In the dynamic landscape of digital forensics, the integration of Artificial
Intelligence (AI) and Machine Learning (ML) stands as a transformative
technology, poised to amplify the efficiency and precision of digital forensics
investigations. However, the use of ML and AI in digital forensics is still in
its nascent stages. As a result, this paper gives a thorough and in-depth
analysis that goes beyond a simple survey and review. The goal is to look
closely at how AI and ML techniques are used in digital forensics and incident
response. This research explores cutting-edge research initiatives that cross
domains such as data collection and recovery, the intricate reconstruction of
cybercrime timelines, robust big data analysis, pattern recognition,
safeguarding the chain of custody, and orchestrating responsive strategies to
hacking incidents. This endeavour digs far beneath the surface to unearth the
intricate ways AI-driven methodologies are shaping these crucial facets of
digital forensics practice. While the promise of AI in digital forensics is
evident, the challenges arising from increasing database sizes and evolving
criminal tactics necessitate ongoing collaborative research and refinement
within the digital forensics profession. This study examines the contributions,
limitations, and gaps in the existing research, shedding light on the potential
and limitations of AI and ML techniques. By exploring these different research
areas, we highlight the critical need for strategic planning, continual
research, and development to unlock AI's full potential in digital forensics
and incident response. Ultimately, this paper underscores the significance of
AI and ML integration in digital forensics, offering insights into their
benefits, drawbacks, and broader implications for tackling modern cyber
threats.

人工智能和机器学习技术在数字取证和事件响应中的应用以及其所涉及的研究领域和主题进行了彻底的分析，揭示了其在数据收集与恢复、网络犯罪时间线重建、大数据分析、模式识别和确保物证链的安全、并针对黑客事件进行响应策略方面的关键作用和局限性。

现代数字取证与事件响应中人工智能和机器学习角色的综合分析

A Comprehensive Analysis of the Role of Artificial Intelligence and  Machine Learning in Modern Digital Forensics and Incident Response

Nowadays, the environments of smart systems for Industry 4.0 and Internet of
Things (IoT) are experiencing fast industrial upgrading. Big data technologies
such as design making, event detection, and classification are developed to
help manufacturing organizations to achieve smart systems. By applying data
analysis, the potential values of rich data can be maximized and thus help
manufacturing organizations to finish another round of upgrading. In this
paper, we propose two new algorithms with respect to big data analysis, namely
UFC$_{gen}$ and UFC$_{fast}$. Both algorithms are designed to collect three
types of patterns to help people determine the market positions for different
product combinations. We compare these algorithms on various types of datasets,
both real and synthetic. The experimental results show that both algorithms can
successfully achieve pattern classification by utilizing three different types
of interesting patterns from all candidate patterns based on user-specified
thresholds of utility and frequency. Furthermore, the list-based UFC$_{fast}$
algorithm outperforms the level-wise-based UFC$_{gen}$ algorithm in terms of
both execution time and memory consumption.

本文研究采用大数据技术设计新的 UFC$_{gen}$ 和 UFC$_{fast}$ 算法，用于收集三种类型的模式，以帮助人们确定不同产品组合的市场位置，并在真实和模拟数据集上比较这两种算法，结果表明基于列表的 UFC$_{fast}$ 算法在执行时间和内存消耗方面优于基于层次的 UFC$_{gen}$ 算法。

智能系统：联合功用和频率进行模式分类

Smart System: Joint Utility and Frequency for Pattern Classification

Thanks to the rapid development of deep learning, big data analysis
technology is not only widely used in the field of natural language processing,
but also more mature in the field of numerical prediction. It is of great
significance for the subject heat prediction and analysis of science and
technology demand data. How to apply theme features to accurately predict the
theme heat of science and technology demand is the core to solve this problem.
In this paper, a prediction method of subject heat of science and technology
demand based on time convolution network (TCN) is proposed to obtain the
subject feature representation of science and technology demand. Time series
prediction is carried out based on TCN network and self attention mechanism,
which increases the accuracy of subject heat prediction of science and
technology demand data Experiments show that the prediction accuracy of this
algorithm is better than other time series prediction methods on the real
science and technology demand datasets.

本文提出了一种基于时间卷积网络（TCN）的科技需求主题热度预测方法，利用主题特征进行科技需求主题热度的时间序列预测，通过实验结果表明该算法在真实科技需求数据集上的预测准确度优于其他时间序列预测方法。