LSTM-Based Cybersecurity Framework Utilizing Image Processing Techniques

Abstract

There is higher level of cybersecurity threats that adopt polymorphic malware, phishing and advanced persistent threats which cannot be detected through traditional means. Emerging as a promising technology in the detection of malicious activity are Long Short-Term Memory (LSTM) networks, which have the potential to learn sequential dependencies. Combining image processing and LSTM models presents a fresh insight into converting unstructured cybersecurity information (network traffic, malware binaries, and system logs) to visual representations that can be reviewed more accurately. The paper provides an overview and a workflow of what can be achieved with LSTM-based models and image processing in order to complement cybersecurity detection systems. Cybersecurity data can be transformed into grayscale or RGB images, allowing a complex pattern to be visualized, in which LSTM models can determine temporal features that would otherwise be challenging to identify in a raw format. Applications mentioned are malware classification, intrusion detection, phishing detection and anomaly detection. The system uses convolutional preprocessing to extract features, and then classifies the features using LSTM to provide spatial and temporal learning. The worst would have high level of computation, model intelligibility, data imbalance and adversarial example vulnerability. Irrespective of these shortcomings, the proposed integration proves to be more accurate, scalable and adaptable than traditional detection techniques. This paper concludes that LSTM, along with image processing, is a step in the right direction to the next-generation cybersecurity models, capable of withstanding future and emerging threats. Federated learning to assist privacy preserving training, explainable AI to assist transparency, and light-weight architectures to assist real-time IoT security are directions to come.