Intrusion Detection System based on Machine Learning and Deep Learning Model

👥 Ankit Gupta, Dr.Harsh Mathur

📙 Abstract : The exponential growth of cloud computing, Internet of Things (IoT), Industrial Internet of Things (IIoT), edge computing, and intelligent networking has significantly increased the frequency and complexity of cyber-attacks. Traditional signature-based Intrusion Detection Systems (IDS) are often ineffective against zero-day attacks, polymorphic malware, and sophisticated network intrusions due to their dependence on predefined attack signatures and limited adaptability. Consequently, Machine Learning (ML) and Deep Learning (DL) techniques have emerged as promising solutions for intelligent intrusion detection by automatically learning network traffic patterns and identifying malicious activities. This literature survey critically reviews recent advances in ML- and DL-based intrusion detection systems, compares existing methodologies using standard performance metrics such as Accuracy, Precision, Recall, F1-score, False Positive Rate (FPR), False Negative Rate (FNR), Area Under the Curve (AUC), and Detection Rate (DR), and identifies current research gaps. This research work presents, comparative study for intrusion detection system using ML-DL based approaches, and also provides valuable insights for designing next-generation intelligent, scalable, explainable, and lightweight intrusion detection systems suitable for modern cloud, IoT, and edge computing environments.

🔖 Keywords :️ Machine learning, Network security, Deep learning, Intrusion detection system, Cyberattacks.

AI-Based Lung Image Segmentation and Compression for Medical Image Analysis

👥 Puneet Tiwari, Dr.Anupam Chouksey

📙 Abstract : Medical image analysis plays an important role in the diagnosis and treatment of lung-related diseases. The increasing availability of high-resolution lung images, including CT scans and chest X-ray images, has created challenges related to accurate image segmentation, storage, processing, and transmission. In this paper, we propose an AI-based lung image segmentation and compression framework for efficient medical image analysis. The proposed approach uses deep learning techniques to automatically identify the lung region from medical images and subsequently compress the extracted region while preserving important diagnostic information. Initially, lung images are collected from publicly available medical image datasets and subjected to preprocessing operations such as resizing, noise removal, normalization, and enhancement. A deep learning-based segmentation model is then used to distinguish the lung region from the surrounding anatomical structures. The segmented lung region is subsequently processed using an autoencoder-based compression technique to generate a compact representation of the medical image. The reconstructed image is evaluated to determine the quality of the compression. The proposed framework can support efficient storage, transmission, and subsequent analysis of medical images. Segmentation performance can be evaluated using Dice coefficient, Intersection over Union, precision, recall, and accuracy, while compression performance can be evaluated using Mean Squared Error, Peak Signal-to-Noise Ratio, Structural Similarity Index, and compression ratio. The proposed AI-based framework provides an integrated solution for automated lung segmentation and efficient medical image compression and can serve as a preprocessing stage for computer-aided diagnosis systems.

🔖 Keywords :️ Artificial Intelligence, Lung Image, Medical Image Analysis, Deep Learning, Image Segmentation, Image Compression, U-Net, Autoencoder, CT Image, CNN.