Intelligent threat detection and prevention in REST APIs using machine learning

IntroductionIn swine disease surveillance, obtaining labeled data for supervised learning models can be challenging because many farms lack standardized diagnostic routines and consistent health monitoring systems. Unsupervised learning is particularly suitable in such scenarios because it does not require labeled data, allowing for detecting anomalies without predefined labels. This study evaluates the effectiveness of unsupervised machine learning models in detecting anomalies in productivity indicators in swine breeding herds.MethodsAnomalies, defined as deviations from expected patterns, were identified in indicators such as abortions per 1000 sows, prenatal losses, preweaning mortality, total born, liveborn, culled sows per 1000 sows, and dead sows per 1000 sows. Three unsupervised models - Isolation Forest, Autoencoder, and K-Nearest Neighbors (KNN) - were applied to data from two swine production systems. The herd-week was used as the unit of analysis, and anomaly scores above the 75th percentile were used to flag anomalous weeks. A permutation test assessed differences between anomalous and non-anomalous weeks. Performance was evaluated using F1-score, precision, and recall, with true anomalous weeks defined as those coinciding with reported health challenges, including porcine reproductive and respiratory syndrome (PRRS) and Seneca Valley virus outbreaks. A total of 8,044 weeks were analyzed.ResultsThe models identified 336 anomalous weeks and 1,008 non-anomalous weeks in Production System 1, and 1,675 anomalous weeks and 5,025 non-anomalous weeks in Production System 2. The results from the permutation test revealed significant differences in productivity indicators between anomalous and non-anomalous weeks, especially during PRRS outbreaks, with more subtle changes observed during Seneca Valley virus outbreaks. The models performed well in detecting the PRRSV anomaly, achieving perfect precision (100%) across all models for both production systems. For anomalies like SVV the models showed lower performance compared to PRRSV.DiscussionThese findings suggest that unsupervised machine learning models are promising tools for early disease detection in swine herds, as they can identify anomalies in productivity data that may signal health challenges.

In this research, we explore the viability of using a biologically-inspired design to develop a host-based next generation firewall. Many of the firewall designs in use today are still signature-based (with very few exceptions just released in 2020), which tend to struggle to protect against zero-day attacks and against encrypted traffic. By applying cellular membrane concepts such as endocytosis and ligand-receptor interactions along with machine learning, we created a feasible next generation firewall prototype capable of defending against zero-day threats even within encrypted traffic. Specifically, we built both supervised and unsupervised machine learning models, optimized their results, then we exported the models, and tested the exported models on zero-day malicious network traffic. Finally, we modified the open source Linux IP Tables firewall software to use our exported model as the packet filtering mechanism. The end result was a working host-based, supervised machine learning driven, next generation firewall prototype that demonstrated very promising results, nearly 100% of normal connections were passed and 77% of zero-day malicious connections were blocked.

Deploying unsupervised anomaly detection systems in heterogeneous smart home environments is hindered by the need for costly, per-site hyperparameter tuning. This paper addresses the critical challenge of hyperparameter transferability for creating zero-tune, plug-and-play security solutions. We systematically evaluate five unsupervised machine learning models [Elliptic Envelope (EE), Isolation Forest (IF), Local Outlier Factor (LoF), One-Class SVM (oSVM), and an Autoencoder (AE)] across five prominent IoT datasets. Using a rigorous dataset-specific hyperparameter tuning approach, we benchmark the performance of transferred configurations against both per-dataset optimization and default settings. Our findings establish a clear performance hierarchy: while dataset-specific tuning remains the gold standard, an intelligent transfer strategy significantly outperforms default configurations. Notably, we identify the IoTID20 dataset as the most effective source. Our quantitative topological analysis supports this, revealing that IoTID20’s high feature space complexity and cluster overlap (evidenced by low Silhouette scores) create a rigorous training environment that produces robust, portable hyperparameters. Furthermore, our analysis reveals a strategic trade-off: Autoencoders and LoF deliver the highest absolute performance, whereas IF offers the most substantial improvement over default settings. This work provides a quantitative framework for dataset-driven initialization, guiding the development of robust, low-maintenance intrusion detection systems.

