Unauthorized Activities Research Articles

Multimodal Operation Data Mining for Grid Operation Violation Risk Prediction

With the continuous expansion of the power grid, the issue of operational safety has attracted increasing attention. In power grid operation control, unauthorized operations are one of the primary causes of personal accidents. Therefore, preventing and monitoring unauthorized actions by power grid operators is of critical importance. First, multimodal violation data are integrated through information systems, such as the power grid management platform, to construct a historical case database. Next, word vectors for three types of operation-related factors are generated using natural language processing techniques, and key vectors are selected based on generalized correlation coefficients using mutual information, enabling effective dimensionality reduction. Independent component analysis is then employed for feature extraction and further dimensionality reduction, allowing for the effective characterization of operational scenarios. For each historical case, a risk score is derived from a violation risk prediction model constructed using the Random Forests (RF) algorithm. When a high-risk score is identified, the K-Nearest Neighbor (KNN) algorithm is applied to locate similar scenarios in the historical case database where violations may have occurred. Real-time violation risk assessment is performed for each operation, providing early warnings to operators, thereby reducing the likelihood of violations, and enhancing the safety of power grid operations.

Enhancing Network Threat Detection with Random Forest-Based NIDS and Permutation Feature Importance

Network Intrusion Detection Systems (NIDS) are critical for protecting computer networks from unauthorized activities. Traditional NIDS rely on rule-based signatures, which can be limiting in detecting emerging threats. This study investigates the effectiveness of the random forest classifier in advancing NIDS capabilities through machine learning. Using the CICIDS-2017 dataset, the data are preprocessed to enhance their quality by removing redundancies. feature selection and permutation importance were employed to identify the most relevant features. The methodology involves rigorous testing and analysis of the random forest classifier’s performance, focusing on f1-score rates compared to other machine learning models. Results demonstrate that by optimizing class weights, applying a custom prediction function and leveraging 26 key features, the random forest classifier achieves an outstanding 99.8% in the weighted f1-score and 93.31% in the macro f1-score in various attack types. This research highlights the potential of machine learning to significantly enhance NIDS effectiveness, offering a robust defense mechanism against evolving cybersecurity threats in modern networks.

A Descriptive Analysis of AI’s Role in Enhancing the Quality of Teaching and Learning Outcomes

This study examines the role of artificial intelligence (AI), specifically generative AI, in higher education, mainly in the teaching-learning process, personalized learning, learning outcomes, and challenges. The researcher has employed a descriptive research method to collect the data by reviewing the research articles, books, and conducting a document analysis (policies of universities on using AI). The study's findings provide multifaceted insights into the use of AI in the teaching-learning process. It reveals that optimizing the use of GenAI can support students to become more independent learners, critical thinkers, and it can also provide plenty of Drill-Practice opportunities for them to master the content, particularly, for Afghan students who are believed to be more reliant on teachers and printed learning resources. On the other hand, the findings suggest that GenAI is an invaluable asset for teachers’ professional development in terms of planning everyday pedagogical practices for their classrooms and generating materials and content. However, the usage of GenAI can also pose some significant challenges for teachers as well as institutions, particularly in the absence of an established code of conduct and implicit rules that should be abided by the students to avoid unauthorized activities. Additionally, the study also provides some useful suggestions predicated on the findings of the study.

Active Security Surveillance and Object Detection

Active security surveillance and object detection have become crucial components in modern security systems, driven by advancements in computer vision and machine learning. These systems are designed to enhance real-time monitoring and threat detection across various environments, such as public spaces, transportation hubs, and critical infrastructure. Active security surveillance systems continuously scan and analyze video feeds or sensor data to identify potential security threats, unauthorized activities, or anomalies. They employ advanced algorithms for motion detection, facial recognition, and behavioural analysis, allowing for the rapid identification of suspicious activities or individuals. Object detection, a key element of these systems, involves the identification and classification of objects within a given scene. This is typically achieved through deep learning techniques, such as convolutional neural networks (CNNs), which can accurately detect and categorize objects like vehicles, weapons, or unattended bags. The integration of object detection with active surveillance enables more precise and automated responses to potential threats, reducing reliance on human operators and minimizing the risk of oversight. The adoption of active surveillance and object detection technologies has led to significant improvements in security management, offering enhanced situational awareness and faster response times. However, these technologies also raise concerns regarding privacy, data security, and ethical implications, particularly in their deployment in public and private spaces. Addressing these concerns requires careful consideration of data governance, transparency, and regulatory compliance to balance security needs with individual rights. As technology continues to evolve, ongoing research and development are essential to improving the accuracy, efficiency, and ethical deployment of active security surveillance and object detection systems.

