Neural Network Solutions for Advanced Persistent Threat Analysis

In the last few years, persistent and deceitful attacks have emerged. These attacks, often known as advanced persistent threats, or APTs, pose a severe challenge to the present security architecture. The term APTs was coined to offer a more accurate explanation of the many sorts of attacks. The word “assaults” can also be interpreted to signify APT in its closest sense. Using neural network architecture, we provide a unique technique for identifying and analyzing APTs. This technique may be beneficial in identifying and evaluating APTs. The authors of the study provided a substantial quantity of evidence to support the validity of the procedures they detailed. To discover and comprehend data anomalies, a multi-level architecture based on recurrent neural networks (RNNs), convolutional neural networks (CNNs), and attention processes may be employed. This makes the system more resistant to such assaults. An attention mechanism was introduced to the model's third layer to improve the system's sensitivity to irregularities. By focusing on certain parts of the input data, this method assists the network in identifying suspicious patterns and behaviors that may be related to an advanced persistent threat.