The extensive growth of Industrial Internet of Things (IIoT) has prompted cyber attackers to commence a variety of attacks which need to be identified to maintain the security of industrial data and services. Digital forensics is an extravagant need to detect cyber attacks and to identify cybercriminals. While conducting criminal investigation using digital forensics, there is high probability of leakage of collected digital evidence. Hence, the need for a secure and lightweight cryptosystem arises to protect it from cyber-attacks. In the present paper, authors aim to design a novel forensic framework for digital evidence administration using advanced encryption and preservation mechanisms. Authors focus to encrypt and preserve only digital image evidence but can be deployed on other types of multimedia data like video frames. Three types of Chaos-map algorithms namely 4D hyperchaotic map, 2D Arnold Cat map (ACM) and Henon Map have been utilized to encrypty and decrypt digital image evidence which enable high sensitivity and dependency on initial conditions, quasi-randomness, and ergodicity to digital content. The proposed approaches have been analyzed on different groups of images of different size and validated using various security parameters such as unified average changing intensity (UACI), correlation coefficient, and the number of pixels changing rate (NPCR). Image transformation and compression has been performed after encryption process using Fast Fourier Transformation (FFT). The proposed work has been analyzed using histogram and auto-correlation. Experimentations have provided the values of the correlation coefficient of plain images near 1 and the value of adjacent cipher images near zero which show a strong correlation. The evaluated results for UACI are more than 33 % for every considered images. The value for NPCR should be near 100 % and here the calculated results for each image are above 99 %. The complexity of the proposed encryption algorithm is O(n) due to key generation using a cryptographically secure pseudo-random number generator (CSPRNG). The compressed and transformed image evidence have been stored into decentralized blockchain to ensure the integrity and confidentiality. Decentralized blockchain has been used as a reliable and promising solution to preserve digital evidence due to its distinct features like secure peer to peer communication, improved trust and transparency among participants, integrity, confidentiality, immutability, without risk of single point-of-failure. The proposed digital forensic model is able to prevent various threats and security challenges which can contaminate or tamper off digital pieces of evidence and assures improved digital evidence administration and defensibility. In the proposed scheme, the encryption process takes only 2–3 s to perform the encryption of each size of image.

工业物联网(IIoT) 的广泛增长促使网络攻击者发起各种攻击，需要识别这些攻击以维护工业数据和服务的安全。数字取证是检测网络攻击和识别网络犯罪分子的巨大需求。在利用数字取证进行刑事调查时，收集到的数字证据很可能会泄露。因此，需要一种安全且轻量级的密码系统来保护其免受网络攻击。在本文中，作者旨在使用先进的加密和保存机制设计一种新颖的数字证据管理取证框架。作者专注于加密和保留数字图像证据，但可以部署在视频帧等其他类型的多媒体数据上。三种类型的混沌图算法，即 4D 超混沌图、2D Arnold Cat 图 (ACM) 和 Henon Map 已用于加密和解密数字图像证据，这些算法使数字图像证据具有高灵敏度和对初始条件的依赖性、准随机性和遍历性内容。所提出的方法已经在不同大小的不同组图像上进行了分析，并使用各种安全参数进行了验证，例如统一平均变化强度（UACI）、相关系数和像素变化率（NPCR）数量。在使用快速傅立叶变换（FFT）加密处理后执行图像变换和压缩。所提出的工作已使用直方图和自相关进行了分析。实验表明，明文图像的相关系数值接近1，相邻密文图像的相关系数值接近0，表现出很强的相关性。对于每个考虑的图像，UACI 的评估结果都超过 33%。 NPCR 的值应接近 100%，此处每幅图像的计算结果均高于 99%。由于使用加密安全伪随机数生成器 (CSPRNG) 生成密钥，因此所提出的加密算法的复杂度为 O(n)。压缩和转换后的图像证据已存储到去中心化区块链中，以确保完整性和机密性。去中心化区块链已被用作保存数字证据的可靠且有前景的解决方案，因为它具有安全的点对点通信、提高参与者之间的信任和透明度、完整性、机密性、不变性以及无单点故障风险等独特功能。所提出的数字取证模型能够防止可能污染或篡改数字证据的各种威胁和安全挑战，并确保改进数字证据管理和防御性。在所提出的方案中，加密过程只需要2-3秒即可完成每个尺寸图像的加密。