THE SUITABILITY OF ACTIVITIES IN THE TRADE SUB-ZONE (THE CORRIDOR OF LETDA SUJONO ROAD, MEDAN TEMBUNG SUB-DISTRICT, MEDAN CITY)

The development of population and infrastructure in cities can enhance the local economy, as seen in the Letda Sujono Road corridor. This area, located between Medan City and Deli Serdang Regency and near the Bandar Selamat Toll Gate, has seen significant growth in trade and service activities. This growth has spurred the development of housing, public services, commercial spaces, warehousing, and industry, increased land demand and causing potential land-use conflicts and traffic issues. Inappropriate space utilization can lead to urban sprawl, often due to inadequate spatial planning control. The Medan City Detailed Spatial Plan (DSP or Rencana Detail Tata Ruang (RDTR)) designates the Letda Sujono Road corridor as a Trade Sub-zone, with zoning regulations limiting some activities to 10% of the block area. Aims: The objective of this study was to study the suitability of existing activities with the zoning plan which has been regulated in the Medan City Detailed Spatial Plan as a space control content in order to create safe, comfortable and sustainable spatial planning. Methodology and results: This study uses a qualitative descriptive method with spatial analysis to assess the alignment of current activities with the DSP zoning plan. Conclusion, significance and impact study: The findings reveal that 60.84% of the activities are suitable, while 39.16% are not. Some blocks, particularly Block 17-07-01 (Tembung), have exceeded the 10% activity limit, indicating that zoning plans have not been fully realized, with unauthorized activities such as integrated schools and vehicle-related services still present.

Detecting vulnerabilities in website using multiscale approaches: based on case study

In the realm of modern web applications, security stands as an utmost priority. To address this critical concern, we've developed a versatile Python script with the primary goal of proactively identifying vulnerabilities and thwarting transient attacks. Leveraging various libraries, this tool comprehensively covers a broad spectrum of threats, including SQL injection (SQLi), cross-site scripting (XSS), cross-site request forgery (CSRF), sensitive data leakage, security misconfiguration, distributed denial-of-service (DDoS) vulnerabilities, and secure socket layer (SSL) or transport layer security (TLS). This Python-based solution prioritizes adaptability, ensuring seamless integration of future updates to effectively combat evolving threats. Utilizing innovative methods such as SQLi and XSS payload injection, the script assesses the susceptibility of input fields. And addressing CSRF vulnerabilities, the script generates and validates tokens, fortifying defenses against unauthorized actions. Employing pattern analysis, it combats sensitive data exposure and security misconfigurations, adeptly identifying elements like credit card numbers, passwords, and headers. Furthermore, the script enhances overall security by scrutinizing SSL/TLS protocols and monitoring port accessibility. It reinforces DDoS detection by actively monitoring traffic patterns, identifying anomalies, and proactively averting disruptions.

Smart Supply Chain Management Using Machine Learning Algorithms

Currently, supply chains are the most widely used in marketing around the world to increase productivity and to add more features to make supply chain management easy to handle. For smart supply chain management, the main focus is on analyzing a product demand and supply chain with fraud suspects of customers and delays in product delivery. RFID can be further extended to authenticate items used to track and manage products. Demand forecasting is a crucial part of any company or supply chain. It aims at predicting and estimating the future demand of products to help in better decision-making and fraud detection is useful because it allows businesses to identify and prevent unauthorized activities. With the integration of machine learning and the IoT, supply chain management can effectively increase the demand for productivity.

Unauthorized Drone Detection Using Deep Learning

Abstract: The growing concerns over drone misuse in national airspace, including activities like drug smuggling and privacy violations, demand advanced drone detection systems. Traditional methods struggle to accurately identify drones among various airborne objects. YOLO v8, renowned for its efficient single-pass object detection, presents a sophisticated solution. Through training on annotated datasets featuring drones in diverse environments and fine-tuning with aerial imagery, YOLO v8 excels at recognizing drones amidst complex backgrounds. Its multi-scale detection capabilities enable it to detect drones of varying sizes, crucial for different distances from the camera. Real-time prediction ensures timely detection, vital for immediate responses to unauthorized intrusions. Integration with drone tracking systems enhances situational awareness, facilitating proactive measures. Furthermore, employing adaptive thresholding and post-processing techniques like Kalman filtering refines drone detection accuracy, reducing false positives. YOLO v8 thus emerges as a potent tool in mitigating risks associated with drone misuse, bolstering defenses against unauthorized activities in national airspace.

