The number of online social network (OSN) users is increasing daily and attacks and threats against over the time spent on online networks has been increasing equally. Attacks against OSN users exploit not only system vulnerabilities but also user-induced vulnerabilities, which naturally affect the hacker’s attack strategy as well. This study is designed to investigate the effect of social media user behaviors on their vulnerability level in terms of security and privacy. The study was conducted survey methods, which was applied to social media users in two countries -Turkey and Iraq. This study documents and analyzes the behaviors of 700 OSN users in two countries. This study examines the behaviors of social media users from two nationalities, investigating whether there is a relationship between social media users’ behaviors and security and privacy threats. Research findings demonstrate that there is a significant relationship between OSN users’ behaviors and their attitudes towards security and privacy. Additionally, Turkish social media users pay more attention to their behaviors in terms of privacy and security awareness than Iraq users.

Underwater acoustic sensor networks (UASNs) are fundamental assets to enable discovery and utilization of sub-sea environments and have attracted both academia and industry to execute long-term underwater missions. Given the heightened significance of battery dependency in underwater wireless sensor networks, our objective is to maximize the amount of harvested energy underwater by adopting the TDMA time slot scheduling approach to prolong the operational lifetime of the sensors. In this study, we considered the spatial uncertainty of underwater ambient resources to improve the utilization of available energy and examine a stochastic model for piezoelectric energy harvesting. Considering a realistic channel and environment condition, a novel multi-agent reinforcement learning algorithm is proposed. Nodes observe and learn from their choice of transmission slots based on the available energy in the underwater medium and autonomously adapt their communication slots to their energy harvesting conditions instead of relying on the cluster head. In the numerical results, we present the impact of piezoelectric energy harvesting and harvesting awareness on three lifetime metrics. We observe that energy harvesting contributes to 4% improvement in first node dead (FND), 14% improvement in half node dead (HND), and 22% improvement in last node dead (LND). Additionally, the harvesting-aware TDMA-RL method further increases HND by 17% and LND by 38%. Our results show that the proposed method improves in-cluster communication time interval utilization and outperforms traditional time slot allocation methods in terms of throughput and energy harvesting efficiency.

Cloud-based telephony platforms face growing fraud risks including voice phishing (vishing), subscription abuse, and organizational impersonation, with detection being especially challenging in low-resource languages such as Arabic. We present an Artificial Intelligence (AI)-based security architecture for fraud detection in Arabic cloud call centers, combining onboarding verification, behavioral monitoring, domain-adapted Automatic Speech Recognition (ASR), semantic transcript search, and Large Language Model (LLM)-based entity verification. The domain-adapted Langa ASR model achieves a Word Error Rate (WER) of 41.0% and Character Error Rate (CER) of 18.2%, outperforming all evaluated commercial baselines. LLM-based entity extraction with multi-call consensus achieves 97.3% company-name accuracy (Generative Pre-trained Transformer 4, GPT-4) and 92.0% in the cost-effective deployed configuration (GPT-3.5 with log-probability filtering). Evaluated on production data from a Middle East and North Africa (MENA)-region provider spanning more than 1000 accounts, the pipeline flagged 47 accounts of which 41 were confirmed fraudulent (directly observed precision 87.2%, 95% confidence interval (CI): 74.3–95.2%; estimated recall 51–82% under conservative base-rate assumptions—not directly measured), providing evidence for the viability of a unified, threat-model-driven architecture for low-resource telephony fraud detection.

Reliable evaluation resources for semantic deduplication, semantic textual similarity (STS), and retrieval remain limited for low-resource and morphologically rich languages. This study introduces KazakhTextDuplicates, a Kazakh-language benchmark for controlled evaluation of semantic duplication under varying levels of semantic preservation and surface-form distortion. The benchmark includes two complementary versions. KazakhTextDuplicates v1.0 is a diagnostic dataset derived from naturally occurring duplicate and near-duplicate pairs for analyzing relationships between duplication labels and lexical overlap. KazakhTextDuplicates v2.0 is a controlled benchmark created using deterministic transformations that define seven semantic duplication regimes: exact duplication, contextual reformulation, paraphrasing, partial semantic overlap, and three levels of character-level noise. Each pair is assigned both a regime label and a predefined similarity score, enabling evaluation across duplicate classification, STS, and retrieval tasks. Five embedding models (BGE-M3, LaBSE, Multilingual-E5-Large, KazEmbed-v5, and OpenAI text-embedding-3-large) were evaluated in a zero-shot setting. Results show that v1.0 is strongly affected by surface-form similarity and is therefore more suitable for diagnostic analysis. In contrast, v2.0 provides a more challenging and informative evaluation environment. OpenAI text-embedding-3-large achieved the strongest STS performance (Pearson = 0.510, Spearman = 0.652, MSE = 0.075) and the best duplicate regime classification results (Accuracy = 0.155, Macro-F1 = 0.066). Retrieval performance remained strong at higher cutoffs despite lower first-rank stability. The results demonstrate that benchmark design substantially affects semantic similarity evaluation and emphasize the need for controlled assessment in low-resource agglutinative languages.

The availability of large, structured legal datasets is essential for advancing legal natural language processing, information retrieval, and the development of reliable language technologies. However, such resources remain scarce for Kazakh, a morphologically rich and comparatively low-resource language, limiting the development and evaluation of domain-specific NLP methods. This data descriptor presents KazakhLawCorpus, a curated corpus of 215,889 document-level legislative metadata records from the Republic of Kazakhstan. The dataset was derived from a PostgreSQL export containing 223,245 source records and constructed through Kazakh-language field selection, essential-field filtering, source-preserving normalization, schema reconstruction, and systematic quality validation, resulting in a retention rate of 96.705%. The final release comprises 25 fields covering document identifiers, temporal attributes, legal status, registry information, Kazakh-language titles and descriptions, legal and institutional categories, geographical information, and source URLs. The records cover legislative acts adopted between 30 October 1947 and 20 July 2026. Quality-control procedures confirmed unique law_id values, complete title and description fields, and parseable adoption dates, while 4635 records with potential source-level or historical anomalies were retained and transparently documented through diagnostic flags. KazakhLawCorpus provides a large-scale, traceable resource for metadata-based legal information retrieval, document classification, temporal and institutional analysis, provenance tracking, and the development of downstream Kazakh legal NLP resources.