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International Conference on Cybersecurity Applications of Machine Learning

ICCAM

17th Jul – 18th Jul 2026 Frankfurt, Germany

Official Invitation Letter Available

An official invitation letter will be provided upon successful registration for your participation in the conference.

Benefits of Registering as Listener

Access to All Conference Sessions

Plenary, keynote and parallel sessions

Networking Opportunities

Connect with global educators & researchers

Certificate of Participation

Digital certificate of participation

Invitation Letter Support

Official invitation letter after successful registration

Conference Kit / Digital Materials

E-proceedings & resource materials

Access to Keynote Sessions

Learn from leading experts & scholars

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Standard Registration Closed
The deadline for Standard Participation has ended. Participants may continue with Virtual Registration to join the conference remotely.
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Participant Details

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Coupon Code

10% OFF on Registration.
Use Coupon Code → EARLY10
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Terms & Condition

Conference Session Tracks

UN SDG Wheel

Aligned with UN Sustainable Development Goals

The conference's session tracks effectively support the following SDGs.

SDG 9 SDG 16
01 Advancements in Intrusion Detection Systems +
This track focuses on the latest methodologies and technologies in intrusion detection systems leveraging machine learning techniques. Researchers are encouraged to present novel approaches that enhance detection accuracy and reduce false positives.
SDG 9 SDG 16
02 Malware Detection and Classification +
This session aims to explore innovative machine learning algorithms for the detection and classification of malware. Contributions that address the evolving nature of malware and propose adaptive solutions are particularly welcome.
SDG 9 SDG 16
03 Anomaly Detection in Network Security +
This track highlights research on anomaly detection techniques that utilize machine learning to identify unusual patterns in network traffic. Papers should demonstrate the effectiveness of these techniques in real-world scenarios.
SDG 9 SDG 16
04 Predictive Threat Modeling and Risk Analysis +
This session invites contributions that focus on predictive threat modeling using machine learning to assess and analyze cybersecurity risks. Innovative frameworks and case studies that illustrate practical applications are encouraged.
SDG 9 SDG 16
05 Phishing Detection Techniques +
This track is dedicated to exploring machine learning approaches for the detection of phishing attacks. Submissions should present novel algorithms or frameworks that improve the identification of phishing attempts across various platforms.
SDG 9 SDG 16
06 Behavioral Analytics for Cybersecurity +
This session seeks to examine the role of behavioral analytics in enhancing cybersecurity measures through machine learning. Papers should focus on how user behavior can be modeled and analyzed to predict and prevent security breaches.
SDG 9 SDG 16
07 Deep Learning Applications in Cybersecurity +
This track focuses on the application of deep learning techniques in various aspects of cybersecurity. Researchers are invited to share their findings on how deep learning can improve threat detection and response mechanisms.
SDG 9 SDG 16
08 Adaptive Defense Systems in Cybersecurity +
This session explores the development of adaptive defense systems that utilize machine learning to dynamically respond to emerging threats. Contributions should highlight the integration of AI in creating resilient cybersecurity architectures.
SDG 9 SDG 16
09 Attack Pattern Recognition and Analysis +
This track aims to investigate machine learning methods for recognizing and analyzing attack patterns in cybersecurity. Papers should focus on the effectiveness of these methods in enhancing threat intelligence and response strategies.
SDG 9 SDG 16
10 Supervised and Unsupervised Learning in Cybersecurity +
This session invites research on the application of both supervised and unsupervised learning techniques in addressing cybersecurity challenges. Contributions should demonstrate the advantages and limitations of these approaches in practical scenarios.
SDG 9 SDG 16
11 Reinforcement Learning for Cyber Defense +
This track focuses on the application of reinforcement learning in developing proactive cybersecurity measures. Researchers are encouraged to present innovative solutions that leverage reinforcement learning to enhance system defenses against cyber threats.
SDG 9 SDG 16