Providing a hybrid approach to increase the accuracy of intrusion detection systems in computer networks

Intrusion detection is a critical obstacle in the realm of security and data mining methodologies. Consequently, researchers have extensively investigated the quest for the swiftest and most precise means of identifying intrusions. Essentially, intrusion detection systems are tasked with recognizing any unauthorized activities, misuse, or harm inflicted upon a system, be it by internal or external users. Recently, in order to design intrusion detection systems, artificial intelligence and machine learning methods have been used, each of which has its own characteristics and advantages. Accordingly, this article focuses on using machine learning to improve the accuracy of the intrusion detection process. In fact, by using machine learning, trends, and patterns can be easily identified and thus used in a network environment to detect intrusion. It can be very useful. For this purpose, we utilize Radial Basis Function (RBF) neural networks and support vector machine (SVM) algorithm to improve decision-making and intrusion detection. By employing RBF neural networks, important features of the data are extracted, which in turnPlease check if the author details and affiliations are presented correctly. Kindly amend if necessary. enhance the overall performance of the solution and the efficiency of the SVM algorithm. This is because feature reduction ultimately leads to improved effectiveness of the SVM algorithm. In methods lacking this capability, the learning algorithm is compelled to utilize features that have no specific correlation with intrusion and essentially do not contribute to identifying attacks. Such learning approaches essentially learn from noisy data, which negatively impacts the intrusion detection solution. Finally, the proposed solution was evaluated using Python programming language and KDD99 data set. The results of the evaluation indicate that the proposed solution has a higher accuracy and precision than other evaluated solutions. So, the accuracy is 97, and the precision is over 99%.

Improving the system of protection of the rights of buyers of vehicles

The subject of the study is the existing system of protection of the rights of buyers of vehicles, which includes the prevention of offenses involving liability for misleading consumers about consumer properties or quality of goods (Article 14.7 of the Administrative Code of the Russian Federation) and for causing property damage by deception or abuse of trust (Article 7.27.1. of the Administrative Code of the Russian Federation), the commission of which involves, including for example, with the correction or reduction of the recorded mileage of the car. The connection of these actions with the technical condition of vehicles and road safety is considered, since any unauthorized actions with the technical condition of the vehicle are associated with a decrease in the level of safety of road users. The prevalence of this phenomenon requires an adequate response from the state's law enforcement system and the establishment of legislative barriers for this activity. The research methodology is based on general scientific (logical, systematic, analysis, generalization) and private scientific (comparative, formal legal) methods. The scientific novelty of the study lies in an integrated approach to studying the existing procedure for protecting the rights of buyers of vehicles at risk of being misled about the quality of the goods. The article substantiates the need to create a legal mechanism at the state level and on its basis an open mileage accounting system for used cars for citizens, which allows them to track their history from the moment they go on sale. It is proposed to give a legal assessment of the actions and establish liability for persons who, through the provision of services for correcting car mileage data, contribute to deceiving consumers, causing property damage to buyers, and are in direct connection with the decisions of vehicle owners on their maintenance and repair, which affects road safety.

Convnext-Eesnn: An effective deep learning based malware detection in edge based IIOT

A rising number of edge devices, like controllers, sensors, and robots, are crucial for Industrial Internet of Things (IIoT) networks for collecting data for communication, storage, and processing. The security of the IIoT could be compromised by any malicious or unusual behavior on the part of any of these devices. They may also make it possible for malicious software placed on end nodes to enter the network and perform unauthorized activities. Existing anomaly detection techniques are less effective due to the increasing diversity of the network and the complexity of cyberattacks. In addition, most strategies are ineffective for devices with limited resources. Therefore, this work presents an effective deep learning based Malware Detection framework to make the edge based IIoT network more secure. This multi-stage system begins with the Deep Convolutional Generative Adversarial Networks (DCGAN) based data augmentation method to overcome the issue of data imbalance. Next, a ConvNeXt-based method extracts the features from the input data. Finally, an optimized Enhanced Elman Spike Neural Network (EESNN) based deep learning is utilized for malware recognition and classification. Using two distinct datasets— MaleVis and Malimg— the generalizability of the suggested model is clearly demonstrated. With an accuracy of 99.24% for MaleVis and 99.31% for the Malimg dataset, the suggested strategy demonstrated excellent results and surpassed all other existing methods. It illustrates how the suggested strategy outperforms alternative models and offers numerous benefits.

