Cyber Threat Detection Framework for Mobile Service Applications: A Case Study of Salon-on-Demand Platforms

Cyber Threat Detection Framework for Mobile Service Applications: A Case Study of Salon-on-Demand Platforms

Shrivatsa Kulkarni

Jain University Bengaluru, India

Shridevi R T

Jain University Bengaluru, India

Tharun Adhith

Jain University Bengaluru, India

Mithun R

Jain University Bengaluru, India

Mohammad Shoaib

Jain University Bengaluru, India

Dr Prabhakaran Mathialagan

Jain University Bengaluru, India

ABSTRACT – Salon-on-demand platforms, increasingly reliant on digital platforms for bookings, payments, and customer management, face significant cybersecurity threats such as phishing, ransomware, and data breaches. These vulnerabilities stem from weak authentication systems, unsecured applications, and insufficient cybersecurity awareness. This study aims to address these challenges by developing a robust cyber threat detection and security framework tailored for Salon-on-demand platforms. The research identifies common threats, evaluates existing security measures, and proposes best practices for enhancing cybersecurity awareness and compliance. A neural network-based model is employed to detect threats, achieving high accuracy (training: 0.97, testing: 0.94) and demonstrating effectiveness in identifying threats like DDoS attacks, phishing attempts, and unauthorized access. The study recommends integrating AI-driven threat detection, blockchain-based payment systems, and continuous employee training to mitigate risks. By enhancing data protection and securing digital transactions, this framework ensures business continuity and customer trust in Salon-on-demand platforms. The framework was evaluated using a convolutional neural network (CNN) model trained on structured cybersecurity data collected from simulated mobile salon systems.

Keywords: Cybersecurity, Salon-on-demand platforms, threat detection, AI-driven security, data protection, blockchain, phishing, ransomware, neural networks, digital transactions.

1. INTRODUCTION

   In the rapidly evolving digital landscape, Salon-on-demand platforms are increasingly adopting online booking systems, digital payment platforms, and customer management applications, making them vulnerable to cyber threats. As these businesses handle sensitive customer data, including

   personal and financial information, they become prime targets for cybercriminals employing phishing, malware, and payment fraud tactics. This research explores the growing cybersecurity challenges faced by Salon-on-demand platforms and proposes a robust threat detection and security framework to mitigate risks. By integrating advanced cybersecurity measures, such as intrusion detection systems, encryption techniques, and secure authentication protocols, this study aims to enhance data protection and ensure safe digital operations for mobile salon businesses.

   Theoretical Background

   The rapid integration of digital technologies into Salon-on- demand platforms has transformed the industry, enabling greater convenience, efficiency, and customer engagement. However, this digital shift has also exposed mobile salon businesses to a wide array of cybersecurity threats, necessitating the development of effective detection and security frameworks. The theoretical foundation for this study is based on established cybersecurity models, threat detection methodologies, and information security theories that guide the protection of digital assets in small-scale and mobile enterprises. One of the core theories applicable to this research is the CIA Triad (Confidentiality, Integrity, and Availability), which serves as the cornerstone of information security. In the context of Salon-on-demand platforms, confidentiality ensures that sensitive customer and business data remain protected from unauthorized access, integrity maintains the accuracy and trustworthiness of digital records, and availability ensures that online booking systems and payment gateways remain functional and resilient against

   cyber threats. The CIA Triad framework helps in structuring security protocols that prevent unauthorized data breaches, financial fraud, and operational disruptions in mobile salon services. Another relevant theoretical perspective is the Zero Trust Architecture (ZTA), which operates on the principle of never trust, always verify. This model is critical for Salon-on-demand platforms as they rely heavily on digital transactions and cloud-based storage systems. Zero Trust emphasizes strict access controls, multi-factor authentication (MFA), continuous monitoring, and micro-segmentation to prevent unauthorized access and limit the damage caused by cyber intrusions. Implementing Zero Trust principles within mobile salon businesses can significantly enhance their cybersecurity posture by reducing the risk of credential-based attacks and insider threats.

