Dimensionality Reduction for Cyberattack Classification: A Comparative Evaluation of PCA and Linear Predictive Coding

High-dimensional feature representations are widely used in machine learning-based cyberattack detection systems. However, they increase computational complexity and may hinder deployment in resource-constrained environments. In this paper, we investigate feature compression techniques for...

Adaptive Malware Detection Using Sequential Feature Selection: a Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification

Traditional malware detection methods exhibit computational inefficiency due to exhaustive feature extraction requirements, creating accuracy-efficiency trade-offs that limit real-time deployment. We formulate malware classification as a Markov Decision Process with episodic feature acquisition a...

Empowering Digital Agriculture: a Privacy-Preserving Framework for Data Sharing and Collaborative Research

Data-driven agriculture, which integrates technology and data into agricultural practices, has the potential to improve crop yield, disease resilience, and long-term soil health. However, privacy concerns, such as adverse pricing, discrimination, and resource manipulation, deter farmers from...

SecureFed: a Two-Phase Framework for Detecting Malicious Clients in Federated Learning

Federated Learning FL protects data privacy while providing a decentralized method for training models. However, because of the distributed schema, it is susceptible to adversarial clients that could alter results or sabotage model performance. This study presents SecureFed, a two-phase FL...

Efficient Malware Detection with Optimized Learning on High-Dimensional Features

Malware detection using machine learning requires feature extraction from binary files, as models cannot process raw binaries directly. A common approach involves using LIEF for raw feature extraction and the EMBER vectorizer to generate 2381-dimensional feature vectors. However, the high...

GiBy: a Giant-Step Baby-Step Classifier for Anomaly Detection in Industrial Control Systems

The continuous monitoring of the interactions between cyber-physical components of any industrial control system ICS is required to secure automation of the system controls, and to guarantee plant processes are fail-safe and remain in an acceptably safe state. Safety is achieved by managing...

Feature Selection Via GANs (GANFS): Enhancing Machine Learning Models for DDoS Mitigation

Distributed Denial of Service DDoS attacks represent a persistent and evolving threat to modern networked systems, capable of causing large-scale service disruptions. The complexity of such attacks, often hidden within high-dimensional and redundant network traffic data, necessitates robust and...

Exploring botnets in VR

By Asaf Nadler & Lior Lahav Botnets often use domain generation algorithms DGAs to select a domain name, which bots use to establish communication channels with their command and control servers C2. Since Akamai analyzes over 2.2 trillion DNS requests per day, and detects thousands of active...

Clustering and Dimensionality Reduction: Understanding the “Magic” Behind Machine Learning

These days we hear about machine learning and artificial intelligence AI in all aspects of life. We see machines that learn and imitate the human brain in order to automate human processes. There are autonomous cars that learn the road conditions to drive, personal assistants we can converse with...