The advancement of cryptographic systems presents both opportunities and challenges in the realm of digital forensics. In an era where the security of digital information is crucial, the ability to non-invasively detect and analyze cryptographic configurations has become significant. As cryptographic algorithms become more robust with longer key lengths, they provide higher levels of security. However, non-invasive side channels, specifically through electromagnetic (EM) emanations, can expose confidential cryptographic details, thus presenting a novel solution to the pressing forensic challenge. This research delves into the capabilities of EM side-channel analysis (EM-SCA), specifically focusing on detecting both cryptographic key lengths and the algorithms employed utilizing a machine-learning-based approach, which can be instrumental for digital forensic experts during their investigations. Through meticulous data processing and analysis, the Support Vector Machine (SVM) model, among others, demonstrated a notable accuracy of 94.55% in distinguishing between AES and ECC cryptographic operations. This capability significantly enhances digital forensic methodologies, offering a novel avenue for noninvasively uncovering encrypted data’s cryptographic settings. By identifying key lengths and algorithms without invasive procedures, this research contributes substantially to the advancement of forensic investigations in encrypted environments.

Cryptography is vital for data security, but cryptographic algorithms can still be vulnerable to side-channel attacks (SCAs), physical assaults exploiting power consumption and EM radiation. SCAs pose a significant threat to cryptographic integrity, compromising device keys. While literature on SCAs focuses on real-world devices, the rise of sophisticated devices necessitates fresh approaches. Electromagnetic side-channel analysis (EM-SCA) gathers information by monitoring EM radiation, capable of retrieving encryption keys and detecting malicious activity. This study evaluates EM-SCA's impact on encryption across scenarios and explores its role in digital forensics and law enforcement. Addressing encryption susceptibility to EM-SCA can empower forensic investigators in overcoming encryption challenges, maintaining their crucial role in law enforcement. Additionally, the paper defines EM-SCA's current state in attacking encryption, highlighting vulnerable and resistant encryption algorithms and devices, and promising EM-SCA approaches. This study offers a comprehensive analysis of EM-SCA in law enforcement and digital forensics, suggesting avenues for further research.

In the era of information explosion in e-commerce, users face an overload problem. Recommender systems alleviate this by filtering target products based on user preferences. Collaborative Filtering (CF) is a popular method but struggles with high-quality recommendations due to data sparsity. Utilizing review texts to capture user preferences has improved CF recommendations. This paper proposes a deep learning-based recommendation framework leveraging bidirectional gated recurrent units (Bi-GRUs) and an attention-based Recurrent Neural Networks (RNN) structure. This framework addresses insufficient user rating data by utilizing user reviews to infer preferences. Experimental results on three datasets show that the proposed Bi-GRUCF model outperforms other state-of-the-art methods, achieving a 55.4% improvement in Mean Absolute Error (MAE) over traditional methods and a 77.8% improvement over other deep learning approaches. This demonstrates the framework's superior recommendation quality, particularly in sparse data scenarios.