   The Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) Model also provides a theoretical basis for cyber threat detection in Salon-on-demand platforms. IDS and IPS work by monitoring network traffic, identifying anomalies, and blocking potential threats before they compromise sensitive data. Mobile salon businesses, often operating with limited IT security expertise, can benefit from automated IDS solutions that use machine learning algorithms to detect suspicious activities, such as unauthorized logins or unusual transaction patterns. This aligns with contemporary research on artificial intelligence- driven threat detection in cybersecurity. Furthermore, the Risk Management Framework (RMF) provides a structured approach for identifying, assessing, and mitigating cybersecurity risks in Salon-on-demand platforms. RMF emphasizes continuous risk assessment and adaptation, ensuring that Salon-on-demand platforms can proactively respond to evolving cyber threats. This framework is particularly useful in designing security strategies that address specific vulnerabilities, such as unsecured payment processing systems and weak authentication mechanisms. The Technology Acceptance Model (TAM) is also relevant in understanding how mobile salon owners and employees perceive and adopt cybersecurity measures. Since many small business owners lack technical expertise, TAM helps in analyzing the factors influencing their willingness to implement cybersecurity frameworks. Factors such as perceived usefulness, ease of use, and awareness campaigns play a crucial role in determining the successful adoption of security best practices.

   Trends, Issues, and Challenges

   The increasing adoption of digital platforms in Salon-on- demand platforms has brought both opportunities and challenges in cybersecurity. As Salon-on-demand platforms rely on online booking systems, digital payments, and

   customer management software, they become potential targets for cybercriminals. The trends in cybersecurity threats against Salon-on-demand platforms highlight the evolving nature of digital risks, while the associated issues and challenges emphasize the need for proactive security strategies. A major trend in cybersecurity threats is the rise of phishing attacks targeting mobile salon businesses. Cybercriminals use deceptive emails and messages to trick employees and owners into revealing sensitive credentials, leading to unauthorized access to financial and customer data. Additionally, ransomware attacks have become a growing concern, where malicious software locks business systems until a ransom is paid. This type of attack can disrupt operations, cause financial losses, and damage brand reputation. Another significant trend is the exploitation of unsecured mobile applicatios and websites. Many Salon- on-demand platforms use third-party booking apps that may lack proper encryption or security protocols, making them vulnerable to data breaches. Similarly, weak authentication mechanisms increase the risk of unauthorized access to customer databases and financial transactions. This challenge is further exacerbated by insufficient cybersecurity awareness among salon owners and employees, leading to poor password management and susceptibility to scams.

   A pressing issue is the lack of regulatory compliance and legal protection in mobile salon cybersecurity. Many businesses fail to adhere to data protection regulations such as the General Data Protection Regulation (GDPR) or the Payment Card Industry Data Security Standard (PCI DSS), leaving them exposed to legal penalties and financial risks. The absence of standardized security policies in the mobile salon industry further complicates efforts to implement comprehensive cybersecurity frameworks. Moreover, the increasing use of Internet of Things (IoT) devices in Salon- on-demand platforms presents a new set of security risks. Smart payment terminals, scheduling devices, and connected beauty tools can be exploited if not properly secured. Cybercriminals can infiltrate these devices to steal data or disrupt business operations, emphasizing the need for stringent IoT security measures. One of the biggest challenges Salon-on-demand platforms face is balancing cybersecurity with usability and cost-efficiency. Many small businesses operate with limited budgets and technical expertise, making it difficult to invest in advanced cybersecurity measures. Implementing security solutions such as multi-factor authentication, encryption, and intrusion detection systems requires financial and operational adjustments that may not always be feasible for small-scale salon owners. To address these challenges, Salon-on-demand platforms must adopt a multi-layered security approach that includes employee training, secure payment processing,

   encrypted communication channels, and regular system updates. Strengthening cybersecurity awareness, integrating AI-powered threat detection, and adhering to regulatory frameworks can significantly mitigate cyber risks.

