Implementation Of Security Measures Research Articles

Abstract Su504: The Impact of Locked Cabinets for Automated External Defibrillators (Aeds) on Cardiac Arrest Outcomes: A Scoping Review

Background: Rapid defibrillation by the public with automated external defibrillators (AEDs) is critical to improving out-of-hospital cardiac arrest (OHCA) survival. Concerns about theft, vandalism, and misuse of AEDs have led to the implementation of security measures, including the use of locked cabinets to house these devices in public areas. Aim: This scoping review aims to systematically explore the existing literature on the impact of locked cabinets for AEDs during emergencies. Methods: A search of Medline, Embase, Cochrane, CINAHL and Google Scholar (20 pages) was performed on May 25th 2024. Studies of any design (e.g., experimental, observational, qualitative) that evaluated the impact of locked cabinets on AED accessibility and effectiveness during emergencies. Data were charting was iterative, and after reading included studies the studies were grouped by the outcomes studied. Results: We screened 2,096 titles and found 10 relevant studies: 8 observational studies and 2 OHCA simulation studies. Four papers were only published as conference abstract and no studies reported on patient outcomes. Data were reported on varying numbers of AEDS (ranging from 39 to 31,938) which were located inside buildings and public spaces. Overall theft and vandalism rates were very low, with the majority of studies reporting rates of <2%. Although in some studies it was unclear if the AED was stolen, or whether it had been used in an emergency and not returned. The impact of locking AEDs in cabinets was reported in one study, and showed minimal impact on rates of theft and vandalism (0.34% vs. 0.12%, p< 0.01). Two OHCA simulation studies conducted at AED locations showed significantly slower AED retrieval when AEDs were locked away. One survey of first responders reported that 50% (25/50) were injured while accessing an AED, which required breaking glass. Conclusion: Rates of theft and vandalism of AEDs located in private and public locations are low overall and in unlocked cabinets. Further research is needed on the impact of locked AEDs on patient outcomes.

How CCTV shapes perceptions of safety in London’s public transport: a quantitative analysis

ABSTRACT The perception of safety while using public transportation varies among individuals, influenced by personal experiences and individual characteristics. Closed-circuit television (CCTV) systems are commonly employed to deter crime in public areas, including transit environments where safety concerns are prevalent. This study aims to scientifically assess the effectiveness of CCTV in enhancing the perceived safety of public transportation users. A comprehensive survey was conducted, involving over 200 students from various universities to ensure a representative sample. The survey targeted public transportation users and focused on their sense of security in relation to the presence of CCTV. Through quantitative analysis, three specific research questions were addressed. The results indicate that the presence of CCTV alone is insufficient to significantly improve feelings of safety among public transportation users. These findings suggest important policy implications: enhancing perceived safety in public transportation requires more than just the implementation of security measures. It is essential to inform and educate the public about actual crime rates, existing security measures, and the importance of crime reporting. This comprehensive approach can more effectively address the multifaceted nature of safety perception in public transportation.

Hybrid dung beetle optimization based dimensionality reduction with deep learning based cybersecurity solution on IoT environment

The Internet of Things (IoT) interconnects various devices and objects through the Internet to interact with corresponding devices or machines. Now, consumers can purchase many internet-connected products, from automobiles to refrigerators. Extending network capacities to every aspect of life can save money and time, increase efficiency, and enable greater access to digital experiences. Cybersecurity analysts often refer to this as increasing the attack surface from which hackers can benefit. Implementing the proper security measures is crucial since IoT devices can be vulnerable to cyberattacks and are often built with limited security features. Securing IoT devices involves implementing security measures and best practices to secure them from potential vulnerabilities and threats. Deep learning (DL) models have recently analyzed the network pattern for detecting and responding to possible intrusions, improving cybersecurity with advanced threat detection abilities. Therefore, this study presents a new Hybrid Dung Beetle Optimization-based Dimensionality Reduction with a Deep Learning-based Cybersecurity Solution (HDBODR-DLCS) method on the IoT network. The primary goal of the HDBODR-DLCS technique is to perform dimensionality reduction with a hyperparameter tuning process for enhanced detection results. In the primary stage, the HDBODR-DLCS technique involves Z-score normalization to measure the input dataset. The HDBO model is used for dimensionality reduction, which mainly selects the relevant features and discards the irrelevant features. Besides, intrusions are detected using the attention bidirectional recurrent neural network (ABiRNN) model. Finally, an artificial rabbits optimization (ARO) based hyperparameter tuning process is performed, enhancing the overall classification performance. The empirical analysis of the HDBODR-DLCS method is tested under the benchmark IDS dataset. The simulation outcomes indicated the HDBODR-DLCS method's improved abilities over existing approaches.

