Essential Skills for Effective Threat Hunting

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In today’s cyber security landscape, where cyber threats continue to evolve in sophistication, organizations must adopt proactive approaches to safeguard their networks and sensitive data. Threat hunting, a human-driven and iterative process, has emerged as a crucial aspect of cyber security. This article aims to highlight the essential skill set required to become a successful threat hunter.

Threat hunting tends to operate under the assumption that adversaries have already breached an organization’s defenses and are hiding within the corporate network. Unlike traditional security measures that tend to rely solely on automated detection tools and known indicators of compromise (IoCs), threat hunting leverages human analytical capabilities to identify subtle signs of intrusion that automated systems may miss.

A successful threat hunter requires a diverse skill set to navigate the complexities of modern cyber threats effectively. Here are some essential skills for aspiring threat hunters:

  • Cyber threat intelligence. Understanding cyber threat intelligence is foundational for any threat hunter. It involves gathering, analyzing, and interpreting information about potential threats and threat actors. This knowledge provides valuable insights into advanced persistence threats, various malware types, and the motivations driving threat actors.
  • Cyber security frameworks. Familiarity with frameworks like the Cyber Kill Chain and ATT&CK is invaluable for threat hunters. The Cyber Kill Chain outlines the stages of a cyber attack, from initial reconnaissance to the exfiltration of data, helping hunters identify and disrupt attack vectors. ATT&CK provides a comprehensive knowledge base of adversary tactics and techniques, aiding in the understanding of attackers’ behavior and their methods.
  • Network architecture and forensics. A strong grasp of network architecture and forensic investigation is crucial for analyzing network activity, identifying anomalous behavior, and tracing the root cause of security incidents. Additionally, threat hunters must be comfortable working with extensive log data and extracting meaningful insights from them.
  • Coding and scripting. Proficiency in coding and scripting languages, such as Python, PowerShell, or Bash, can be highly beneficial for threat hunters. These skills allow them to automate repetitive tasks, conduct custom analysis, and develop tools to aid in their investigations.
  • Data science. Threat hunting often involves dealing with vast amounts of data. Data science skills enable hunters to develop algorithms, create statistical models, and perform behavioral analysis, significantly enhancing their ability to detect and respond to threats effectively.
  • Organizational systems. Each organization operates differently, and threat hunters need to be well-versed in their organization’s systems, tools, and incident response procedures. This knowledge allows them to discern deviations from normal activity, leading to quicker response times and more accurate threat assessments.
  • Collaboration and communication. Threat hunters often work in teams and collaborate with other cybersecurity professionals. Strong communication skills are essential for sharing findings, coordinating responses, and effectively conveying complex technical information to non-technical stakeholders.

Threat hunting is not a one-size-fits-all approach, but a personalized, data-driven, and iterative process tailored to an organization’s unique risk profile. Cultivating a skilled team and proactive culture bolsters defenses against dynamic cyber threats. Staying informed, collaborating, and embracing technology ensures success in securing organizations from advanced adversaries.

Security and Ethical Risks of Using Large Language Models for Code Generation

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The rise of Large Language Models (LLMs) has revolutionized software development, offering developers the ability to generate code at an unprecedented scale. While LLMs like ChatGPT have proven to be powerful tools, they come with security and ethical risks that developers must be cautious about.

  1. Vulnerable code: LLMs are trained on extensive datasets, including code with potentially known vulnerabilities. This makes them prone to inadvertently produce code susceptible to attacks like SQL injection. Additionally, LLM-generated code might contain malicious elements like viruses or worms, and inadvertently leak sensitive data such as passwords or credit card numbers, putting users and organizations at grave risk.
  2. Challenges in code maintenance and comprehensibility: LLMs have the capability to generate intricate code that can be challenging to comprehend and maintain. The complexity introduced by such code can pose significant obstacles for security professionals when it comes to identifying and addressing potential security flaws effectively.
  3. Ethical and legal concerns: The use of LLMs for code generation raises ethical issues regarding code plagiarism, where developers might copy others’ work without proper attribution. Moreover, generating code that infringes on copyright can lead to severe legal consequences, hindering innovation and discouraging original contributions.