   Problem Statement

   The increasing reliance on digital platforms in Salon-on- demand platforms has led to significant cybersecurity vulnerabilities, exposing sensitive customer and financial data to cyber threats such as phishing attacks, ransomware, and data breaches. Many mobile salon businesses operate without robust security frameworks, making them easy targets for hackers who exploit weak authentication systems, unsecured mobile applications, and unencrypted payment gateways. Additionally, the lack of awareness and compliance with cybersecurity best practices further exacerbates these risks. Given the growing cyber threats in this industry, there is an urgent need for an effective cyber threat detection and security framework tailored to Salon-on- demand platforms. This study aims to address these challenges by developing a structured approach to enhancing cybersecurity in mobile salon businesses, ensuring data protection, business continuity, and customer trust.

   Objectives

   1. To identify common cybersecurity threats affecting Salon-on-demand platforms.
   2. To develop a cyber threat detection framework for mobile salon businesses.
   3. To analyze the effectiveness of existing security measures in Salon-on-demand platforms.
   4. To propose best practices for improving cybersecurity awareness and compliance.
   5. To enhance data protection and secure digital transactions in Salon-on-demand platforms.

2. LITERATURE REVIEW

   1. Identifying Common Cybersecurity Threats Affecting Salon-on-demand platformsSalon-on-demand platforms face a range of cybersecurity threats that jeopardize their operations and customer data. Data breaches and privacy violations are among the most significant concerns, as these businesses collect and store sensitive customer information, including names, phone numbers, payment details, and appointment records. Cybercriminals often target such data for identity theft and financial fraud, particularly in small businesses like Salon- on-demand platforms, which frequently lack robust security protocols (Elahi et al., 2021) [2]. Phishing attacks and social

      engineering are also prevalent, with hackers using deceptive emails, text messages, or fraudulent websites to trick employees into revealing login credentials or payment information (Mehta et al., 2021) [7]. Ransomware and malware attacks further exacerbate the risks, as small businesses often lack the defenses to prevent data encryption or operational disruptions caused by malicious software (Zhang, 2013) [15]. Additionally, insecure mobile applications and payment systems, coupled with reliance on public Wi-Fi networks, expose Salon-on-demand platforms to financial fraud and data interception (Kant, 2024; Miller, 2024) [5], [8].

   2. Developing a Cyber Threat Detection Framework for Mobile Salon BusinessesTo address these threats, recent advancements in AI and machine learning have been leveraged to improve cyber threat detection. (Abraham et al.,2024) propose a multi-factor authentication framework that integrates EEG-based biometrics with AI-driven anomaly detection, enhancing security in mobile systems. Blockchain technology has also been explored as a solution for secure transactions, with decentralized frameworks offering transparency and fraud prevention (Muraja, 2024) [6]. Real-time monitoring and risk assessment are critical components of an effective cyber threat detection framework. (Phang et al.,2024) [13] advocate for risk-based authentication, which dynamically adapts security protocols to varying threat levels, ensuring proactive threat mitigation.
   3. Analyzing the Effectiveness of Existing Security Measures in Salon-on-demand platformsSalon-on-demand platforms commonly employ several cybersecurity measures, including encryption, multi-factor authentication (MFA), and cybersecurity training (Olaiya et al., 2024) [11]. While these measures provide strong protection against unauthorized access and phishing attempts, their effectiveness is often undermined by human error and limited adoption of advanced technologies like AI-driven threat detection (Thandayuthapani & Bhuvanesh, 2024). Public Wi-Fi usage remains a significant vulnerability, as unsecured networks increase exposure to cyber threats (Alhanatleh et al., 2024) [7]. Despite these limitations, encryption and MFA have proven effective in reducing unauthorized access incidents, and regular cybersecurity training has been shown to improve employee awareness (Mushinzimana & Faisal, 2025).
   4. Proposing Best Practices for Improving Cybersecurity Awareness and ComplianceCybersecurity awareness and compliance are essential for mitigating risks and ensuring adherence to industry regulations. Structured training programs, such as simulated phishing exercises, have been shown to reduce security breaches by enhancing employee vigilance (Foster et al., 2024) [9]. Clear cybersecurity policies and regular updates to these policies are critical for addressing emerging threats (Mandru, 2025) [10]. Compliance with regulations like GDPR and PCI DSS not only reduces financial and reputational risks but also strengthens overall security posture (Alwahaibi et al., 2024) [11]. Technological solutions, such as AI-driven threat detection and blockchain-based security frameworks, further enhance compliance and data protection (Rahman, 2024; Ajayi et al., 2025) [2], [14].
   5. Enhancing Data Protection and Securing Digital Transactions in Salon-on-demand platforms