SBAC-SDN: A Scalable Blockchain-based Access Control in Northbound Interface for Multi-Controller SDN with Load Balancing Mechanism

Abstract The rapid adoption of Software-Defined Networking (SDN) has not only revolutionized network management but also presented challenges in scalability and security, particularly in multi-controller environments. This paper introduces a load balancing technique aimed at enhancing the scalability of the Northbound interface, a critical component often susceptible to performance bottlenecks. The experimental setup utilized Mininet and Ryu to create a simulated multi-controller SDN environment, where SBAC-SDN was thoroughly evaluated based on CPU and memory usage, response times, and error rates. The results showed substantial improvements in critical performance indicators, confirming the effectiveness of the load balancing technique in addressing scalability challenges. The findings indicate that the system can be scaled without excessive resource consumption, as CPU usage peaked at 60.3% and memory usage remained below 8 GB even with eight controllers. The stable average response times were consistently below 1 s, and the high throughput, ranging from 4.34 to 4.67 requests per second, further demonstrates the efficiency of the system. The error rate remained 0.0% across all configurations, highlighting the robustness of the implemented security measures. This study contributes to ongoing efforts to improve the scalability and overall robustness of SDNs, aligning with the broader goals of reliability and efficiency in network management.

Responsive patient care in Israel: A qualitative study of hospital rules and regulations

Hospital rules and regulations can contribute to standardizing care, streamlining processes, and improving the quality of care. Over the past decade, hospitals in Israel have introduced written rules and regulations for staff that provide guidance on quality control, patient safety, and the patient-provider relationship. This study aimed to explore how these written guidelines, when implemented, can promote responsive care for inpatients. Using a thematic analysis, the study analyzed the content of staff guidelines from six Israeli hospitals. The analysis found that hospital rules and regulations provide similar, relatively precise instructions with regard to improving the responsiveness to and dignity of care of patients. The guidelines address three essential aspects of responsiveness - disclosing medical information and respecting the patients’ autonomy and physical space. The guidelines highlight that healthcare providers should implement security measures to safeguard medical information, respect patients' autonomy, involve patients in decision-making, and provide adequate physical space to maintain their privacy and modesty. The guidelines contribute to ensuring patients’ legal and ethical rights. Policymakers should consider introducing and implementing the dimensions of responsiveness that were stressed by Israeli hospitals' rules and regulations. Further research is needed to confirm the relevance of the various rules and regulations for improving the quality of care provided to patients.

Vulnerability extraction and prediction method based on improved information gain algorithm.

More and more attention has been paid to computer security, and its vulnerabilities urgently need more sensitive solutions. Due to the incomplete data of most vulnerability libraries, it is difficult to obtain pre-permission and post-permission of vulnerabilities, and construct vulnerability exploitation chains, so it cannot to respond to vulnerabilities in time. Therefore, a vulnerability extraction and prediction method based on improved information gain algorithm is proposed. Considering the accuracy and response speed of deep neural network, deep neural network is adopted as the basic framework. The Dropout method effectively reduces overfitting in the case of incomplete data, thus improving the ability to extract and predict vulnerabilities. These experiments confirmed that the excellent F1 and Recall of the improved method reached 0.972 and 0.968, respectively. Compared to the function fingerprints vulnerability detection method and K-nearest neighbor algorithm, the convergence is better. Its response time is 0.12 seconds, which is excellent. To ensure the reliability and validity of the proposed method in the face of missing data, the reliability and validity of Mask test are verified. The false negative rate was 0.3% and the false positive rate was 0.6%. The prediction accuracy of this method for existing permissions reached 97.9%, and it can adapt to the development of permissions more actively, so as to deal with practical challenges. In this way, companies can detect and discover vulnerabilities earlier. In security repair, this method can effectively improve the repair speed and reduce the response time. The prediction accuracy of post-existence permission reaches 96.8%, indicating that this method can significantly improve the speed and efficiency of vulnerability response, and strengthen the understanding and construction of vulnerability exploitation chain. The prediction of the posterior permission can reduce the attack surface of the vulnerability, thus reducing the risk of breach, speeding up the detection of the vulnerability, and ensuring the timely implementation of security measures. This model can be applied to public network security and application security scenarios in the field of computer security, as well as personal computer security and enterprise cloud server security. In addition, the model can also be used to analyze attack paths and security gaps after security accidents. However, the prediction of post-permissions is susceptible to dynamic environments and relies heavily on the updated guidance of security policy rules. This method can improve the accuracy of vulnerability extraction and prediction, quickly identify and respond to security vulnerabilities, shorten the window period of vulnerability exploitation, effectively reduce security risks, and improve the overall network security defense capability. Through the application of this model, the occurrence frequency of security vulnerability time is reduced effectively, and the repair time of vulnerability is shortened.

