actors’ behaviors. Furthermore, trend analysis enables analysts to spot patterns in cyber threats across time, allowing businesses to predict and prepare for new risks.

Effective intelligence analysis necessitates a mix of technical expertise, critical thinking, and domain knowledge. It is crucial for facilitating decision-making processes and proactive solutions to a variety of challenges, including those in the cybersecurity arena.