   Data protection in Salon-on-demand platforms relies heavily on encryption, secure cloud storage, and compliance with data privacy laws (Mahida, 2024) [1]. End-to-end encryption ensures the confidentiality of customer data during transactions, while secure cloud storage prevents unauthorized access to business records (Omowole et al., 2024) [11]. Digital payment systems, though convenient, are vulnerable to phishing attacks and card-not-present fraud. Implementing blockchain-based payment solutions and tokenization can enhance transaction security and transparency (Naim & Hasan, 2025; Wong et al., 2024) [6], [11]. However, human error remains a significant vulnerability, underscoring the need for continuous cybersecurity training and the adoption of advanced technologies like AI-driven fraud detection (Sari & Khairiyah, 2024; Kumar et al., 2024) [7], [2].

3. EXPERIMENTAL DESIGN

   The image is a process flow diagram representing a Cyber Threat Detection and Security Framework for Salon-on- demand platforms. It starts with Mobile Salon Devices, progressing through Data Collection, Threat Identification, Risk Assessment, Security Implementation, Continuous Monitoring, Incident Response, and Threat Mitigation, finally leading to Security Updates & Improvements. Supporting elements like Threat Database, Security Policies, Encryption & Authentication, and AI & ML-based Detection enhance security. A red dashed feedback loop ensures continuous improvement. Rectangular nodes represent processes, while oval nodes highlight security components. The diagram showcases a cybersecurity lifecycle for securing mobile salon businesses.

   Research Methodology

   This research follows a descriptive research design, aiming to systematically investigate and document cyber threats faced by Salon-on-demand platforms. The study employs structured data collection, neural network-based analysis, and performance evaluation using key metrics such as F1-score, recall, precision, and accuracy.

   Data Collection

   The study will gather data from various mobile salon devices to analyze cybersecurity risks. Key data sources include Point of Sale (POS) systems for digital transactions, IoT-enabled salon equipment such as smart mirrors and automated shampoo dispensers connected to cloud services, customer management software for scheduling and data handling, mobile network logs for internet and communication tracking, and surveillance cameras and biometric devices for security authentication. The dataset was divided into 70% training data and 30% testing data for model training and evaluation. Essential data attributes include timestamps, device IDs, IP addresses, transaction logs, network traffic data, user authentication logs, and anomaly detection flags to support cyber threat identification and prevention.

   Data Preprocessing

   The collected data undergoes preprocessing to enhance accuracy and reliability by removing noise, duplicate entries, and irrelevant logs. Data cleaning is performed to eliminate missing values and inconsistent records, ensuring a high- quality dataset. Data normalization standardizes key attributes such as IP addresses, timestamps, and device IDs for uniformity. Feature selection focuses on retaining critical attributes like abnormal transaction patterns and unusual login activities to improve threat detection. Finally, data labeling categorizes the dataset based on predefined cyber threat types, including malware, unauthorized access, and DDoS attacks, facilitating efficient cybersecurity analysis.

   Neural Network-Based Analysis

   A deep learning model, specifically a Convolutional Neural Network (CNN), was employed for cyber threat detection. The model consisted of an input layer that processed structured cybersecurity data, hidden layers that extracted features, and an output layer that classified threats into predefined categories. Training was conducted using the 70% labeled dataset, utilizing the backpropagation algorithm to optimize weights, ReLU activation functions for efficient learning, and the Adam optimizer for adaptive adjustments.

   The trained model was then tested on the 30% testing dataset to evaluate its effectiveness, with key performance metrics including F1-score (balancing precision and recall), recall (correctly identifying threats), precision (accuracy of detections), and accuracy (overall model correctness).