Detection and Analysis of Malicious Software Using Machine Learning Models

The continuous evolution of malware poses a significant challenge in cybersecurity, adapting to technological advancements despite implemented security measures. This paper introduces an innovative approach to enhance the detection of obfuscated malware through the integration of machine learning (ML). Utilizing a real-world dataset of prevalent malware types such as spyware, ransomware, and trojan horses, our study addresses the evolving challenges of cybersecurity. In this study, we evaluate the performance of ML algorithms for obfuscated malware detection using the CIC-MalMem-2022 dataset. Our analysis encompasses binary and multi-class classification tasks under various experimental conditions, including percentage splits and 10-fold cross-validation. The evaluated algorithms include Random Tree (RT), Random Forest (RF), J-48 (C4.5), Naive Bayes (NB), and XGBoost. Experimental results demonstrate the effectiveness of RF, J-48, and XGBoost in achieving high accuracy rates across different classification tasks. NB also shows competitive performance but faces challenges in handling imbalanced datasets and multi-class classification. Our findings highlight the importance of employing advanced ML techniques for enhancing obfuscated malware detection capabilities and provide valuable insights for cybersecurity practitioners and researchers. Future research directions include fine-tuning model hyperparameters, exploring ensemble learning approaches, and expanding evaluation to diverse datasets and real-world scenarios.

Economic security of the enterprise: modern challenges and threats

In this article is defined and analyzed the concept of economic security of enterprises. The article examines various aspects of economic security of an enterprise, in particular, the classification of threats that may adversely affect its activities. Internal and external threats, their sources, nature, probability of realization and possible consequences are characterized. The methods of neutralizing these threats are considered, in particular, the possibility of creating a sustainable system of economic security at an enterprise and measures to minimize risks and threats are considered. The principles and stages of implementation of security measures that allow an enterprise to more effectively withstand potential challenges are presented.

ПРОАКТИВНОЕ МОДЕЛИРОВАНИЕ УТЕЧЕК ДАННЫХ ОГРАНИЧЕННОГО ДОСТУПА НА ОБЪЕКТАХ КРИТИЧЕСКОЙ ИНФОРМАЦИОННОЙ ИНФРАСТРУКТУРЫ (НА ПРИМЕРЕ ТРАНСПОРТНОЙ ОТРАСЛИ)

The article presents a comprehensive methodology for predicting data breaches and assessing the potential risks of harmful effects on critical information infrastructure, using the example of the transport industry. Statistical data on the number of restricted access data leaks in Russia from 2013 to 2022 are analyzed. Within the study, several numerical forecasting methods are compared: linear regression, moving average smoothing, and exponential smoothing. Linear regression was found to be the most accurate method for predicting restricted access data leaks in the transport sector for 2024 to 2028. Additionally, a cognitive model is proposed that allows for assessing the likelihood of data breaches and their potential consequences, considering factors such as attack vectors, system vulnerabilities, user behavior, and implemented security measures. The combined approach, which combines numerical methods and cognitive modeling, provides a comprehensive view of cybersecurity risks. This allows for more accurate predictions and better-informed decisions. The study's results emphasize the importance of considering both technical and human aspects in improving cybersecurity, and offer recommendations for future research that could refine the cognitive model by involving experts from not only the technical field, but also the economic and legal domains.

