• Hruthkarsha D S
• Divya B S
• Chaitra D
• Arpitha R
• Dr. Praveen Kumar K V

The arrival of deep literacy ways, similar to Convolutional Neural Networks( CNNs) and intermittent Neural Networks( RNNs), has revolutionized colorful disciplines, including image processing and natural language understanding. Despite their remarkable success in these fields, their operation to information security, specifically attack discovery, remains limited. In this design we propose an innovative approach to enhance information security through intelligent attack discovery. We work Long Short-Term MemoryTransformer( LSTM- Transformer) and introduce a comprehensive frame that seamlessly integrates data preprocessing, feature abstraction, and multi-channel training. Our system initiates with rigorous data preprocessing to ensure the quality of input data. latterly, different features are strictly uprooted from the reused data, landing both subtle and overt patterns associated with attacks. A core element of our approach is multi-channel training, wherein neural networks are trained with distinct point sets. This holistic approach effectively retains the nuances of attack characteristics within input vectors, easing precise isolation between normal and vicious conditioning. To make robust opinions regarding the attack events, we employ a decision emulsion medium that summates the labors of multiple classifiers and also introduce a voting algorithm to decide whether the input data is attacked or not. This agreementgrounded approach significantly enhances the delicacy and trustability of attack discovery. Experimental evaluations demonstrate the superiority of our approach over conventional styles employing point discovery and traditional classifiers, similar as Bayesian or Support Vector Machines( SVMs)

Attack detection, LSTM-Transformer, multichannel training, robust decisions, voting algorithm, Support Vector Machine(SVM), Convolutional Neural Networks(CNNs), Deep Learning.

"Intelligent Multi-Channel Threat Detection For Enhanced Data Security Using Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.f52-f59, February-2024, Available :http://www.jetir.org/papers/JETIR2402506.pdf

"Intelligent Multi-Channel Threat Detection For Enhanced Data Security Using Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 2, page no. ppf52-f59, February-2024, Available at : http://www.jetir.org/papers/JETIR2402506.pdf