In conclusion, LLMs revolutionize software development with unprecedented code generation capabilities. However, caution is crucial due to security and ethical risks. Collaborative efforts for better comprehension and flaw identification are essential. Respecting intellectual property fosters an ethical coding community. By acknowledging risks and adopting responsible practices, developers can maximize LLMs’ benefits while safeguarding software integrity and security in this era of advancement.

EU Data Initiatives: Developments to Watch in 2024 and Beyond

The European Union (EU) has been at the forefront of global efforts to protect privacy and personal data. Over the years, the EU has implemented several initiatives and regulations that aim to safeguard the privacy rights of its citizens. The International Association of Privacy Professionals (IAPP) has created a timeline of key dates for these EU regulations and initiatives, including those that are yet to be finalized.

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Here are the key dates to watch out for the year 2024 and beyond:

  • February 17, 2024: The Digital Services Act (DSA), which aims to establish clear rules for online platforms and strengthen online consumer protection, will become applicable
  • Spring 2024: The AI Act is expected to be adopted
  • Mid-2024: The Data Act is expected to enter into force
  • October 18, 2024: The NIS2 directive will become applicable
  • January 17, 2025: The DORA regulation will become applicable

In conclusion, the EU’s data initiatives are set to undergo significant changes in the coming years with the implementation of regulations like the DSA, AI Act, Data Act, NIS2 directive, and DORA regulation. These initiatives aim to establish clear rules for online platforms, strengthen online consumer protection, facilitate data sharing, and more. It is crucial for organizations, including individuals, to stay up-to-date with these key dates to ensure compliance with the new regulations and to take advantage of the opportunities they present.

For a more detailed overview of the EU’s data initiatives and their key dates, check out the infographic created by the IAPP here.

Exploring the Frontier of Green Intelligent Homes: My Presentation in Prague

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As we continue to embrace the benefits of smart technology, the concept of the Green Intelligent Home is an exciting and promising development in the evolution of smart homes. Our paper, which I presented at the IoTBDS conference in Prague last weekend, explores this frontier.

The possibilities of a world where Green Intelligent Homes are the norm are intriguing, as they offer increased automation, personalization, sustainability, and more. Nonetheless, as with any emerging technology, it is important to be aware of potential risks and implications. These range from security and privacy, manipulation of people, a lack of self-sufficiency, and more.    

As the Green Intelligent Home prospect continues to develop, it is essential to stay informed and explore the potential of this technology. In case you are interested in learning more about the Green Intelligent Home or collaborating on related projects, please get in touch.

Securing the University: My Information Security Awareness Session

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As technology continues to advance, so do the risks and threats associated with it. To protect ourselves and our institutions, it is crucial to remain informed and updated with the latest security trends and best practices. This was the main focus of my recent 45-minute security awareness session with the university technical staff.

In addition to discussing fundamental security measures, I also covered the latest threat actors and threats in the cyber security landscape affecting universities and public institutions. This included state-sponsored actors, cybercriminals, hacker-for-hire groups, and hacktivists. I emphasized the potential consequences of a cyber attack, which can be severe and damaging, such as financial losses, reputational harm, and legal liability.

One alarming statistic I shared was that according to estimates from Statista’s Cybersecurity Outlook, the global cost of cybercrime is expected to surge in the next five years, rising from $8.44 trillion in 2022 to $23.84 trillion by 2027. This underscores the importance of taking proactive steps to mitigate potential risks.

While technical measures are essential, we also discussed the human element of security, including social engineering tactics like phishing emails or pretexting phone calls. Information security starts and ends with all of us, and it is crucial that everyone takes responsibility for protecting sensitive information and assets.