It is challenging to predict which patients who meet criteria for subcortical ischemic vascular disease (SIVD) will ultimately progress to subcortical vascular cognitive impairment (SVCI). We collected clinical information, neuropsychological assessments, T1 imaging, diffusion tensor imaging, and resting-state functional magnetic resonance imaging from 83 patients with SVCI and 53 age-matched patients with SIVD without cognitive impairment. We built an unsupervised machine learning model to isolate patients with SVCI. The model was validated using multimodal data from an external cohort comprising 45 patients with SVCI and 32 patients with SIVD without cognitive impairment. The accuracy, sensitivity, and specificity of the unsupervised machine learning model were 86.03%, 79.52%, and 96.23% and 80.52%, 71.11%, and 93.75% for internal and external cohort, respectively. We developed an accurate and accessible clinical tool which requires only data from routine imaging to predict patients at risk of progressing from SIVD to SVCI. Our unsupervised machine learning model provides an accurate and accessible clinical tool to predict patients at risk of progressing from subcortical ischemic vascular disease (SIVD) to subcortical vascular cognitive impairment (SVCI) and requires only data from imaging routinely used during the diagnosis of suspected SVCI. The model yields good accuracy, sensitivity, and specificity and is portable to other cohorts and to clinical practice to distinguish patients with SIVD at risk for progressing to SVCI. The model combines assessment of diffusion tensor imaging and functional magnetic resonance imaging measures in patients with SVCI to analyze whether the "disconnection hypothesis" contributes to functional and structural changes and to the clinical presentation of SVCI.

The COVID-19 pandemic is a global health concern that has spread around the globe. Machine Learning is promising in the fight against the COVID-19 pandemic. Machine learning and artificial intelligence have been employed by various healthcare providers, scientists, and clinicians in medical industries in the fight against COVID-19 disease. In this paper, we discuss the impact of the Covid-19 pandemic on alcohol consumption habit changes among healthcare workers in the United States during the first wave of the Covid-19 pandemic. We utilize multiple supervised and unsupervised machine learning methods and models such as decision trees, logistic regression, support vector machines, multilayer perceptron, XGBoost, CatBoost, LightGBM, AdaBoost, Chi-Squared Test, mutual information, KModes clustering and the synthetic minority oversampling technique on a mental health survey data obtained from the University of Michigan Inter-University Consortium for Political and Social Research to investigate the links between COVID-19-related deleterious effects and changes in alcohol consumption habits among healthcare workers. Through the interpretation of the supervised and unsupervised methods, we have concluded that healthcare workers whose children stayed home during the first wave in the US consumed more alcohol. We also found that the work schedule changes due to the Covid-19 pandemic led to a change in alcohol use habits. Changes in food consumption, age, gender, geographical characteristics, changes in sleep habits, the amount of news consumption, and screen time are also important predictors of an increase in alcohol use among healthcare workers in the United States.

An unsupervised machine learning model that uses the mechanism of the Local Outlier Factor to flag and detect ambiguous as well as potentially fraudulent claims in Accidental and Healthcare insurance is proposed in this paper. It entirely automates the manual investigation of claims by claim appraisers in any organization. The ethos of this model is to comprehensively automate and expedite the claim investigation process using certain parameters to aid the claim appraiser’s workload of going through straightforward claims and saving their time to investigate more critical and complex claims. The model flags anomalous claims by comparing them to the model’s threshold, and input parameters and alerts are generated. These alerts generated are then investigated for fraud based on the parameters stated. The model can classify these claims and the cost of billable associated with these claims by reporting an accuracy of 99.5% for the Local Outlier Factor model in comparison with other implemented techniques of Isolation Forest which had an accuracy of only 78.37%. Our model has been tested and validated on real-world data and is showing promising results. Being able to identify and flag potentially fraudulent claims before they are paid out can save insurance companies a lot of money and resources.

Abstract—The landscape of API security risks has expanded considerably with the widespread adoption of APIs in modern software architectures. APIs, especially those used in critical enterprise applications and systems like Energy Storage Systems (ESS), have become prime targets for attackers due to their public exposure and accessibility. This article explores the evolving threat landscape of API security by examining common vulnera- bilities, attack techniques, and defense strategies specific to API implementations. We discuss the trade-offs between security and performance in different API communication protocols, such as RESTful APIs and GraphQL, and how these choices influence attack vectors and data protection. Additionally, we investigate how API usage patterns can be monitored to identify anomalies and potential security risks through advanced techniques like API embeddings, such as API2VEC. The article also addresses the challenges of securing APIs in environments where formal specifications or source code are unavailable, proposing behav- ioral analysis as a valuable tool for improving security. Lastly, we introduce a comprehensive learning framework for API security based on the OWASP API Security Top 10 risks, incorporat- ing gamification to enhance awareness and preparedness for emerging threats. Our research emphasizes the critical need for implementing proactive API security practices at every stage of the software development lifecycle to minimize risks and ensure a secure digital transformation. Index Terms—Endpoint Protection, API Exploits, OWASP API Security Risks, API Weaknesses, Cyber Threats, API Risk Environment, API Behavior Analysis, API Security Education.

