VIETNAM 7th International Congress on AI, Cybersecurity, IoT, and Cloud Solutions: AICICS-27

Call for papers/Topics

Topics of Interest for Submission include, but are Not Limited to:

1. Core Domains & Subtopics

These represent the foundational pillars of each technology when viewed as standalone fields of study and implementation.

Artificial Intelligence (AI) & Machine Learning (ML)

• Core Paradigms: Supervised learning, unsupervised learning, reinforcement learning, and self-supervised learning.

• Deep Learning: Neural networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers.

• Generative AI: Large Language Models (LLMs), Diffusion models, Retrieval-Augmented Generation (RAG), and synthetic data generation.

• Natural Language Processing (NLP): Sentiment analysis, machine translation, text summarization, and speech-to-text.

• Computer Vision: Image classification, object detection, facial recognition, and semantic segmentation.

• Data Engineering: Data pipelines, feature engineering, data labeling, and data warehousing.

Cybersecurity

• Network Security: Firewalls, Intrusion Detection/Prevention Systems (IDS/IPS), Virtual Private Networks (VPNs), and network segmentation.

• Identity and Access Management (IAM): Multi-Factor Authentication (MFA), Single Sign-On (SSO), Role-Based Access Control (RBAC), and Privileged Access Management (PAM).

• Application & Endpoint Security: Secure coding practices, vulnerability patching, Antivirus/Anti-Malware, and Endpoint Detection and Response (EDR).

• Cryptography: Symmetric/Asymmetric encryption, hashing, Public Key Infrastructure (PKI), and post-quantum cryptography.

• Governance, Risk, and Compliance (GRC): Security frameworks (NIST, ISO 27001), regulatory compliance (GDPR, HIPAA, PCI-DSS), and risk assessments.

• Incident Response & Digital Forensics: Threat hunting, log analysis, disaster recovery, and malware analysis.

Internet of Things (IoT)

• Hardware & Sensors: Actuators, microcontrollers (MCUs), Single Board Computers (SBCs), and smart sensor arrays.

• Connectivity & Protocols: MQTT, CoAP, LoRaWAN, Zigbee, Bluetooth Low Energy (BLE), cellular (5G/6G), and Wi-Fi.

• Edge & Fog Computing: Localized data processing, micro-data centers, and gateway architectures.

• Firmware & Embedded Systems: Real-Time Operating Systems (RTOS), over-the-air (OTA) updates, and low-power optimization.

• Device Management: Provisioning, onboarding, lifecycle management, and remote diagnostics.

Cloud Solutions

• Service Models: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), and Function as a Service (FaaS/Serverless).

• Deployment Models: Public cloud, private cloud, hybrid cloud, and multi-cloud strategies.

• Cloud Architecture & Infrastructure: Virtualization, containerization (Docker, Kubernetes), microservices, and Infrastructure as Code (IaC).

• Storage & Databases: Object storage, block storage, relational databases (SQL), NoSQL databases, and data lakes.

• Networking & Content Delivery: Virtual Private Clouds (VPC), load balancing, Content Delivery Networks (CDN), and DNS management.

2. Interrelated Domains

The true power of modern technology lies where these four domains overlap. Below are the subtopics born from their integration.

AI + Cybersecurity

• AI-Driven Threat Detection: Using ML for anomaly detection, behavioral biometrics, and automated phishing analysis.

• Adversarial Machine Learning: Securing AI models against prompt injection, data poisoning, model evasion, and model inversion attacks.

• SOAR (Security Orchestration, Automation, and Response): Utilizing AI to automatically triage and mitigate cyber threats at scale.

• Deepfake Defense: AI models designed to detect synthetic media, voice clones, and altered video assets.

AI + IoT (The Artificial Intelligence of Things - AIoT)

• Edge AI: Running optimized, compressed machine learning models (TinyML) directly on IoT hardware for zero-latency inference.

• Predictive Maintenance: Analyzing real-time IoT sensor streams to predict industrial machine failures before they occur.

• Autonomous Systems: Smart drones, self-driving vehicles, and robotics that process spatial IoT data to navigate and act autonomously.

• Smart Environments: AI-driven optimization of smart cities, grids, and buildings based on historical and real-time environmental IoT telemetry.

AI + Cloud Solutions

• Cloud AI Services & APIs: Managed cognitive services (vision, speech, translation) hosted by hyperscalers (AWS, Azure, Google Cloud).

• MLOps (Machine Learning Operations): DevOps practices tailored for AI, covering automated model deployment, continuous monitoring, and drift detection in cloud environments.

• Scalable Model Training: Distributed cloud computing architectures utilized to train massive foundational models.

• Intelligent Cloud Management: AI optimizing cloud resource allocation, auto-scaling, and cost management.

Cybersecurity + IoT (IoT Security)

• IoT Device Firmware Security: Securing the hardware supply chain, secure boot mechanisms, and encrypted OTA firmware updates.

• Network-Level IoT Defense: Micro-segmentation to isolate vulnerable IoT devices from critical corporate IT networks.

• IoT Threat Landscape: Mitigating IoT-specific threats like Botnets (e.g., Mirai variants), Man-in-the-Middle (MitM) attacks on sensor data, and physical tampering.

• Zero Trust for IoT: Implementing strict, continuous machine-to-machine authentication without relying on human interaction.

Cybersecurity + Cloud Solutions (Cloud Security / DevSecOps)

• Cloud Security Posture Management (CSPM): Automated monitoring for misconfigurations, compliance drift, and security gaps across cloud infrastructures.

• DevSecOps: Integrating automated security scanning (SAST, DAST) directly into CI/CD cloud deployment pipelines.

• Cloud Workload Protection (CWPP): Securing virtual machines, containers, and serverless functions against runtime attacks.

• Shared Responsibility Model: Delineating security duties between the cloud provider (security of the cloud) and the enterprise customer (security in the cloud).

IoT + Cloud Solutions

• Cloud IoT Platforms: Centralized cloud ingestion hubs (e.g., AWS IoT Core, Azure IoT Hub) capable of handling millions of concurrent device connections.

• Data Pipelines & Analytics: Streaming vast amounts of time-series IoT data into cloud-based data lakes for heavy analytical processing.

• Digital Twins: Creating virtual, cloud-hosted replicas of physical IoT-enabled assets or processes to simulate performance and behavior.

3. The Ultimate Convergence (AI + Cyber + IoT + Cloud)

When all four domains operate together in a unified ecosystem, it enables complex, enterprise-grade architectures:

• Next-Generation Smart Grids: IoT sensors collect energy data, which is sent via Cloud infrastructure, optimized using AI algorithms, and defended continuously by automated Cybersecurity systems.

• Connected Healthcare (IoMT): Medical IoT devices monitor patients locally, stream encrypted health metrics to a secure Cloud, use AI to flag anomalies to physicians, all while strictly adhering to cloud healthcare compliance (HIPAA/GDPR) via advanced Cybersecurity frameworks.

• Autonomous Supply Chains: Fleet vehicles (IoT) relay spatial and environmental data to Cloud hubs, where AI dynamically reroutes logistics based on weather or traffic, while end-to-end Cryptography (Cybersecurity) prevents unauthorized hijacking of the fleet coordinates