According to the latest Verizon DBIR report, credential abuse, including password reuse and human factors in password creation, remains the leading attack vector. It was revealed that most users change their passwords only when they forget them, and 35% of respondents find mandatory password rotation policies inconvenient. These findings highlight the importance of combining technical solutions with user-focused education to strengthen password security. In this research, the “human factor in the creation of usernames and passwords” is considered a vulnerability, as identifying the patterns or rules used by users in password generation can significantly reduce the number of possible combinations that attackers need to try in order to gain access to personal data. The proposed method based on an LSTM model operates at a character level, detecting recurrent structures and generating generalized masks that reflect the most common components in password creation. Open datasets of 31,000 compromised passwords from real-world leaks were used to train the model and it achieved over 90% test accuracy without signs of overfitting. A new method of evaluating the individual password creation habits of users and automatically fetching context-rich keywords from a user’s public web and social media footprint via a keyword-extraction algorithm is developed, and this approach is incorporated into a web application that allows clients to locally fine-tune an LSTM model locally, run it through ONNX, and carry out all inference on-device, ensuring complete data confidentiality and adherence to privacy regulations.

Analog digital video recorders (DVRs) are still extensively used in small-to-medium business and home security systems, but there are special problems when it comes to forensic recovery of video evidence in these systems that are not covered by tools or methodology. Compared to the IP-based network video recorders, analog DVRs packetize video frames of several coaxial-connected cameras into a single interleaved binary stream on disk, necessitating channel demultiplexing before single camera footage can be reassembled. In this paper, we discuss a multi-channel analog Dahua DVR system utilizing the DHAV frame format, with a focus on the forensic recovery approach. Three significant contributions are presented in the methodology: (1) a channel demultiplexing algorithm that separates interleaved frames with up to 32 cameras on the basis of embedded channel identifiers and temporal coherence analysis; (2) a frame sequence stitching mechanism to reassemble continuous video segments on the basis of non-contiguous disk fragments using adaptive frame number tolerance (±3 frames) and temporal validation (≤1 second difference); and (3) a native C implementation with Win32 GUI providing significant performance improvements over interpreted alternatives. The system was tested on 14 analog Dahua DVR hard drives of various models, with a 92.3% recovery rate (97.1% on hard drives with no hardware damage), 91.3% temporal accuracy, 97.5% channel separation accuracy and a 1.8% false positive rate. The methodology fills an important gap in the literature of surveillance forensics, where current studies have only concentrated on IP-based digital systems, and analog DVRs form an estimated 35–40% of operational surveillance systems across emerging markets. The channel demultiplexing capability, which is not found in any current commercial or academic tool, enables automated per-camera organization of interleaved streams, converting what was previously a manual multi-day process into an automated one.