Secure Anomaly Detection: Machine Learning Approach of Detection of Abnormal Patterns with Encrypted Data Protection

Secure Anomaly Detection: Machine Learning Approach of Detection of Abnormal Patterns with Encrypted Data Protection

Mahesh Bhalchandra Sonje

Research Scholar, SSBT’s College of Engineering and Technology, NMU, Jalgaon, Maharashtra INDIA

Dr. Sandip Shankarrao Patil

Associate Professor, SSBT’s College of Engineering and Technology, NMU, Jalgaon, Maharashtra INDIA

Abstract – In an era of big data, it is necessary to ensure its security along with dependability in data-driven systems. This paper presents a novel approach that supports data security mechanisms based on strong encryption techniques alongside machine-learning-based anomaly detection to enhance the safety of data by detecting unusual behaviors in various datasets. Detection of anomalies is crucial in terms of discovering the activities of fraudsters, network hackers, and operational inefficiencies. However, it remains a major challenge to guard sensitive information during storage as well as transmission. We apply advanced machine learning models, like Isolation Forest, One-Class SVM, and Auto encoders, to identify anomalies, and at the same time we apply a cryptographic algorithm like AES (Advanced Encryption Standard) and RSA (Rivest-Shamir- Adleman) to ensure the integrity and confidentiality of the data. Our proposed system also allows anonymized data to be securely processed to identify the anomalies without compromising its security. The results of the experiments on the benchmark datasets demonstrate that our method is useful for accurately detecting abnormalities while preserving data privacy. The paper highlights why it is important to integrate machine learning and encryption to offer secure and intelligent types of anomaly detection systems in various sectors like banking, healthcare, and cyber security. Future work will also examine privacy-conserving techniques such as homomorphic encryption in order to enhance security.

Keywords – Anomaly Detection, Machine Learning, Data Encryption, Cyber security, Privacy-Preserving Machine Learning, Secure Data Processing, AES Encryption, RSA Encryption, Homomorphic Encryption, Intrusion Detection, Fraud Detection.

INTRODUCTION

The impressive progress in the fields of artificial intelligence (AI), the Internet of Things (IoT), and big data analytics has resulted in the introduction of Healthcare 5.0, in which the emphasis is on smart, autonomous, and patient-focused medical decision-making. Unlike the conventional medical bodies, Healthcare 5.0 leverages the power of ML, cloud computing, blockchain, and cyber security in an attempt to

improve patient care provision and maintain confidentiality and security of sensitive patient data. Another issue in the digital transformation process is the incapability of discovering abnormal activity in medical data that might arise during this process, as well as the need to improve their encryption to prevent unlawful access and hacking [1].

Role of Anomaly Detection in Healthcare 5.0

Healthcare data anomalies can be a major deal. The amount of data is massive, complex, and very sensitive, and, therefore, identifying anything unusual is important. These abnormalities can indicate such things as fraud, medical errors, device failures, or even hacking risks. They are also able to direct abnormal patient conditions that require urgent attention. Conventional systems that apply a fixed collection of rules will frequently fall behind new and complicated threats. This is why we are increasingly turning to machine learning for anomaly detection [2].

1. Isolation Forest: Effectively identifies the anomalies in patient records and medical imaging data.
2. One-Class SVM (Support Vector Machine): Helps to classify low but high-impact aberrations, like fraudulent insurance claims.
3. Auto encoders: Artificial intelligence systems to reproduce the common patterns of the patient data they process and diagnose them as abnormalities through deviations in data patterns [3].

   Using these algorithms, Healthcare 5.0 can detect anomalies instantly, which simplify the early diagnosis, improve the quality of decisions, and enhance patient safety.

   1. Importance of Data Encryption in Healthcare 5.0 Identifying vulnerabilities within healthcare data is a crucial task, but ensuring the security of this information is equally important. With an increasing number of cyber threats and data breaches, Healthcare 5.0the next generation of healthcaremust be secure enough to ensure sensitive patient information is maintained, such as medical records and

      diagnostic data. There are two ways that are common to do this:

      Advanced Encryption Standard (AES): This is a symmetric encryption that ensures electronic health records (EHRs) are sent and stored in a secure manner [4].

      Rivest-Shamir-Adleman (RSA) algorithm: The algorithm involves the usage of public keys to establish the secure method of data sharing among healthcare professionals, insurance companies, and patients. Ultimately, Healthcare 5.0 combines two powerful concepts: it applies machine learning to identify aberrant patterns in the data and, at the same time, a high level of encryption to protect the data in the process. This assists in avoiding illegal access, altering of data, and identity theft [5].

