Attention to Patterns is all you need for Insider threat detection

This paper Introduces a fresh approach to insider threat detection in organisations. By leveraging advanced deep learning models such as Time-Distributed Deep Learning Architecture (TD-CNN-LSTM) and Contextually Aware Attention-Based Architecture (TD-CNN-Attention), this method enhances anomaly detection by capturing complex patterns in user behaviour. The combination of CNNs with LSTMs or attention mechanisms extracts spatial and temporal features from user access data, leading to significant accuracy and improvement in F1 scores. This research fonts a significant breakthrough in identifying insider threats, playing a pivotal role in fortifying the security of critical assets amid the constantly evolving threat landscape.
Keywords: Insider Threats, Deep Learning, Anomaly Detection, Time-Distributed, Contextually Aware Attention-Based Architecture, User Behaviour Pattern.

Partitioned Problem Space (PaPS) Ensemble For Zero-day Intrusion Detection

The ubiquity of low-cost cloud data storage has exponentially increased data generation, posing significant challenges to data security. Traditional intrusion detection systems struggle with the volume and speed of cloud data. This work introduces a novel partitioned problem space deep-learning ensemble approach, outperforming existing methods in zero-day intrusion detection tasks.
Keywords: Deep learning, neural learners, malware, intrusion detection, zero-day attack, ensemble, CIC IDS, UNSW NB-15, BODMAS, UNR IDD, cybersecurity.

Blender-GAN: Multi-target conditional Generative Adversarial Network for novel class synthetic data generation

The global increase in computer network usage necessitates robust intrusion detection systems, prompting the application of machine learning and deep learning models. Limited training data for deep neural networks is addressed by synthetic data generation, with Blender-GAN proposed as a novel approach allowing the creation of new data by blending multiple class labels. The architecture demonstrates success in generating realistic synthetic network intrusion data with varied attack classes..
Keywords: Generative Adversarial Network, Synthetic Data, Deep Learning, Network Intrusion, Attack classes.

Securing from Unseen: Connected Pattern Kernels (CoPaK) for Zero-day Intrusion Detection

The surge in data from digitization and cloud adoption requires advanced intrusion detection. Classic systems struggle with complexity, necessitating a proposed deep learning connected pattern kernel architecture. This model excels in zero-day intrusion detection, demonstrating superior performance and generalisation in monitoring network traffic.
Keywords: Deep Learning, Neural Networks, Machine Learning, Malware, Intrusion Detection, Zero-day attack, UNSW NB-15, BODMAS, UNR IDD, Cybersecurity.