AI-based IoT attack filtering methods in the context of connected vehicles

With the rapid development of connected vehicle technology, the volume of data generated in the context of connected vehicles is exhibiting an exponential growth trend compared to the traditional automotive industry. This data is closely related to the security of connected vehicles. However, if threats such as malicious intrusions, information tampering, and data theft cannot be effectively resisted, they will pose significant risks and hazards to users, the entire automotive industry, and society. Therefore, researching and implementing security measures for connected vehicles becomes crucial to ensure the sustainable development and widespread application of connected vehicle technology. In this regard, this paper proposes an optimized IoT attack filtering method based on random forest and neural network algorithms in deep learning to predict and filter malicious attacks. The CICIOT2023 dataset is used for training and evaluation, and results are analyzed based on accuracy, recall, precision, and F1 scores. A comparison between models constructed using random forest and neural networks reveals that while both perform similarly in terms of accuracy and precision, the model built with neural networks significantly outperforms random forest in terms of recall and F1 score. Therefore, for IoT attack filtering methods targeted at connected vehicle scenarios, the algorithm using neural networks for attack identification and filtering is recommended, as it can better protect the confidentiality and security of data in connected vehicle scenarios, ensuring user driving safety and safeguarding societal transportation security.

The Current Status and Challenges of Cybersecurity Risks

<i>Research Context and Aims</i>: The swift expansion of internet technologies has rendered the digital network essential to contemporary life, highlighting the significance of cybersecurity. Network data not only encompasses personal information management and communication but also pertains to the protection of confidential corporate, governmental, and vital national infrastructures. Consequently, cybersecurity has become a critical issue in current society. Frequent occurrences like privacy violations, data thefts, and national security threats underline the critical need for enhanced network defenses and protective measures. <i>Research Approach</i>: This study examines the state of cybersecurity by assessing pertinent studies across public databases such as PubMed, CNKI, and CrossRef. It compiles and evaluates significant cybersecurity events including data breaches, malware attacks, and phishing, outlining key security challenges faced by networks. The paper also evaluates the current cybersecurity technologies and methods, pinpointing their effectiveness and limitations in addressing network threats. <i>Research Findings</i>: The findings reveal that cyber attackers have refined their methods, employing sophisticated, covert techniques for prolonged periods, which often outpace current defenses. In cases of data breaches, perpetrators frequently utilize precise social engineering or deploy advanced persistent threats (APTs). Additionally, the proliferation of IoT technology has not only obscured the boundaries of cybersecurity but also broadened potential attack vectors. Despite the implementation of security measures like encryption and multi-factor authentication, these can be compromised by managerial or operational oversights. <i>Research Conclusions</i>: With the cybersecurity landscape becoming increasingly challenging, future defenses will likely prioritize the adoption of integrated, proactive strategies. It is crucial to foster the development of smart security solutions, such as leveraging artificial intelligence to detect and respond to anomalies. Furthermore, boosting security awareness among users and ensuring standardized practices are imperative. Ultimately, formulating future cybersecurity policies will require a holistic approach, integrating technological, managerial, legal, and educational initiatives to forge a robust network defense architecture.

A method of using modern endpoint detection and response (EDR) systems to protect against complex attacks

The subject of the research in this article is the architecture of Endpoint Detection and Response and the EDR agent as their base parts in terms of mechanisms for detecting and countering complex attacks on information and communication systems (ICS). The aim of the work is to develop of method for improving the efficiency of using Endpoint Detection and Response (EDR) to reduce the risks of compromising ICS information, industrial, and infrastructure objects by effectively redistributing and utilizing the available EDR mechanisms, the cybersecurity team, and other resources available for implementing security measures in an enterprise, institution, or organization. The article addresses the following tasks: reviewing and analyzing existing EDR systems, analyzing the architecture of EDR solutions and EDR agents, the features of their use, the logic behind the construction of methods and mechanisms for detecting threats to the system from malicious actors and malicious code. The task of providing recommendations for the organization of ICS is also separately addressed in terms of the need to protect the entire ICS and its individual elements, as well as in terms of the available resources (the cybersecurity team, their qualifications and level of awareness of the architecture of EDR solutions) and means (available EDR system elements) for organizing protection. The following methods are used: modeling attack mechanisms, modeling attacker behavior. The following results were obtained: general and specific recommendations were formulated for optimizing the operation of EDR systems and ensuring the effective use of EDR system elements in the information and communication networks of enterprises, organizations, and institutions of various types and orientations depending on the available resources and the information requiring protection. Conclusions: The identified recommendations for the application of EDR mechanisms for protecting information systems and networks allow optimizing the costs of creating a protection infrastructure and implementing security measures, taking into account the characteristics of the available tools and the training and awareness of the cybersecurity team both in terms of response time to threats and the complexity and cost of performing protection tasks.