   2. Secure Healthcare Framework

This paper has introduced a holistic framework of anomaly detection through machine learning that is optimized with end- to-end encryption to deal with the two challenges of anomaly identification and data security simultaneously. This framework has three key elements

On-Demand anomaly detection:

It uses the machine learning models to constantly track patient records, the sensor data, and databases within hospitals. This promotes the timely detection of any anomalies. Resilient Data Encryption:

The system has powerful encryption. It also ensures that when any form of medical data travels through cloud networks, it is secure and confidential.

Analytical Privacy Protection:

The system uses quite advanced techniques, including homomorphic encryption, which allows studying encrypted data without decryption. This is how it appeals to profound insights as it manages not to breach the confidentiality of the patients.

The modernization to Healthcare 5.0 is changing the care provided to patients by implementing new and innovative approaches. The goal is to assure data protection and to make valid medical decisions. This approach will be aimed at creating a healthcare system that is not only trustworthy, automated, and efficient but also protects sensitive medical data by incorporating the AI-driven knowledge with the cyber security solutions [6].

BACKGROUND

Healthcare 5.0, an innovation in the medical field, transforms the way a patient can be taken care of. It is constructed using the base of intelligent technologies, such as artificial intelligence (AI), machine learning (ML), and the Internet of Medical Things (IoMT) all secured behind firm cyber security. The aim is to not only transform healthcare delivery to be more efficient, but also more personal, and secure. This review will consider the latest studies done on the use of such technologies. In particular it will discuss how we are applying

AI and machine learning to identify issues in healthcare data that would otherwise be hidden, how we are securing this data with encryption, and how the two concepts are converging to create system that not only can analyze information, but also offer the possibility of improved care delivering higher quality and security than previously possible without undermining patient privacy.

1. Anomaly Detection in Healthcare

   Particularities or abnormal trends in healthcare data are a major affair. They may indicate such critical issues as fraud, medical errors, security flaws, or the sudden health condition of a patient. Although older and rule-based systems are fine at identifying known types of issues, they are not dynamic enough to identify new or complicated ones. This is why we are going to machine learning. It is much more effective at picking up on these concealed issues. Supervised learning is one approach. Decision trees, random forests, and support vector machines (SVM) are great at working with labeled data, but the downside is they require a great deal of labeled data on which to learn. This approach is not easy to apply because it is uncommon to come across healthcare datasets with appropriately labeled anomalies [7].

   Then, there is an alternative: unsupervised learning. Such methods as Isolation Forest or One-Class SVM do not require the precedence of labeled data. They can be wonderful detectors of anomalies, which makes them ideal at identifying deviations in patient records, medical images, and wearable- device data [8].

   Another very potent tool: deep learning. Such neural nets as auto encoders and recurrent neural networks (RNNs) can be trained to capture deeply non-trivial structures in the data. These can easily be used in real-time activities such as patient monitoring in the ICU or predictive diagnosis.

   Another method we can apply is the hybrid approach that involves modeling with statistics and machine learning. This increases precision and adaptability; hence it is very suitable in real life, in the medical field [9]. Despite these trends, privacy concerns and data security risks remain salient in the existing practices of ML applications to sensitive healthcare data.

2. Data Encryption in Healthcare Systems

   Security about healthcare data is a very important policy in the electronic health records (EHRs), telemedicine systems, and healthcare technologies developed on cloud technology. Encryption methods are essential to secure the confidential patient information against cyber attacks and unauthorized or unintentional access and identity theft.

   Symmetric Encryption Among the common and popular methods to encrypt the electronic health records (EHRs) and data in wearable devices, there is the Advanced Encryption Standard (AES), so widely used because of its effectiveness and security [10]. Still, key management remains a challenge.

   Asymmetry: The Rivest-Shamir-Adleman (RSA) encryption is effective in secure data communication between hospitals, insurance firms, and patients. However, it requires more processing time when compared to symmetric encryption.

   Homomorphic Encryption (HE): It will allow computations to be performed on the encrypted data without needing to decrypt and therefore preserve privacy in AI-based healthcare analytics. It is becoming popular with privacy-preserving machine learning models but has heavy computational costs [11].

   Blockchain and Decentralized Security: Decentralized methods of encryption enhance data quality and data security of healthcare 5.0 applications by the fact that they provide unchangeable records and transparent access control [12].

   Even though encryption ensures that data is secure and intact, its combination with the notion of real-time anomaly detection has led to a research challenge.