Here is a redacted version of the presentation. Additionally, I recently co-authored an article titled “Human Factors for Cybersecurity Awareness in a Remote Work Environment”, which delves into relevant and relatable cyber security aspects for remote employees.”

Navigating the Risks and Rewards of Drone Technology

The use of drones for various applications has been on the rise in recent years. From delivery services to aerial photography, drones have proven to be a valuable tool for a variety of industries. However, the increased prevalence of drones has also raised concerns about security and safety. In high-security locations such as airports, the possibility of rogue drones posing a threat to the safety of passengers and personnel has led to the development of counter-drone technologies. One such technology that has gained attention in recent years is the use of drones to take down other drones. See the video here:

Video source: https://twitter.com/HowThingsWork_/status/1611069508201943055

The use of drones as a means of warfare has been a controversial topic for some time now. Military drones, also known as unmanned aerial vehicles, have been used by various countries for surveillance, intelligence gathering, and targeted airstrikes. While drones can provide an advantage in certain situations, their use has also raised ethical and legal issues, particularly with regard to civilian casualties.

The use of drones for warfare is not limited to military applications. Non-state actors have also been known to use drones for hostile purposes, such as smuggling drugs and weapons across borders or carrying out attacks. In some cases, these drones have been used to disrupt critical infrastructure, such as oil facilities and power plants. The use of drones as a means of warfare is likely to increase in the future, as the technology becomes more widespread and sophisticated. As such, the development of counter-drone technologies will become increasingly important in order to protect against these threats.

Exploring Some Misconceptions and Complexities of Artificial Intelligence

Artificial intelligence (AI) is a rapidly advancing field that has the potential to revolutionize many aspects of our daily lives. However, as with any complex subject, there are often misunderstandings and misconceptions about what AI is and what it can do. In this article, we will explore some of these misconceptions.

The intersection of reasoning and learning in AI techniques. AI techniques can be broadly grouped into two categories based on their ability to reason and learn. However, these techniques are not mutually exclusive. For example, expert systems, which involve reasoning, may also incorporate elements of learning, such as the ability to adjust the rules or weightings based on past performance or feedback.

The versatility of machine learning. Machine learning is a technique that enables AI systems to learn how to solve problems that cannot be precisely specified or whose solution method cannot be described by symbolic reasoning rules. However, machine learning is not limited to solving these types of problems. It can also be used to learn from structured data and can be combined with symbolic reasoning techniques to achieve a wider range of capabilities. 

The diversity of machine learning techniques. Machine learning definitions and sometimes taxonomies only mention supervised, unsupervised, and reinforcement learning. However, there are other types of machine learning, such as semi-supervised learning and active learning.  These different types of machine learning each have their own unique characteristics and are suited to different types of problems and data.

The relationship between AI and robotics. AI and robotics are closely related fields that often overlap, but they are distinct areas of study. While robotics can be considered a subfield of AI, it is possible to study robotics independently of AI. Similarly, AI can be studied without necessarily delving into the field of robotics. 

In conclusion, the field of AI is vast and complex, with many nuances and misconceptions that are important to understand. Despite these complexities, the potential for AI to revolutionize many aspects of our lives makes it a field worth exploring and understanding.

Understanding Cyber Warfare Through Frameworks

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Cyber warfare is a rapidly evolving field, and various frameworks have been developed to better understand and defend against cyber attacks. Several cyber kill chains have been developed to explain what an attacker might do. The most commonly used at present are the Lockheed Martin Cyber Kill Chain and the MITRE ATT&CK framework.

The Lockheed Martin Cyber Kill Chain is a seven-stage framework that describes the steps an attacker might take in a cyber attack. It includes stages for reconnaissance, weaponization, delivery, exploitation, installation, command and control, and actions on objectives. 

The MITRE ATT&CK framework is a comprehensive database of tactics, techniques, and procedures used by attackers that is organized into several categories such as initial access, execution, persistence, privilege escalation, defense evasion, credential access, discovery, lateral movement, collection, command and control, and exfiltration.