The Crime of Invasion Electronic Data and Information in the Emeriti Law

This study examines the crime of unauthorized access to electronic data and information, focusing on the role of the UAE legislator in combating this crime through Federal Decree-Law No. 34 of 2021 on Combatting Rumors and Cybercrimes. It assesses the law’s effectiveness in addressing unauthorized activities such as the destruction or removal of information and data, and invasions of privacy. The paper concludes with recommendations to strengthen legal measures against such crimes. Key suggestions include defining “invasion” within the law to clarify the crime and its penalties, distinguishing between personal and professional email breaches with harsher penalties for the latter due to their significant impact on both state-owned and private enterprises, and recognizing the disruption of information networks as a punishable offense. These recommendations aim to enhance the UAE’s legal framework for protecting electronic data and information.

Ensemble Learning for Robust Malware Detection in the Windows 7 Environment

In today's digital era, the exponential growth in the dissemination of malicious software presents a formidable and pervasive threat to individuals and organizations alike. Malware, comprising of computer code or scripts, exhibits a propensity to override computer systems and engage in unauthorized activities, such as the illicit extraction of valuable information. The magnitude of such control is particularly perilous in the contemporary landscape. The rapid and constant evolution of malware compounds this risk, necessitating the development and implementation of sophisticated countermeasures to combat this pressing issue. In response to this challenge, this study has turned to the utilization of ensemble learning techniques as a means of effectively and accurately identifying and detecting malware. Specifically, the investigation focuses on the classification of applications on the MS Windows 7 and 8 operating systems as either malicious or benign, by leveraging static and dynamic features extracted from these applications. The foundational models employed in this study consist of recurrent neural networks (RNNs), trained on the dynamic features of malware, and convolutional neural networks (CNNs), and trained on the static features of malware. This comprehensive approach ensures that no malware goes undetected. Multiple techniques for constructing an ensemble model, such as Boosting, Stacking, and Bagging, are thoroughly examined and analyzed. Ultimately, the Bagging technique is deemed the most suitable and is consequently implemented in this study.

Machine Learning for Web Vulnerability Detection: The Case of Cross-Site Request Forgery

Cross-site request forgery (CSRF) vulnerabilities pose a significant threat to web application security, enabling attackers to execute unauthorized actions on behalf of authenticated users. Conventional CSRF detection methods, such as manual code review and static analysis, are often time-consuming, error-prone, and inefficient. Proposes Mitch, a novel machine learning (ML)-based solution for the black-box detection of CSRF vulnerabilities. Mitch employs supervised learning, trained on a comprehensive dataset of HTTP requests and responses, to effectively identify security-sensitive HTTP requests and uncover CSRF vulnerabilities within them. Rigorous evaluations on a diverse set of real-world web applications demonstrate Mitch's remarkable ability to detect CSRF vulnerabilities with high accuracy, outperforming traditional methods. Mitch's automated nature eliminates the need for manual code review and static analysis, saving time and effort while reducing the risk of human error. Additionally, Mitch's scalability allows seamless integration into continuous integration and continuous delivery (CI/CD) pipelines, enabling continuous security monitoring and vulnerability detection. Mitch's efficacy extends beyond detecting known CSRF vulnerabilities. Its ability to identify patterns and relationships enables it to uncover obscure CSRF vulnerabilities that may have been overlooked by traditional methods, including zero-day vulnerabilities. In conclusion, Mitch emerges as a powerful tool for enhancing web application security, offering a comprehensive and automated solution for detecting CSRF vulnerabilities. Its ability to handle complex web applications, uncover hidden CSRF vulnerabilities, and integrate into CI/CD pipelines makes it an indispensable tool for web security professionals. Mitch's adoption has the potential to significantly reduce the risk of CSRF attacks and safeguard sensitive user data. We propose a methodology to leverage machine learning (ML) for the detection of web application vulnerabilities. We use it in the design of Mitch, the first ML solution for the black-box detection of cross-site request forgery vulnerabilities. Finally, we show the effectiveness of Mitch on real software. In this project, we propose a methodology to leverage Machine Learning (ML) for the detection of web application vulnerabilities. Web applications are particularly challenging to analyses, due to their diversity and the widespread adoption of custom programming practices. ML is thus very helpful for web application security it can take advantage of manually labeled data to bring the human understanding of the web application semantics into automated analysis toolsMitch allowed us to identify 35 new CSRFs on 20 major websites and 3 new CSRFs on production software. Keywords: Mitch, CSRF, CI/CD pipelines, Security Token Service (STS), Same-Origin Policy (SOP).