4. RESULTS AND DISCUSSIONS

   Table 1: Accuracy of the model

   Metric	Value
   Accuracy	0.94
   Training Accuracy	0.97
   Testing Accuracy	0.94

   (Source: Primary data)

   The high training accuracy (0.97) and testing accuracy (0.94) suggest that the cyber threat detection model for securitysolutions in Salon-on-demand platforms performs effectively with minimal overfitting. This indicates that the model has learned to identify potential threats accurately across different datasets. Given the increasing reliance on digital platforms for mobile salon bookings, transactions, and customer management, securing these systems from cyber threats such as data breaches, ransomware, and unauthorized access is critical. The models strong performance implies its

   Finally, the models performance was compared to traditional methods like signature-based and heuristic-based detection, with a statistical evaluation and confusion matrix analysis confirming its superior accuracy and reliability in detecting cyber threats.

   Mobile salon security system

   potential for real-world deployment in identifying and mitigating security threats in Salon-on-demand platforms, ensuring data integrity and customer privacy. However, continuous monitoring, periodic retraining, and incorporating real-time threat intelligence are necessary to maintain robustness against evolving cyber threats.

   Table 2: Threat type detection

   Threat Label	Authentication Attempts
   DDoS Attack	5
   Phishing Attempt	4
   Unauthorized Access	3
   Malware Infection	3
   No Threat	1

   (Source: Primary Data)

   The table reveals that cyber threats significantly increase authentication attempts, likely due to repeated login failures, brute force attacks, or security mechanisms blocking unauthorized access. DDoS attacks (5.0 attempts on average) have the highest authentication attempts, suggesting that attackers may be exploiting authentication services to overwhelm the system. Phishing attempts (4.0) and unauthorized access attempts (3.0) indicate frequent login trials, possibly from credential stuffing attacks. Malware infections (3.0 attempts) may stem from automated bots attempting unauthorized logins. In contrast, legitimate users under the “No Threat” category average only 1.0 authentication attempt, implying seamless and secure access. These insights emphasize the need for multi-factor authentication (MFA), anomaly detection in login patterns, and stronger rate-limiting mechanisms to prevent unauthorized access in mobile salon security solutions.

   Table 3: Classification Report

   Class	Precision	Recall	F1-Score	Support
   0	0.95	0.93	0.94	35
   1	0.92	0.95	0.94	35
   Accuracy			0.94	70
   Macro Avg	0.94	0.94	0.94	70
   Weighted Avg	0.94	0.94	0.94	70

   (Source: Primary data)

   The classification report indicates a well-performing model with an overall accuracy of 94%, demonstrating its effectiveness in distinguishing between the two classes (0 and 1). Class 0 has a slightly higher precision (0.95), meaning fewer false positives, while Class 1 has a higher recall (0.95), indicating better identification of actual positive cases. The F1-score remains balanced at 0.94 for both classes, confirming the model’s robustness. The macro and weighted averages are also 0.94, showing that performance is consistent across both classes without bias. This suggests that the model is reliable for cyber threat detection in mobile salon security solutions, making it suitable for real-time threat classification with minimal errors.

   Statistical analysis

   Table 4: Threat type detection

   Threat Type Distribution
   Threat Label	Count
   No Threat	35
   Unauthorized Access	12

   Phishing Attempt	10
   DDoS Attack	7
   Malware Infection	6

    

   Table 5: Average transaction amount by threat type

   Threat Label	Transaction Amount ()
   DDoS Attack	1265
   Malware Infection	920
   No Threat	378.5
   Phishing Attempt	0
   Unauthorized Access	0

   (Source: primary data)

   The Threat Type Distribution table highlights that the majority of transactions (35 cases) experienced no threats, indicating a relatively secure environment for most mobile salon operations. However, several security incidents were recorded, with Unauthorized Access (12 cases) and Phishing Attempts (10 cases) being the most frequent cyber threats. These attacks typically target sensitive customer and financial data, posing risks of identity theft or financial fraud. Additionally, DDoS Attacks (7 cases) and Malware Infections (6 cases), though lower in count, can still be highly disruptive and may lead to service downtime or system compromise. The presence of these threats highlights the need for strong security measures such as multi-factor authentication, real-time threat detection, and secure transaction protocols to protect both salon businesses and their customers.