Simplifying security processes in large organizations while maintaining an appropriate level of security

This paper presents an approach to simplifying security in large organizations, ensuring that the work of ordinary employees is more accessible without negatively impacting the organization's security. Access to the Internet is becoming increasingly important in various aspects of human life, including interpersonal communication, professional work, and business operations. Enterprises have digitized information on their devices, which can be of interest to attackers. Incorrect security measures can expose companies to various losses, such as financial costs or image losses. As the number of enterprises connected to the Internet grows, IT system security is gaining importance. Cyberattacks are malicious actions by individuals or organized groups with motives such as financial gain, employee dissatisfaction, or government interference. Attackers aim to access financial data, customer lists, financial data, and customer databases. Cybersecurity should be the basis of every organization, regardless of its size. The number of attacks each year is related to increased investments in cybersecurity strategies and focusing on finding and stopping hackers. Information security protects information to prevent unauthorized access, use, and disclosure. It includes implementing policies and procedures to protect information and prevent data loss or theft. However, implementing security measures can be challenging for ordinary employees, leading to decreased security.

Optimizing Student Information Management: A Holistic Examination of Implementation Strategies

This study explores the implementation of a Student Information and Accounting System (SIAS) at Benguet State University with the aim of enhancing academic efficiency. A thorough analysis evaluates the impact of SIAS on educational processes, records management, and transaction processing, focusing on assessing its effectiveness in improving administrative and academic operations, streamlining student information management, and enhancing overall academic performance. The findings underscore the importance of transitioning to such a system to boost efficiency, reduce errors, and improve student services. Recommendations include integrating SIAS with the university's website, implementing security measures, and conducting regular evaluations to align the system with evolving institutional needs. This abstract underscore the significance of adopting an online Student Information System for improved management and service delivery at Benguet State University.

Implementasi Nilai-Nilai Kebangsaan Berbasis UUD 1945 Guna Mendukung Sistem Cyber Security Dalam Penggunaan Sistem Informasi Akademik (SIA) Di Institusi Perguruan Tinggi

Cyber Security for Higher Education Academic Information System (AIS) data is an increasingly important issue along with the increasing dependence of higher education on information technology. Students have their education data in the Academic Information System (AIS) which is owned by each campus. Campuses are obliged to give their students the right to privacy and the right to information. The right to privacy and limitations on the right to information include state protection of personal data. In this way, students have the right to receive protection for their personal data in the academic information system (AIS). This is regulated by the 1945 Constitution (UUD 1945) and the Personal Data Protection Law (UU PDP), which are an important foundation for regulations on data management and cyber security systems. This research uses a journal literature review method which aims to investigate the security threats faced by Academic Information System (AIS) data and to design effective protection strategies. The methods used include risk analysis, identification of system vulnerabilities, and development of appropriate security policies. The findings show that sensitive data such as student personal information and academic records are the main targets for attackers. Therefore, the need to implement security measures such as data encryption, use of double authentication, strict system monitoring, as well as training for information system users is very important. Through a policy analysis approach and literature study, this research aims to identify strategies and best practices in integrating national values into cyber security policies and practices. It is hoped that this research will provide an overview of the importance of national principles in terms of cyber security in higher education. It will also provide recommendations for institutions to improve academic information systems in accordance with the country's constitution.

Habsburški mejni kordon proti Osmanskemu cesarstvu kot branik zdravja in njegove kazenskopravne osnove

In the 16th century, the Habsburgs started constructing a defensive buffer zone known as the Military Frontier to deter Ottoman incursions. The region was deliberately militarised and fell under full military administration along with its civilian population. As the Ottoman threat gradually diminished from the late 17th century onwards, the Military Frontier lost its defensive significance. However, due to the severe risk of infectious diseases, especially the plague, spreading from the southeast, the authorities made use of the military regime at the border to combat epidemics. They assigned the military cordon the task of a permanent sanitary cordon, enhancing the existing infrastructure and adapting the health regime to the strict hierarchical system of the military administration. In addition to the norms regulating the cordon’s structure and the implementation of security measures at the border, criminal regulations aimed at ensuring prevention and thereby protecting public health were also drawn up. This contribution presents the genesis of the cordon, analyses the relevant criminal code regulations from the 18th and 19th centuries, and evaluates their effect.

