Exploring Advanced Techniques in Computer and Network Forensics for Enhanced Cybersecurity

Because cybersecurity is continually changing, it’s crucial to have effective, adaptable defenses. This paper demonstrates a novel computer security method. This is done via blockchain technology, enhanced cryptography, threat intelligence integration, machine learning-based strangeness detection, and behavioral analysis. After carefully comparing our solution to others, we feel it is the best way to secure computer and network systems. The Machine Learning-Based Anomaly Identification approach accurately identifies anomalies using complex mathematical calculations and repetitive procedures to adjust parameters. Blockchain technology unites odd data into a decentralized ledger using encryption for forensics efficiency. Homomorphic encryption and quantum-resistant signatures protect the program against new attacks. The proposed method exhibits superior performance across various metrics, achieving a detection accuracy of 98.5% with a low false positive rate of 1.0%. Visualizations elucidate trade-offs, trends, and classification metrics. This cybersecurity framework offers a promising solution for addressing the dynamic challenges posed by a wide range of cybersecurity threats. Its adaptability, versatility, and collective efficacy position it as a noteworthy advancement in the pursuit of securing digital environments from evolving and sophisticated cyber threats.

I. Introduction

New computer and network analysis tools must be researched and applied to keep ahead of malicious actors since cybersecurity is continually changing. Hackers’ methods evolve with technology [1]. This is why cybersecurity specialists must explore innovative methods to discover, evaluate, and combat cyber threats. What You Must Do: This research highlights the ingenuity of computer and network security professionals. We will look at employing machine learning to identify hazardous behaviour, blockchain technology to encrypt data, and other security measures [2–4]. These technologies seek to enhance security and protect businesses from emerging cyberthreats. This article discusses three key features of advanced computer and network forensics. Applications of Machine Learning in Forensic Science: The ultimate objective of real-time risk detection is to use machine learning in forensic investigations to identify patterns and flaws. b. Blockchain Technology for Immutable Evidence: Exploring how blockchain technology might prevent digital evidence tampering [5]. Cryptography and Cyber Investigations: Future Perspectives To protect sensitive information, forensic investigators use homomorphic and post-quantum encryption. Integrating threat intelligence: Forensic investigations may use threat information streams to detect and prevent new cyber risks. People have an important role in developing trustworthy cybersecurity [6]. Technology can examine user behaviour and build psychological profiles. Computer and network forensics advanced approaches are covered in this books Readers may increase their cybersecurity defences against a constantly shifting threat landscape with this knowledge [7]. We’ll examine each contribution in detail in the following sections to give cybersecurity professionals and academics a complete picture and relevant recommendations.