Jakarta, Gizmology – Advanced Persistent Threats (APTs) are one of the biggest challenges in modern cybersecurity and this has been stated by researchers at Kaspersky. In the first half of 2024, Kaspersky’s Global Research and Analysis Team (GReAT) recorded a 25% increase in APT detections across critical sectors such as government, finance and telecommunications. This increase not only shows that there are more threats, but also that the tools and technologies used by Kaspersky have evolved to detect threats more quickly and accurately.
The Machine Learning technology used by Kaspersky played a key role in this achievement. Using algorithms such as Random Forest and data analysis techniques such as Term Frequency-Inverse Document Frequency (TF-IDF), Kaspersky is able to analyze millions of data points every day to detect threats that may have been missed by traditional detection methods. The use of this technology not only improves detection accuracy, but also shortens the response time to emerging cyber threats.
These findings provide important insights for companies operating in an increasingly complex digital landscape. With Machine Learning technology that is constantly being updated and refined, companies like Kaspersky can help mitigate the risks faced by critical sectors and maintain the stability and security of digital infrastructure around the world.
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The Role of Machine Learning Technology in APT Detection
Machine learning technology is the backbone of advancements in cyber threat detection in this digital age. By applying models such as Random Forest and TF-IDF, Kaspersky is able to process and analyze a large amount of threat data in a very short time. This technology allows the system to recognize patterns that are usually undetectable by traditional methods, which are often more reactive than proactive.
Machine Learning’s strength lies in its ability to learn from existing data and adapt quickly to new threats. This means that even if the types of threats change, systems utilizing this technology are able to update themselves and provide accurate insights in the detection of new threats. The use of Machine Learning techniques indirectly reduces reliance on human intervention, speeds up the threat identification process, and strengthens overall cyber defense.
Kaspersky, through its Global Research and Analysis Team (GReAT) platform, has shown that utilizing Machine Learning can increase APT detection by up to 25%. This is a significant improvement in the world of cybersecurity, where early detection can be the deciding factor in preventing attacks that damage critical infrastructure.
The Impact of AI and Machine Learning on Cybersecurity Strategy
The use of artificial intelligence (AI) and machine learning in cybersecurity has brought about major changes in the way organizations respond to and deal with cyber threats. With AI’s ability to process data in real-time, organizations can take preventive measures faster and more efficiently. This is particularly important in the context of APT attacks, where threats are often stealthy and operate for long periods of time before detection.
In addition, AI can also reduce the number of “false positives” or false detections, which typically drain organizational resources and slow response times to real threats. With this reduction in false positives, companies can focus more on real and critical threats, ensuring that defense measures taken are more efficient and targeted.
In the future, Kaspersky and other cybersecurity companies will continue to develop tools that incorporate artificial intelligence and machine learning. With their participation in events like GITEX 2024, they seek to continue discussing the impact and future of AI in cybersecurity. With the ethical use of these technologies, digital security will continue to improve, and organizations will be better prepared for increasingly complex and diverse cyber threats.
Machine Learning technology has opened up new avenues in the world of cybersecurity, enabling more accurate and rapid threat detection. The 25% increase in advanced persistent threat (APT) detection by Kaspersky in the first half of 2024 shows the huge potential this technology has. With the ever-changing cyber threat landscape, utilizing tools such as AI and Machine Learning is an important step in protecting critical sectors from increasingly sophisticated attacks.
In the future, cybersecurity strategies will increasingly rely on these technologies to prevent attacks before they can cause significant damage. As such, companies around the world need to continue to invest in technology and expertise that will allow them to stay ahead in the fight against evolving cyber threats.