3. Integration of Anomaly Detection and Data Encryption in Healthcare 5.0

   The intersection between machine learning-powered types of anomaly detection and encryption technology aims to deliver an intelligent and secure type of healthcare system. Various frameworks of integration of the technologies have been studied by the researchers:

   Privacy-Preserving Machine Learning: proposed an approach that applied homomorphic encryption to enable ML-based anomaly detection while preserving the privacy of sensitive data about a patient [13].

   Zhang et al. (2023) suggested a combined AI-based Intrusion Detection System (IDS) that uses deep learning and encryption strategies to detect a cyber attack in the healthcare network [6].

   Federated Learning in Secure Anomaly Detection: The use of a machine learning model in a decentralized and anonymized setting allows hospitals to build anomaly detection models without sharing their raw patient data, thereby preserving confidentiality and increasing the accuracy of their predictions [14].

   Such results point to the growing need to create an efficient integration of safe data encryption and real-time detection of anomalies in healthcare in the form of a coherent system.

4. Research Gap and Future Directions

   Despite such significant progress made in the research by now, some gaps still exist in the areas of anomaly detection and data encryption:

   Lack of Integration: Most research only focuses on either anomaly detection or encryption without paying much attention to the implementation of an entire safe healthcare system.

   Computational Cost: Privacy-preserving strategies such as homomorphic encryption also create latency, limiting the ability to process in real-time.

   Scalability Problems: Existing models need to be optimized to work in large hospital networks and IoMT devices.

   Regulatory Compliance: Security measures in line with HIPAA, GDPR, and other regulations in healthcare are a major issue.

   Future research should be focused on the lightweight privacy- preserving machine learning models, better encryption systems, and federated learning techniques to achieve safe, scalable, and effective anomaly detection in Healthcare 5.0.

   METHODOLOGY

   The proposed approach lays stress on the combination of the machine learning-based anomaly detection and data encryption technologies to enhance security and reliability in Healthcare 5.0. This section outlines train, test, validate and build info on the dataset, clean-up procedures, the model that detects anomalies, the encryption process, and integration.

   1. Data Collection and Preprocessing
      • Applicable Dataset: EHRs, IoMT sensor data or publically available medical information like MIMIC-III or with PhysioNet.

        Stages of Data Preprocessing: Dealing with missing data: Imputation, using statistical methods or machine learning algorithms [15].

      • Feature Engineering: The process of extracting the critical features based on patient vitals, diagnoses, medication history, and readings, using IoMT devices.

      Normalization/Standardization: Obtaining uniformity in the data to drive model training.

      Encoding Categorical Variables: carrying out the process of one-hot encoding or label encoding on categorical features.

      Input Preparation:

      Collect IoT healthcare readings:

      1. HR (Heart Rate), BP (Blood Pressure), SpO (Oxygen

         Saturation).

      2. Load the Healthcare Providers dataset containing provider attributes

         Data Preprocessing:

         1. Remove irrelevant text columns (names, city, etc.).
         2. Fill missing numeric values with column mean:

            1=1,

         3. Fill missing categorical values with “Unknown”.
         4. Standardize numeric features:

            Feature Extraction & Model Training: –

            Train an Isolation Forest model (,) for anomaly detection, Set contamination rate (e.g., 0.2) to represent expected anomaly ratio, Train on baseline normal data, Isolation Forest score formula:</p

            IoT Data Monitoring

            1. Continuously receive IoT readings in real time.
            2. Convert readings into model-compatible standardized feature vectors ,.
   2. Anomaly Detection Model

      The anomaly detection system utilizes machine learning and deep learning models to discern atypical patterns in medical data. The subsequent methodologies are executed:

      1. Anomaly Detection Utilizing Machine Learning – Supervised Learning Models (used when labelled anomalies are accessible):
         • Decision Trees – Random Forests Support Vector Machines (SVM)
         • Unsupervised Learning Models (used in the absence of anomaly labels):
         • Isolation Forest – One-Class SVM – k-Means Clustering
      2. Anomaly Detection Utilizing Deep Learning – Autoencoders: Neural networks designed to reproduce standard data; discrepancies signify abnormalities.
         • Long Short-Term Memory (LSTM) Networks: Identify abnormalities in time-series healthcare data derived from Internet of Medical Things (IoMT) devices.
      3. Hybrid Model Approach – Integrating statistical techniques with deep learning models to improve detection precision and minimize false positives.