   The Average Transaction Amount by Threat Type table reveals interesting insights into the financial impact of cyber threats. DDoS Attacks (1265.0) and Malware Infections (920.0) are associated with the highest transaction amounts, indicating that high-value transactions may be prime targets for these advanced cyber threats. Conversely, Phishing Attempts and Unauthorized Access have transaction amounts

   of 0.0, suggesting that these threats might be aimed more at data breaches or account takeovers rather than direct financial fraud. The No Threat category shows an average transaction amount of 378.5, implying that normal business transactions tend to be lower in value compared to those affected by cyber threats. These findings emphasize the importance of implementing AI-driven fraud detection, secure payment gateways, and cybersecurity awareness programs to mitigate financial and operational risks in mobile salon services.

   Table 6: Multi class model evaluation

   Threat Type	Precisi on	Rec all	F1- Score	Support
   No Threat	0.94	0.94	0.94	16
   Unauthorized Access	0.83	1	0.91	5
   Phishing Attempt	1	0.8	0.89	5
   Malware Infection	1	1	1	3
   DDoS Attack	1	1	1	1
   Accuracy			0.93	30
   Macro Avg	0.95	0.95	0.95	30
   Weighted Avg	0.94	0.93	0.93	30

   (Source: Primary data)

   The classification model achieves an impressive accuracy of 93.33%, indicating its strong capability in correctly identifying various threat types. The “No Threat” class has a balanced precision and recall of 0.94, showing that the model correctly classifies non-threatening instances with high reliability. Unauthorized Access has a slightly lower precision (0.83) but a perfect recall (1.00), meaning that while all actual cases of unauthorized access were detected, there

   were some false positives. Phishing Attempt has the lowest recall (0.80) among threat types, suggesting that some phishing cases were missed by the model, which could be a concern in cybersecurity applications. The Malware Infection and DDoS Attack classes both achieve perfect scores (1.00) in precision, recall, and F1-score, though their sample sizes are small (3 and 1 instances, respectively). The macro average (0.95) and weighted average (0.93) indicate that the model performs consistently across all categories, though the small sample size for some threats might slightly inflate these values. This study has certain limitations. The dataset used for training and evaluation was relatively small and partially simulated, which may limit the generalizability of the results. Additionally, certain threat categories such as DDoS attacks had fewer samples, which may influence classification performance. Future research should incorporate larger real- world datasets and explore additional machine learning models to improve detection accuracy.

5. CONCLUSIONS

   The increasing reliance on digital platforms in Salon-on- demand platforms has exposed these businesses to significant cybersecurity threats, including phishing, ransomware, and data breaches. This research aimed to address these vulnerabilities by developing a comprehensive cyber threat detection and security framework tailored for Salon-on- demand platforms. The study successfully identified common cybersecurity threats, evaluated the effectiveness of existing security measures, and proposed actionable best practices to enhance cybersecurity awareness and compliance.

   The neural network-based threat detection model demonstrated high accuracy (training: 0.97, testing: 0.94), effectively identifying threats such as DDoS attacks, phishing attempts, and unauthorized access. The model’s performance underscores its potential for real-world deployment in Salon- on-demand platforms, offering a proactive approach to mitigating cyber risks. Additionally, the analysis revealed that advanced threats like DDoS attacks and malware infections are often associated with higher transaction amounts, highlighting the financial impact of cyberattacks on small businesses. Key recommendations from this research include the adoption of AI-driven threat detection systems, blockchain-based payment solutions, and continuous employee training programs. These measures can significantly enhance data protection, secure digital transactions, and improve overall cybersecurity resilience in Salon-on-demand platforms. Furthermore, compliance with regulatory standards such as GDPR and PCI DSS is essential to mitigate legal and financial risks. In conclusion, ths study provides a structured and practical framework for addressing cybersecurity challenges in Salon-on-demand platforms. By

   integrating advanced technologies, fostering cybersecurity awareness, and adhering to best practices, mobile salon businesses can safeguard sensitive customer data, ensure operational continuity, and build trust in their digital services. Future research could explore the scalability of this framework across other small businesses and investigate the integration of IoT security measures to address emerging threats.

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