This chapter presents comprehensive concepts on forensic analysis applied to Bitcoin through machine learning techniques. The study begins with a theoretical overview of Bitcoin and its ecosystem, detailing the decentralized nature of its blockchain and the challenges associated with its pseudonymous transactions. It then explores methods for acquiring and processing blockchain data, highlighting fundamental statistical analyses that reveal transaction patterns and anomalies. A section is also dedicated to the application of heuristics H1 and H2, which are essential for tracing mixed transactions. Additionally, we examine the concept of OSINT to enrich blockchain data with external intelligence, providing deeper insights into suspicious activities. Finally, the chapter explores the application of supervised and unsupervised machine learning models in Bitcoin forensics. These models are evaluated for their effectiveness in detecting illicit activities, identifying suspicious entities, and improving the accuracy of forensic investigations. The findings underscore the potential of combining machine learning with traditional forensic methods to enhance the overall robustness of Bitcoin investigations.

Federated learning is a collaborative and distributed machine learning model that addresses the privacy issues in centralized machine learning models. It emerges as a promising technique that addresses the data sharing concerns for data-private multi-institutional collaborations. However, most existing federated learning systems deal with centralized coordinators and are vulnerable to attacks and privacy breaches. We propose a blockchain-empowered coordinator-less decentralized, federated learning platform “Rahasak-ML” to solve issues in centralized coordinator-based federated learning systems by providing better transparency and trust. It uses an incremental learning approach to train the model by multiple peers in the blockchain network. Rahasak-ML is integrated into the Rahasak blockchain as its data analytics and machine learning platform. Each peer in the blockchain can establish supervised or unsupervised machine learning models with the existing data on its own off-chain storage. Once a peer generates a model, it can be incrementally/continuously trained and aggregated by other peers through the blockchain using the federated learning approach without requiring a centralized coordinator. The model parameters sharing, local model generation, incremental model training, and model sharing functions are implemented in the Rahasak-ML platform. We discussed the promise of Rahasak-ML machine learning in medicine.KeywordsFederated learningBlockchainMedicineDrug discoveryBig data

A continuous supply of electricity is necessary to maintain an acceptable standard of life, and the power distribution system's overhead line components play a crucial role in this matter. In Pakistan, identifying defective parts often necessitates human involvement. An unmanned aerial vehicle was used to gather a collection of 10,343 photos to automate this procedure. Using supervised and unsupervised machine learning methods, a number of automated anomaly detection systems were created. Support vector machine, random forest, VGG16, and ResNet50 were used as supervised machine learning models, and a convolutional auto‐encoder was used as the unsupervised machine learning model. VGG16 achieved the best accuracy of 99.00% while random forest achieved the worst accuracy of 72.49%. The convolutional auto‐encoder was successful in distinguishing between normal and abnormal components. The aforementioned machine learning models can be put on unmanned aerial vehicles to immediately identify defective parts.

Ecological restoration tightly winds urbanization, industrialization, and unsustainable tourism against the urgency of denoting innovative solutions for the same and converging its only imaginary and possible path along sustainable development. This study thus wants to put forward a new framework for synergizing eco-tourism and phytoremediation for waste management, ecological restorative performance, and conservation. The proposed analysis empirically assesses the effectiveness of plant-based phytoremediation in contaminated sites through machine learning techniques in aiding complementing eco-tourism in the establishment of an ecologically functional yet economically viable green landscape. Real-world datasets were analyzed against soil heavy metal concentrations, water quality indices, and tourism activity metrics through supervised and unsupervised machine learning models such as Random Forest and Support Vector Machines and K-means clustering. The findings conclude that ML models are capable of predicting and optimizing the efficiency of phytoremediation, as well as their potential to accomplish integration with eco-tourism and environmental conservation, driven by data strategy. This contributes to a sustainable framework from the policymakers' view, conservationist, and development of tourism.