Design and Construction of Fan and Bulb Automation System

The automation system described in this project leverages the power of Bluetooth wireless communication to control the bulb and fan. The system utilize readily available components such as Arduino Uno, HC-05 Bluetooth module, and 4-channel relay module, it provides a cost-effective solution for creating a smart environment. The Arduino Uno acts as the central controller, receiving commands wirelessly via the Bluetooth module and subsequently controlling the power supply to connect devices through the relay module. This allows users to remotely turn on or off devices such as bulbs and fans, providing convenience and energy efficiency. The measured response times for turning on/off the fan and bulb in the automation system were found to be 15.47 μs for turning on the fan, 15.20 μs for turning off the fan, and 16.27 μs for turning on/off the bulb. These results indicate that the system is highly efficient and reliable in controlling the devices. Future enhancements can include the implementation of security measures to protect against unauthorized access and integration with other smart devices. The Bluetooth automation system showcases the potential for creating affordable and customizable automation solutions using easily accessible components

Candidate Authentication using OCR Techniques

This paper explores the application of Optical Character Recognition (OCR) techniques for authenticating candidates based on identity documents. As advancements in technology continue to redefine various industries, the integration of OCR into candidate authentication processes offers a streamlined and efficient solution. The aim is to design a system which gets the image of the identity proof and the details are being retrieved using the character segmentation which is done by a feature extraction optical character recognition algorithm (OCR). The authentication process encompasses document validation and implementation of security measures to safeguard against fraud.By combining YOLO object detection, Optical Character Recognition (OCR) and fuzzy matching technique ,this approach aims to enhance the accuracy and reliability of candidate authentication. This underscores the importance of a comprehensive authentication framework, incorporating both technological and procedural elements, to establish a robust and secure candidate authentication process in diverse contexts. Keywords: ID Verification,Optical Character Recognition (OCR), Text Extraction, YOLO,Object Detection,Image Segmentation, EasyOCR , Fuzzy Matching.

Analysis of Correctional Institution Officer Interaction Patterns and Stress During Implementation of Correctional Revitalization in Nusakambangan Indonesian

The implementation of correctional revitalization is not only related to structural adjustments and security protocols at Super Maximum Security (SMAX), Maximum Security (MAX), and Medium Security (MS) correctional facilities; it may also have an impact on changes in officer-inmate interaction patterns and the psychological stress officers. After the implementation of institutional revitalization, this study intends to investigate the patterns of officer-inmate interaction and the levels of stress experienced by correctional institution personnel in Nusakambangan with various degrees of security. Eight officers were interviewed as part of the research approach, which involved descriptive qualitative analysis, in four prisons on Nusakambangan Island. The analysis's findings give an example of the many ways that SMAX, MAX, and MS prison guards and convicts engage with one another. Because of restrictions on prisoners' freedom of movement, the implementation of security measures, and disparities in treatment within the development program, different interaction patterns result. Comparing convicts in XMAX and MAX prisons to those in MS prisons, authorities believe that MS prisoners spend more time outside of their rooms engaging in activities, which could compromise security and order. The result is that because inmates in MS prisons are free to roam the facility, but inmates in SMAX and MAX prisons engage in more activities in their quarters, the stress level for officers in MS prisons is higher than the stress level in SMAX and MAX prisons.

Defeating Denial of Service Backdoor Attacks

Denial of Service (DoS) backdoor attacks are a significant threat to computer systems. Backdoor attacks involve the installation of unauthorized software on a target system, which is then used to launch various attacks against other systems. In this paper, we explore several techniques that can be used to defeat DoS backdoor attacks, including access control, encryption, network monitoring, patch management, incident response planning, and employee education. Denial of Service (DoS) and Backdoor attacks are common types of cyber-attacks that can cause severe damage to computer systems and networks. DoS attacks aim to disrupt the availability of a system by overwhelming it with traffic or exploiting vulnerabilities, while Backdoor attacks provide attackers with unauthorized access to a system. This paper proposes a framework for defeating both types of attacks by implementing security measures at multiple levels, including the application layer, network layer, and host layer. The proposed framework involves techniques such as traffic filtering, load balancing, network segmentation, access control, and anomaly detection. At the application layer, the framework proposes implementing security measures such as input validation, error handling, and resource allocation to prevent DoS attacks. At the network layer, the framework recommends deploying firewalls, routers, and intrusion detection systems to filter traffic and detect anomalies. At the host layer, the framework proposes implementing security measures such as operating system hardening, patch management, and antivirus software to prevent Backdoor attacks. The proposed framework aims to provide a comprehensive approach to defeating DoS and Backdoor attacks by combining multiple security measures to provide a layered defense. By implementing the proposed framework, organizations can enhance the security of their computer systems and networks and prevent these types of attacks from causing damage.