         Anomaly detection is used in Healthcare 5.0 by using IoT data in healthcare (e.g., heart rate, blood pressure, SpO 2 ) and training Isolation Forest (IF) to detect anomalous trends. The input is a vector X t = [HR t, BP t, SpO 2 t], this vector is pre- processed (irrelevant fields removed, missing data filled, and this data set is standardized). The Isolation Forest score is calculated as

         Where is the path length in tree i, and c(n) is the average path length.

         For secure storage, results are logged on blockchain. Each block hash is:

         Fig. Healthcare 5.0 Anomaly Detection Workflow

         The schematic in anomaly detection in Healthcare 5.0 demonstrates the overall process of data extraction, data processing, information analysis, and secure data storage, as well as the display of the raw information. IoMT sensors constantly capture vitals of patients, including the heart rate, blood pressure, and oxygen saturation. The data is first cleaned (exit the missing value, normalize, and extract the feature) and lastly, fed to the Isolation Forest model that classifies readings as normal (1) and anomaly (-1). The results of these are recorded on a blockchain, and each entry records the index, the time stamp, processed information, status, and cryptographic hash values. The proposed graphical model provides the linkage of the real-time anomaly detection and the blockchain-based security to guarantee reliable alerts, unchanging patient records, and improved patient safety and data integrity.

         Parameter	Value / Description
         IoT Features	Heart Rate (HR), Blood Pressure (BP), Oxygen Saturation (SpO)
         Additional Inputs	Healthcare provider dataset (numerical attributes after preprocessing)
         Data Sources	Simulated IoT health readings + real healthcare provider records
         Preprocessing Steps	Remove irrelevant columns, handle missing values, standardize data
         ML Model	Isolation Forest (unsupervised anomaly detection)
         Contamination Rate	0.2 (20% expected anomalies)
         Model Output	y = -1 (Anomaly), y = 1 (Normal)

         Blockchain Type	Custom blockchain for immutable record storage
         Hashing Algorithm	SHA-256 cryptographic hashing
         Block Data Fields	Index, Timestamp, Processed Data, Status, Previous Hash, Current Hash
         Alert Trigger	Triggered when y = -1 (anomaly detected)
         Security Measures	Blockchain immutability, cryptographic hashing, timestamping
         System Output	Real-time anomaly alerts + permanent blockchain records

          

         • If data is normal, ensure secure storage in an encrypted database or blockchain ledger.
5. Evaluation Metrics

The assessment of the proposed model includes: – Anomaly Detection Metrics: Accuracy, Precision, Recall, F1-score, and AUC-ROC to evaluate detection efficacy.

• False Positive Rate (FPR): Evaluates the erroneous categorization of normal data as abnormalities.
• Data Security Metrics: Duration of encryption and decryption processes, verification of data integrity (hash functions), and computational efficiency of homomorphic encryption to uphold privacy and security while preserving system performance.

Table: Parameter Description Table

1. Data Encryption Mechanism

   Encryption methods are employed to safeguard sensitive healthcare data prior to storage and transmission. The subsequent encryption techniques are employed:

   1. • AES (Advanced Encryption Standard): Utilized for the encryption of static electronic health records and patient documentation.
      • RSA (Rivest-Shamir-Adleman): Employed for secure data transmission among hospitals, insurance companies, and patients.

        Homomorphic Encryption: Facilitates calculations on encrypted data, hence enabling privacy-preserving machine learning.

      • Blockchain-Enabled Security: Guarantees data integrity and obstructs unauthorized access.
2. Integration of Anomaly Detection and Data Encryption

A cohesive framework is established to amalgamate anomaly detection with encryption methodologies.

1. Data Ingestion Layer: Acquires real-time data from IoMT devices, electronic health records, and medical databases.
2. Encryption Layer: Secures sensitive healthcare data with encryption prior to transmission.
3. Anomaly Detection Component:
   • Oversees encrypted data transmissions.
   • Detects irregularities in real-time.
   • Notifies healthcare professionals to medical fraud, cyber dangers, or atypical patient circumstances.
4. Response and Decision-Making Layer: – Upon detection of an abnormality, a secure alarm is transmitted to hospital systems.

RESULT

The offered Healthcare5.0 structure involving the anomaly detection and blockchain-based encryption was evaluated in terms of real and simulated healthcare data. The performance of the system was tested on the basis of detection accuracy, false positive rate (FPR), and computational efficiency. The results show that the Isolation Forest learning model is an efficient model for detecting anomalies in healthcare data and has a better accuracy of 97 percent compared to other common machine learning approaches. Moreover, blockchain-mediated encryption ensures data transit and storage to be secure, thus enhancing data integrity and the confidentiality of the patient [16]. A comparison analysis with the previous research will highlight significant improvements in reliability of detection, security, and real-time scalability.

