Journal of Intelligent Systems and Internet of Things

Internet of Things (IoT) devices enter the vulnerability record through firmware advisories that are scored, catalogued and enriched by institutions under strain. We separate device CVEs from the rest of the National Vulnerability Database using product enumerations and characterise those published from January 2022 to June 2026 with five models. They are more severe. A quarter are Critical against 13.7% of other CVEs, memory-safety and command-injection flaws each carry an odds ratio above three, and a single router vendor accounts for one device CVE in eight. An expectation–maximisation latent class model finds eight attack profiles. A logistic text classifier trained on enriched records (AUC 0.98), combined with adjusted classify-and-count, recovers device CVEs that NIST never enriched and raises the 2025 count by about a third, although the decline in the device share survives the correction. Topic models are dominated by reporting templates. They also record a rise in end-of-life language, from 0.1% to 4.8% of device CVEs. With severity and weakness held fixed, device CVEs are less likely to reach the Known Exploited Vulnerabilities catalog (hazard ratio 0.61), which suggests that their high scores overstate observed exploitation.

IoT intrusion-detection studies often treat a benchmark as adequate once it is large, recent, and widely used. That assumption can fail. This study audits five public IoT and IIoT security benchmarks—CICIoT2023, Edge-IIoTset, ToN-IoT, BoT-IoT, and IoT-23—from their published class populations and introduces no synthetic classifier results; the audit combines Shannon entropy, Hill diversity, Simpson concentration, Gini inequality, rank-frequency tail fitting, rare-class observation probability, Wilson uncertainty, and a constrained power-tempered sampling model. Across the five corpora, record counts range from 461,043 to 73,370,443, normalized entropy from 0.421 to 0.600, and effective Shannon diversity from 2.20 to 3.98 classes. Size is a poor proxy for label adequacy. IoT-23 is the clearest case: its 6.05 million records include a singleton class, so a random 1% sample has only about a 1%probability of observing that label, while the worst-case Wilson half-width for a class-conditioned proportion is 0.445. We then define qi(τ) = piτ /∑k pτk and prove that its entropy is non-increasing in τ; at a 1% training budget with a target of 25 expected observations in the rarest class, the minimum-flattening exponent is 1.000 for CICIoT2023, 0.855 for Edge-IIoTset, 0.807 for ToN-IoT, 0.954 for BoT-IoT, and 0.450 for IoT-23. The last case needs an approximately 2,500-fold uplift of the singleton label. Such an uplift changes training exposure to one observation; it cannot create independent evidence. The resulting audit protocol therefore separates training rebalancing from evaluation validity and gives quantitative checks that can be applied before IoT benchmark results are interpreted as class-wide security evidence.

Internet of Things gateways, edge controllers and smart-home hubs run on open-source packages, so the advisories published for those packages are the signal that intelligent vulnerability-monitoring systems must interpret. We model that signal as a stochastic process, using every reviewed advisory in the GitHub Advisory Database as of 31 July 2026 across eight package ecosystems. Binary segmentation places a regime change in February 2026, after which monthly volume rose almost fivefold, driven by maintainer-written advisories that now usually precede the National Vulnerability Database. A modulated Hawkes process, estimated by expectation–maximisation, separates batch release from triggering and finds that nearly half of advisories arrive in same-hour batches, while self-excitation between batches is weak (branching ratio 0.05, half-life eight hours). Risk is concentrated. Advisories per package are heavy-tailed (exponent 2.14), and a beta-binomial empirical Bayes model concentrates the 12% of unpatched entries in a small set of packages. IoT-related packages carry more Critical advisories (22% against 13%) and more unpatched entries (18% against 12%). An adaptive early-warning score derived from the fitted process anticipates follow-up disclosures within seven days with an AUC of 0.75, equal to the strongest heuristic and interpretable, although its probabilities run low during the surge.

The tertiary industry is increasingly influenced by digital technologies that support service efficiency, data-driven decision-making and sustainability-oriented transformation. This study reviews the way the Internet of Things (IoT) and Big Data Analytics (BDA) contribute to sustainability in service sectors, including healthcare, education, tourism, hospitality, logistics, transport, finance and smart-city services. A systematic literature review and bibliometric analysis were conducted using Scopus and Web of Science for records dated from 2015 through 28 February 2025. From 1,969 initial records, 551 were used for bibliometric and metadata analysis and 136 studies were included in qualitative synthesis. The publication trend, productive sources, authors, countries, subject areas, keywords and science-mapping pattern were studied by using Bibliometrix, Biblioshiny and VOSviewer. The findings show rapid growth after 2019 and strong concentration in computer science, engineering, smart healthcare, smart cities and digital sustainability research. The main contribution is an integrated service-sector synthesis showing that IoT functions as the sensing and connectivity layer, while BDA, AI, cloud-edge architectures, blockchain and predictive analytics convert service data into monitoring, optimization, decision support and automation. Sustainability outcomes appear across environmental, economic and social dimensions, including energy savings, emissions reduction, waste recovery, cost efficiency, healthcare access, inclusion and service quality. Finally, the review concludes that IoT and BDA, among other factors, have sustainable value if supported by governance, data quality, cybersecurity, interoperability, skills and organizational readiness.