An Ensemble Transfer Learning Model for Detecting Stego Images

Abstract

As internet traffic grows daily, so does the need to protect it. Network security protects data from unauthorized access and ensures their confidentiality and integrity. Steganography is the practice and study of concealing communications by inserting them into seemingly unrelated data streams (cover media). Investigating and adapting machine learning models in digital image steganalysis is becoming more popular. It has been demonstrated that steganography techniques used within such a framework perform more securely than do techniques using hand-crafted pieces. This work was carried out to investigate and examine machine learning methods’ critical contributions and beneficial roles. Machine learning is a field of artificial intelligence (AI) that provides the ability to learn without being explicitly programmed. Steganalysis is considered a classification problem that can be addressed by employing machine learning techniques and recent deep learning tools. The proposed ensemble model had four models (convolution neural networks (CNNs), Inception, AlexNet, and Resnet50), and after evaluating each model, the system voted on the best model for detecting stego images. Since active steganalysis is a classification problem that may be solved using active deep learning tools and modern machine learning methods, this paper’s major goal was to analyze deep learning algorithms’ vital roles and main contributions. The evaluation shows how to successfully detect images that contain a steganography algorithm that hides data in images. Thus, it suggests which algorithms work best, which need improvement, and which are easier to identify.

Evaluating Machine Learning and Deep Learning Models for Enhanced DDoS Attack Detection

“…Subsequently, various ensemble models employing strategies such as bagging, boosting, and stacking are trained using the augmented dataset. Additionally, deep learning models, including CNNs, Recurrent Neural Networks (RNNs), and their variants, are deployed to identify complex correlations and patterns within network traffic data [4].…”

“…This framework integrates traffic analysis, anomaly detection, and machine learning algorithms, thereby equipping it to recognize and mitigate DDoS attacks in SDN infrastructures. Mikhail et al [4] suggested a machine learning-based technique for DDoS attack detection, which combines the random forest (RF) feature importance method with mutual information. This technique proficiently identifies DDoS attacks by analyzing patterns in network traffic data, assessing feature relevance through mutual information, and implementing a RF classifier.…”