11-12-2024 , 10:23 AM
https://www.kaspersky.com/about/press-re...in-h1-2024 Kaspersky achieves 25% increase in APT detection with machine learning
October 2, 2024
Kaspersky’s Global Research and Analysis Team (GReAT) has recorded a 25% increase in the detection of advanced persistent threats (APTs) during the first half of 2024. By leveraging machine learning techniques in its internal service, GReAT uncovered thousands of new advanced threats targeting government, finance, enterprise, and telecommunications sectors. These findings were achieved by analyzing global cyberthreat data from the Kaspersky Security Network (KSN).
The machine learning models employed in Kaspersky’s solutions use techniques such as Random Forest and term frequency–inverse document frequency (TF-IDF) to process vast amounts of data, enabling faster and more accurate detection of subtle threats. This combination of ML methods allows for the identification of indicators of compromise (IoCs) that traditional detection systems might overlook, leading to more precise anomaly detection and a significant improvement in overall threat detection capabilities.
Kaspersky’s ongoing use of machine learning has allowed its systems to process millions of data points daily, providing real-time insights into emerging threats. This has resulted in a 25% increase in threat detections for the first half of 2024, significantly enhancing the ability to reduce response times and mitigate cyber risks.
“The results have exceeded our expectations,” said Amin Hasbini, Head of the META Research Center at Kaspersky’s GReAT. “These technologies improve detection accuracy and foster proactive defense strategies, helping organizations stay ahead of evolving cyber threats. The future of cybersecurity lies in ethically harnessing these tools to ensure a safer digital environment for all.”
Kaspersky’s machine learning models are regularly refined and updated with new data to maintain their effectiveness in a constantly changing cyberthreat landscape. As new attack vectors emerge, these models are monitored and tuned to provide timely insights, strengthening defenses and enhancing organizational resilience.
The research results will be discussed at GITEX 2024, where Kaspersky will participate in a panel on the impact of AI on cybersecurity. To learn more about machine learning in threat hunting, visit Securelist.com.
About Kaspersky
Kaspersky is a global cybersecurity and digital privacy company founded in 1997. With over a billion devices protected to date from emerging cyberthreats and targeted attacks, Kaspersky’s deep threat intelligence and security expertise is constantly transforming into innovative solutions and services to protect businesses, critical infrastructure, governments and consumers around the globe. The company’s comprehensive security portfolio includes leading endpoint protection, specialized security products and services, as well as Cyber Immune solutions to fight sophisticated and evolving digital threats. We help over 200,000 corporate clients protect what matters most to them. Learn more at www.kaspersky.com.
October 2, 2024
Kaspersky’s Global Research and Analysis Team (GReAT) has recorded a 25% increase in the detection of advanced persistent threats (APTs) during the first half of 2024. By leveraging machine learning techniques in its internal service, GReAT uncovered thousands of new advanced threats targeting government, finance, enterprise, and telecommunications sectors. These findings were achieved by analyzing global cyberthreat data from the Kaspersky Security Network (KSN).
The machine learning models employed in Kaspersky’s solutions use techniques such as Random Forest and term frequency–inverse document frequency (TF-IDF) to process vast amounts of data, enabling faster and more accurate detection of subtle threats. This combination of ML methods allows for the identification of indicators of compromise (IoCs) that traditional detection systems might overlook, leading to more precise anomaly detection and a significant improvement in overall threat detection capabilities.
Kaspersky’s ongoing use of machine learning has allowed its systems to process millions of data points daily, providing real-time insights into emerging threats. This has resulted in a 25% increase in threat detections for the first half of 2024, significantly enhancing the ability to reduce response times and mitigate cyber risks.
“The results have exceeded our expectations,” said Amin Hasbini, Head of the META Research Center at Kaspersky’s GReAT. “These technologies improve detection accuracy and foster proactive defense strategies, helping organizations stay ahead of evolving cyber threats. The future of cybersecurity lies in ethically harnessing these tools to ensure a safer digital environment for all.”
Kaspersky’s machine learning models are regularly refined and updated with new data to maintain their effectiveness in a constantly changing cyberthreat landscape. As new attack vectors emerge, these models are monitored and tuned to provide timely insights, strengthening defenses and enhancing organizational resilience.
The research results will be discussed at GITEX 2024, where Kaspersky will participate in a panel on the impact of AI on cybersecurity. To learn more about machine learning in threat hunting, visit Securelist.com.
About Kaspersky
Kaspersky is a global cybersecurity and digital privacy company founded in 1997. With over a billion devices protected to date from emerging cyberthreats and targeted attacks, Kaspersky’s deep threat intelligence and security expertise is constantly transforming into innovative solutions and services to protect businesses, critical infrastructure, governments and consumers around the globe. The company’s comprehensive security portfolio includes leading endpoint protection, specialized security products and services, as well as Cyber Immune solutions to fight sophisticated and evolving digital threats. We help over 200,000 corporate clients protect what matters most to them. Learn more at www.kaspersky.com.