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Dr. Priyank Sharma is a Professor in the Department of Electronics and Communication Engineering at Meerut Institute of Engineering and Technology, Meerut, Uttar Pradesh, India. He is engaged in teaching and academic activities in electronics and communication engineering. His contribution focuses on the application of multimodal AI for cybersecurity monitoring and intelligent incident response.
Autonomous vehicles signify a revolutionary advancement in smart transportation, allowing vehicles to sense their environment, analyse intricate road scenarios, and execute driving choices with minimal or no human involvement. Multimodal AI confronts these obstacles by amalgamating information from cameras, light detection and ranging (LiDAR), radar, ultrasonic sensors, GPS, inertial measurement units (IMUs), high-definition maps, and vehicle-to-everything (V2X) communication. By integrating sensor fusion, deep learning, spatiotemporal modelling, and attention-driven architectures, multimodal systems can create a more holistic depiction of the driving context. This chapter explores the underpinnings of autonomous vehicle perception, key sensory modalities, multimodal integration techniques, real-time scene analysis, object identification and tracking, semantic comprehension, trajectory forecasting, and decision-making assistance.
The escalating intricacy of assaults, decentralised computing frameworks, cloud-centric infrastructures, and interlinked digital ecosystems has posed considerable difficulties for traditional cybersecurity measures. Conventional security measures frequently assess separate data sources, including network traffic, system logs, in isolation. The suggested framework integrates machine learning, deep learning, natural language processing, behavioural analytics, and multimodal data fusion to detect dubious actions, categorise cyber risks, correlate security incidents, and facilitate automated incident response. The section addresses data collection, preprocessing, feature extraction, integration techniques, attack correlation, and response coordination. The suggested architecture provides a theoretical basis for creating adaptable, context-sensitive, and intelligent cybersecurity technologies that assist security analysts in intricate digital landscapes.