Exploring the Efficacy of a Novel Support Vector Neural Network for Anomaly Detection in Digital Forensics Data

Sofia Hana^*
*Correspondence: Sofia Hana, Department of Criminal Justice, Institute of Forensic Science, le batochime, 1015 Lausanne-Dorigny, Switzerland, Email:

Keywords

Anomaly detection • Digital forensics • Machine learning

Introduction

As digital technologies proliferate, the volume and complexity of digital data continue to escalate, posing challenges for forensic investigators in identifying anomalous behavior indicative of security breaches or malicious activities. Traditional methods for anomaly detection often struggle to cope with the dynamic and diverse nature of digital forensics datasets. In recent years, machine learning techniques, including Support Vector Neural Networks (SVNN), have emerged as promising tools for enhancing anomaly detection capabilities in digital forensics. This mini-review aims to explore the efficacy of SVNN in detecting anomalies within digital forensic datasets.

Literature Review

SVNN integrates the strengths of both Support Vector Machines (SVM) and Neural Networks (NN) to leverage their complementary capabilities in handling complex datasets. SVM excels in separating data points in highdimensional spaces by constructing hyperplanes with maximum margins, while NN offers flexibility in capturing nonlinear relationships and patterns within the data. By combining these techniques, SVNN can effectively model intricate data distributions and identify subtle anomalies that may evade traditional methods [1].

In digital forensics, SVNN has shown promising results in detecting various types of anomalies, including network intrusions, insider threats, fraudulent transactions, and data exfiltration attempts. By leveraging features extracted from diverse sources such as network traffic logs, system event logs, and user behavior patterns, SVNN can discern abnormal patterns indicative of malicious activities or security breaches. Moreover, SVNN's ability to adapt to evolving threats and learn from historical data enhances its utility in real-world forensic investigations [2].

Discussion

Compared to traditional anomaly detection techniques, SVNN offers several advantages, including:

SVNN can accommodate diverse data types and adapt to evolving forensic scenarios, making it suitable for dynamic and complex digital environments.

SVNN's ability to handle noisy and high-dimensional datasets enables robust detection of anomalies amidst background noise and variability.

SVNN can scale efficiently to large-scale forensic datasets, facilitating timely detection and response to anomalous activities across enterprise networks or digital systems [3].

SVNN's hybrid architecture combines the interpretability of SVM with the nonlinear modeling capabilities of NN, enabling forensic investigators to understand and interpret detection outcomes effectively.

Despite its promise, SVNN-based anomaly detection in digital forensics faces several challenges, including:

Imbalanced datasets and skewed class distributions may hinder SVNN's performance and lead to biased anomaly detection outcomes.

Extracting informative features and representing complex forensic data in a suitable format for SVNN pose challenges, requiring domain expertise and careful preprocessing.

Enhancing the interpretability of SVNN models is essential for forensic investigators to trust and validate detection outcomes, necessitating techniques for explaining model decisions and highlighting salient features [4].

Future research directions for SVNN-based anomaly detection in digital forensics include:

Integrating ensemble learning methods to combine multiple SVNN models and improve detection accuracy while addressing data heterogeneity and imbalances [5].

Leveraging transfer learning techniques to transfer knowledge from related domains or pre-trained models to enhance SVNN's performance on limited forensic datasets.

Developing techniques for explaining SVNN's decision-making process and providing transparent insights into detection outcomes to enhance forensic interpretability and trustworthiness [6].

Conclusion

SVNN holds promise as a powerful tool for anomaly detection in digital forensics, offering flexibility, robustness, and scalability in identifying suspicious activities and security breaches. By leveraging its hybrid architecture and machine learning capabilities, SVNN enables forensic investigators to detect subtle anomalies amidst complex digital environments effectively. Addressing challenges such as data imbalance and model interpretability, and exploring future research directions will further enhance SVNN's utility in digital forensics investigations, ultimately bolstering cybersecurity defenses and safeguarding digital assets against emerging threats.

Acknowledgement

None.

Conflict of Interest

None.

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