Arctic charr (Salvelinus alpinus) is a salmonid species of high ecological and commercial value in the Holarctic region. Nevertheless, more information is needed about its underlying genetic diversity and population structure in the Nordics, especially regarding farmed populations. High-throughput sequencing was applied in three Arctic charr populations of anadromous or landlocked origin from Finland, Norway and Sweden. More specifically, the animals from the Swedish and Norwegian populations originated from a major egg supplier and producer, respectively. Furthermore, in the case of the Finnish population, the sampled animals originated from the only active conservation program for Arctic charr in the country with a potential interest in farming. Using double-digest restriction site-associated DNA sequencing (ddRAD-seq) on more than 500 fish, over 2000 single nucleotide polymorphisms (SNPs), both in the form of individual SNPs and as read haplotypes, were used to study the genetic diversity and structure of those populations. Genetic diversity metrics were similar between the Norwegian and the Swedish populations. However, substantially lower (40–50 %) genetic diversity was found in the Finnish population. Moreover, considerable genetic differentiation was implied between the studied populations as the mean fixation index (FST) was above 0.1 in all pairwise comparisons. All populations were easily discernible through either principal component analysis (PCA) or discriminant analysis of principal components (DAPC). In addition, unsupervised machine learning models such as K-means, Gaussian and Bayesian Gaussian mixtures were assessed for their ability to detect genetic clusters. A preceding dimensionality reduction step by PCA resulted in all three models, suggesting that the most probable number of clusters was three. Overall, our study affirmed the utility of the developed ddRAD-seq genotyping method and unveiled the genetic structure of the studied populations, both of which could contribute to their more efficient management by captive breeding.

This research examines passion and self-efficacy through experience and knowledge, as motivational factors that support entrepreneurs within the gig economy (GE). It sheds light on entrepreneurs’ sources of passion in the GE literature. The sample is composed of all the 1164 entrepreneurship activities offered worldwide through Airbnb, Tour by Locals, and Withlocals on 5 May 2022. The study is supported by unsupervised machine learning models and seeks to find latent topics emerging from the analysis of the entrepreneurs’ descriptions and exposes the main correlated clustered dimensions. There are six main motivators behind GE platforms as a first step toward entrepreneurship: Experience, Passion for share, Knowledge, Classic traditions, Empowered community and local activities, and Well-being. It also confirms the correlation between passion and self-efficacy through experience, pointing them as main factors behind entrepreneurship in the GE. Five of the six sources of passion previously pointed by theory were found: Passion for growth, for people, for product/service, for innovation and for social mission. This study discloses self-efficacy and the sources of passion and points directions to practitioners involved in entrepreneurial activities in the GE ecosystem. This work used machine learning models to access quantitatively a paradigm that is inductive by nature. The results point to well-being as a significant factor to be addressed in future research regarding entrepreneurship. This research only studies individuals involved in the GE; as such, further studies should cohort new populations from different fields.

We report a framework for calculating the exergy and energy losses in the hydrochlorination of the methanol process for a methyl chloride production unit. Machine learning-based predictive maintenance models are identified to assess plant toxicity. The proposed novel framework integrates Hyprotech Systems (HYSYS) and machine learning models. It optimizes the operating conditions of the chloromethane plant. Both supervised and unsupervised machine learning models such as Bayesian Ridge regression (BRR), Nearest Neighbors regression (KNR), and Stochastic gradient descent regression (SGD) are used to forecast energy and exergy destruc¬tion. Among these models, the BRR exhibits exceptional accuracy in predicting thermodynamic losses, notably achieving an R2 value of 0.998. Thereby, an integrated framework could serve as a valuable diagnostic tool for assessing real-time plant data to enhance plant opera¬tional efficiency.

Artificial intelligence has become ubiquitous. Machine learning, a branch of artificial intelligence, leads the current technological revolution through its remarkable ability to learn and perform on data sets of varying types. Machine learning applications are expected to change contemporary medicine as they are brought into mainstream clinical practice. In the field of cardiac arrhythmia and electrophysiology, machine learning applications have enjoyed rapid growth and popularity. To facilitate clinical acceptance of these methodologies, it is important to promote general knowledge of machine learning in the wider community and continue to highlight the areas of successful application. The authors present a primer to provide an overview of common supervised (least squares, support vector machine, neural networks and random forest) and unsupervised (k-means and principal component analysis) machine learning models. The authors also provide explanations as to how and why the specific machine learning models have been used in arrhythmia and electrophysiology studies.