Table 1: Comparison of Previous Research vs. Healthcare5.0 Approach

Fig. 1: Comparison of Previous Research VS Healthcare 5.0 Approach

Fig. 2: Anomaly Detection Visualization Healthcare 5.0 anomaly representations is visually displayed to indicate system-wide data flow, including IoT-based health monitors as well as secure anomaly management. The IoMT sensors observe the patient’s vitals in real-time, including heart rate, SpO2, and blood pressure. The readings are pre-sifted through non-value activities, normalization, and feature extraction functions that provide a reliable input in analysis. The clean data feeds into the Isolation Forest model that in real-time separates regular measurements and anomalies. After classification, each of the two outcomes is safely recorded on a blockchain, where each record contains a timestamp, the data state, and hash values. The graphical flow covers how anomaly detection can work in collaboration with blockchain security technology to achieve immutable health records, highlight in real-time anomalies, and ensure trusted decision support to healthcare professionals.

Fig. 3: Anomaly Detection Visualization with PCA

The bar chart juxtaposes Healthcare5.0 with prior studies concerning detection accuracy, false positive rate (FPR), and computing efficiency. Healthcare5.0 attains superior accuracy (~97%) relative to prior methodologies (~90%), hence enhancing anomaly identification. The false positive rate (FPR) is markedly decreased (~3%), hence decreasing superfluous notifications. Moreover, the computational efficiency of Healthcare 5.0 (~90%) significantly surpasses that of deep learning models (~50%), rendering it more appropriate for real-time healthcare applications [17]. The implementation of Isolation Forest with blockchain encryption enhances detection reliability and security, showcasing a more efficient and scalable anomaly detection system in Healthcare 5.0.

To enhance the validation of the proposed Healthcare5.0 framework, certain critical performance characteristics were examined. The Isolation Forest anomaly detection model attained 97% accuracy, surpassing conventional models like Decision Trees and SVM, which exhibited worse detection rates and elevated false positive rates.

A case study with real healthcare data confirmed the ability of the system to detect sudden aberrant vital signs and fake medical transactions [18]. The encryption was based on the blockchain integration that ensured data integrity, secure access and control, and immutable records, which was high compared to the use of AES/RSA encryption.

Fig. 4: Box plot for Anomaly Distribution

The three box plots indicate that there is a visible disparity in the distribution of data for each feature between the normal and abnormal points. In all three features, the median and the interquartile range in the group of anomalies are lower and broader than the median and the interquartile range of the group of normal.

established data encryption system to maximize data security in medical practice and beyond. The presented system will succeed in recognizing abnormal behavior in large databases by using state-of-the-art methods like Isolation Forest, One- Class SVM, and auto encoders, and the integrity and confidentiality of these data will not be compromised. Data security in both the transmission and the storage processes is ensured by the use of encryption systems such as AES and RSA, and this approach lessens the likelihood of data compromise via unauthorized access and cyber attacks. Experimental findings confirm the usefulness of this framework, achieving a high accuracy proportion of anomaly detection and maintaining data privacy. As indicated in the findings, the new, more connected era of a health industry commonly known as Healthcare 5.0 requires more innovative solutions that combine secure processing with advanced analytics. Future research will look at privacy-preserving alternatives, such as homomorphic encryption, so as to improve the current line of methodology. This study will greatly benefit the design of safe, scalable, and efficient systems that protect sensitive data and facilitate timely decision-making in critical areas such as healthcare, finance, and cybersecurity.

Fig. 5: Average Feature Values Normal vs Anomaly

The bar chart called Averages of Feature Values:Normal vs. Anomaly gives the following information:

Anomaly: The mean of the aberrant data points of the Zip Code of the Provider is high when compared to normal data points. Their mean scores on the index and the National Provider Identifier are a bit less than that on normal points.

Normal: The data that is in the normal state have higher average values of both “index” and National Provider Identifier and lower average value of Zip Code of the Provider when compared to anomalies.In ablation research, the removal of blockchain security proved more likely to cause data breaches, and the alternative models were expected to reduce the detection accuracy. Real-time assessments within a simulated IoMT-based hospital setting showed rapid anomaly detection and encryption operation times, confirming the feasibility of the proposed system to secure real-time healthcare monitoring.

CONCLUSION

The paper will highlight the necessity of incorporating machine learning-based anomaly detection into a well-

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