Research on Intelligent Cyber Defense Architecture: Integrating Real-Time Vulnerability Scanning, Asymmetric Encryption, and Network Reconnaissance in Modern Attack Surfaces
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Abstract
In the field of cybersecurity, ethical hackers or cyber forensics professionals require a combined automated data-driven systems to amplify threat detection and digital forensics operations in this contemporary era. The contemporary applications require security analysts to share classified data by sending it to the third party remote cloud servers, which creates data exposure and invasion of privacy because they need internal network packet captures and delicate binary payloads. In the cybersecurity study, the latest results of archival datasets and signature-based procedures, which get 84 to 87 percent phishing detection precision because this study offers a system that provides better results through its guarded network data safeguarding procedures. This study produces a two tier architecture system which uses the latest AI technology and native forensic engines for data gathering through user input and file value analysis which includes. PCAP, EXIF, binary strings, and hashes and the system improves current threat intelligence. The system reaches 97.4 percent precision, which surpasses the earlier 84 to 87 percent precision range by a remarkable margin. This research is based on localised architecture that works well with powerful AI and forensic engines that capture localised data by blending the user’s inputs and file values (PCAP strings, EXIF, Hex signatures), in addition with their particular analytic needs. This research achieved 97.4 percent precision that is vastly superior and surpassing the old 84 to 87 percent range. To ensure the safe management of the private data, all procedures are done on the user’s own downloaded system environment instead of being sent over the Internet. By using HTML5 Web Workers, massive forensic processes, such as the extraction of malicious strings and QR matrix decoding are done totally on the user’s local memory. Additionally, it also provides a chat assistant service (J.A.R.V.I.S.) that provides the outcome of the inquiry that the user asks. This chat note is based on a traditional Multi layer Perceptron (MLP) neural network and TF-IDF encoding
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