Cyberloafing deterrence in the public sector of Ghana

AbstractThe integration of the Internet into various aspects of business operations, including the public sector, is rapidly becoming a norm. However, within this evolving landscape, the issue of cyberloafing, the misuse of the internet for unauthorized activities, cannot be ignored. Existing research on cyberloafing in developing countries, specifically in Ghana, has predominantly focused on higher education students. A brief examination of the literature pertaining to cyberloafing in developing nations reveals a prevalent emphasis on categorizing cyberloafing activities, often with limited insight into the motivations and deterrence of such behavior. Drawing on the framework of the General Deterrence theory, this paper delves into the underlying factors that deter cyberloafing behavior within the public sector of Ghana. Through the utilization of a partial least square structural equation model, the study analyzed data from 473 responses collected from public institutions across Ghana. The study revealed that factors such as certainty, monitoring, and security risks significantly influence the manifestation of cyberloafing behavior among employees in the Ghanaian public sector. However, the Severity and Celerity of sanctions were found not to influence cyberloafing behavior. The moderating effect of age and culture on the deterrent constructs and cyberloafing behavior vary. The study serves as a strategic framework to curd the rampant cyberloafing activities in many public sector institutions particularly in the global south. In essence, this study contributes not only to the understanding of cyberloafing behavior in the Ghanaian public sector but also provides a valuable foundation for designing targeted interventions to address this issue, ultimately fostering a more productive and responsible online engagement within these institutions.

Dugat-LSTM: Deep learning based network intrusion detection system using chaotic optimization strategy

Network intrusion is a huge harmful activity to the privacy of the data sharing network. The activity will result in a cyber-attack, which causes damage to the system as well as the user’s data. Unauthorized activities such as data tampering, illegal access to data and theft of credentials are increasing on the internet world every day. The detection of intrusion may be done by multiple methodologies; still, it is the biggest issue in the networks. Hence, an automated attack classification model is required to promote classification accuracy with fewer error possibilities based on the input parameters. To get relief from the insecurity of data, this paper presents an innovative model using deep networks. The proposed model is a deep learning based network intrusion detection system using a chaotic optimization strategy. The method is pre-processed using data cleansing and M-squared normalization. After pre-processing, the unbalanced datasets are balanced using the Extended Synthetic Sampling approach. After balancing, the features of the dataset are taken out using kernel-assisted principal component analysis. The optimal features are selected by the Chaotic Honey Badger optimization algorithm. After all required features have been extracted, the attacks are classified by the Gated Attention Dual Long Short Term Memory (Dugat-LSTM). The above process is performed using the TON-IOT and NSL-KDD datasets. The prototype is evaluated using the following metrics: accuracy, precision, recall, and F1 score. The accuracy value of the proposed model is 98.76% in the TON-IOT dataset and 99.65% in the NSL-KDD dataset. Thus, the accuracy and robustness of the model show that it outperforms other existing models.

Implementasi Wazuh Integritas File untuk Perlindungan Keamanan Berdasarkan Aktivitas Log di BTSI UKSW

Technological developments are now increasingly sophisticated, especially in data security. Data security and File Integrity have an essential role and are one of the main concerns in the corporate environment. Practical steps are needed for a data security system that detects data threats, especially File Integrity within the SWCU BTSI environment. The Wazuh system provides monitoring and detection of File change activity. This feature works because of Real-Real File change activity and log analysis. To maintain system security is to monitor File Integrity, namely ensuring that critical Files are not subject to unauthorized changes by unauthorized parties. Therefore, research was carried out and implementation of File Integrity Wazuh for security protection based on log activity at SWCU BTSI. The results showed that the implementation of the File Integrity Wazuh system can identify security incidents against suspicious activity on a File and is effectively able to provide notifications to administrators against unauthorized activity

Improving Network Security: An Intrusion Detection System (IDS) Dataset from Higher Learning Institutions, Mbeya University of Science and Technology (MUST), Tanzania