The Unified Kill Chain is a framework that combines elements from the Lockheed Martin Cyber Kill Chain, the MITRE ATT&CK framework, and other frameworks to provide a more comprehensive view of cyber attacks.  It includes eighteen attack phases, which are the steps a cyberattack may progress through.

Overall, cyber warfare is highly complex and requires extensive knowledge and understanding of the different frameworks and best practices for defending against attacks. By familiarizing ourselves with these frameworks, we can better prepare ourselves for the challenges ahead and ensure our networks remain secure.

Advantages and Concerns of Using Machine Learning in Security Systems

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Machine learning (ML) has revolutionized the security market in recent years, providing organizations with advanced solutions for detecting and preventing security threats. ML algorithms are able to analyze large amounts of data and identify patterns and trends that may not be immediately apparent to human analysts. This has led to the development of numerous ML-based security systems, such as intrusion detection systems, malware detection systems, and facial recognition systems.

ML-based security systems have several advantages over traditional security systems. One of the main advantages is their ability to adapt and learn from new data, making them more effective over time. Traditional security systems rely on predetermined rules and protocols to detect threats, which can become outdated and ineffective as new threats emerge. In contrast, ML-based systems are able to continuously learn and improve their performance as they process more data. This makes them more effective at detecting and responding to new and evolving threats.

Another advantage of ML-based security systems is their ability to process large amounts of data in real time. This enables them to identify threats more quickly and accurately than human analysts, who may not have the time or resources to manually review all of the data. This makes ML-based systems more efficient and effective at detecting security threats.

Despite the numerous benefits of ML-based security systems, there are also some concerns that need to be addressed. One concern is the potential for bias in the data used to train ML algorithms. If the data used to train the algorithm is biased, the algorithm itself may be biased and produce inaccurate results. This can have serious consequences in the security context, as biased algorithms may overlook or wrongly flag certain threats. To mitigate this risk, it is important to ensure that the data used to train ML algorithms is representative and diverse and to regularly monitor and test the performance of the algorithms to identify and address any biases.

Another concern with ML-based security systems is that they are only as good as the data they are trained on. If the training data is incomplete or outdated, the system may not be able to accurately identify threats. This highlights the importance of maintaining high-quality and up-to-date training data for ML-based security systems.

Despite these concerns, the use of ML in security systems is likely to continue to grow in the coming years. As more organizations adopt ML-based security systems, it will be important to ensure that these systems are trained on high-quality data and are continuously monitored to ensure that they are performing accurately. This will require ongoing investment in data management and monitoring infrastructure, as well as the development of best practices for training and maintaining ML-based security systems.

Recently, I published an article on this topic. Take a look at it here: https://www.scitepress.org/Link.aspx?doi=10.5220/0011560100003318

Please get in touch with me if you want to discuss themes related to cyber security, information privacy, and trustworthiness, or if you want to collaborate on research or joint projects in these areas.

Unveiling the Lack of Transparency in AI Research

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A recent systematic review by Burak Kocak MD et al. has revealed a lack of transparency in AI research. The data, presented in Academic Radiology, showed that only 18% of the 194 selected radiology and nuclear medicine studies included in the analysis had raw data available, with access to private data in only one paper. Additionally, just one-tenth of the selected papers shared the pre-modeling, modeling, or post-modeling files.

The authors of the study attributed this lack of availability mainly to the regulatory hurdles that need to be overcome in order to address privacy concerns. The authors suggested that manuscript authors, peer-reviewers, and journal editors could help make AI studies more reproducible in the future by being conscious of transparency and data/code availability when publishing research results.

The findings of the study highlight the importance of transparency in AI research. Without access to data and code, it is difficult to validate and replicate results, leading to a lack of trust in the results. This is especially important for medical AI research, as the safety and efficacy of treatments and diagnostics depend on accurate and reliable results. What further steps can be taken to increase transparency while still protecting privacy?