Nowadays, Internet-driven culture securing computer networks in Higher Learning Institutions (HLIs) has become a major responsibility. Intrusion Detection Systems (IDS) are crucial for protecting networks from unauthorized activity and cyber threats. This paper examines the process of improving network security by creating a comprehensive IDS dataset using real traffic from HLIs, highlighting the importance of accurate and representative data in improving the system's ability to identify and mitigate future cyber-attacks. The IDS model was created using a variety of machine learning (ML) techniques. Metrics like accuracy, precision, recall, and F1-score were used to assess the performance of each model. The dataset used for training and testing was real-world network traffic data obtained from the institution's computer network. The results showed that the developed IDS obtained exceptional accuracy rates, with Random Forest, Gradient Boosting, and XGBoost models all achieving an accuracy of around 93%. Precision and recall values were likewise quite high across all algorithms. Furthermore, the study discovered that data quality has a substantial impact on IDS performance. Proper data preparation, feature engineering, and noise removal were found to be helpful in improving model accuracy and reducing false positives. While the IDS models performed well throughout validation and testing, implementing such systems in a production setting necessitates careful thought. As a result, the essay also examined the procedures for testing and deploying the IDS models in a real-world scenario. It underlined the significance of ongoing monitoring and maintenance in order to keep the model effective in identifying intrusions. The research aids in the progress of network security in HLI. Educational institutions can better protect their precious assets and sensitive information from cyberattacks by understanding the impact of data quality on IDS performance and implementing effective deployment techniques

A Hybrid Intrusion Detection Approach for Cyber Attacks

The field of cybersecurity constantly evolves as attackers develop new methods and technologies. Defending against cyberattacks involves a combination of robust security measures, regular updates, user education, and the use of advanced technologies, such as intrusion detection systems and artificial intelligence, to find out the threats in real-time. IDS are designed to identify and address any unauthorized actions or potential security threats within a computer network or system. A hybrid intrusion detection system (IDS) combines many detection techniques and strategies from different IDS types into a single, coherent solution. Combining the benefits of each approach should result in more comprehensive and effective intrusion detection. This paper outlines a proposed anomaly intrusion detection system (AIDS) framework that leverages a hybrid of deep learning strategies. It incorporates Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models, which were developed using XGBoost, and their efficacy was assessed with the NSL-KDD dataset. The evaluation of the suggested model focused on its accuracy, detection capabilities, and the rate of false positives. The outcomes of this research are noteworthy within the cybersecurity field. In this paper, a framework of an Anomaly IDS is proposed. The purpose of an anomaly IDS, or AIDS, is to spot odd behavior on a network or system that might point to a security breach or malevolent attempt to hack it. Anomaly-based IDSs concentrate on finding departures from accepted typical behavior, in contrast to signature-based detection systems, which depend on a predefined database of known attack patterns.

Improving Network Security: An Intrusion Detection System (IDS) Dataset from Higher Learning Institutions, Mbeya University of Science and Technology (MUST), Tanzania

Nowadays, Internet-driven culture securing computer networks in Higher Learning Institutions (HLIs) has become a major responsibility. Intrusion Detection Systems (IDS) are crucial for protecting networks from unauthorized activity and cyber threats. This paper examines the process of improving network security by creating a comprehensive IDS dataset using real traffic from HLIs, highlighting the importance of accurate and representative data in improving the system's ability to identify and mitigate future cyber-attacks. The IDS model was created using a variety of machine learning (ML) techniques. Metrics like accuracy, precision, recall, and F1-score were used to assess the performance of each model. The dataset used for training and testing was real-world network traffic data obtained from the institution's computer network. The results showed that the developed IDS obtained exceptional accuracy rates, with Random Forest, Gradient Boosting, and XGBoost models all achieving an accuracy of around 93%. Precision and recall values were likewise quite high across all algorithms. Furthermore, the study discovered that data quality has a substantial impact on IDS performance. Proper data preparation, feature engineering, and noise removal were found to be helpful in improving model accuracy and reducing false positives. While the IDS models performed well throughout validation and testing, implementing such systems in a production setting necessitates careful thought. As a result, the essay also examined the procedures for testing and deploying the IDS models in a real-world scenario. It underlined the significance of ongoing monitoring and maintenance in order to keep the model effective in identifying intrusions. The research aids in the progress of network security in HLI. Educational institutions can better protect their precious assets and sensitive information from cyberattacks by understanding the impact of data quality on IDS performance and implementing effective deployment techniques