Operational Disruptions Research Articles

The Economic Advantages of Mediation: Saving Costs and Time for Pakistani Businesses

Mediation is an increasingly recognized method of alternative dispute resolution (ADR) that offers significant economic benefits for businesses in Pakistan. The research explores how mediation can address the pressing challenges of high litigation costs, prolonged case durations, and strained business relationships, which are common in the traditional court system. The purpose of the study is to evaluate mediation’s potential to save costs and time while fostering sustainable dispute resolution practices in the Pakistani business environment. The study employs an analytical approach, drawing on economic data, case studies, and comparative insights to examine the cost-effectiveness and efficiency of mediation. Key findings indicate that mediation reduces legal fees, administrative expenses, and operational disruptions by resolving disputes swiftly and amicably. Additionally, mediation helps preserve business relationships and protects reputations due to its confidential and non-adversarial nature. Despite these advantages, the study identifies barriers to mediation adoption in Pakistan, such as cultural resistance, lack of awareness, and inadequate legal frameworks. It concludes with recommendations to promote mediation, including legislative reforms, awareness campaigns, and the establishment of more mediation centres. Overall, the research underscores the economic advantages of mediation as a viable alternative to litigation for businesses in Pakistan.

Enhancing Organizational Productivity through Effective Occupational Health and Safety Practices: Insights from the Beverage Manufacturing Unit of Abdul Monem Ltd.

The main objective of this research was to identify the critical role of Occupational Health and Safety (OHS) practices in driving organizational productivity, with focusing on Abdul Monem Ltd. Beverage Unit (AMLBU), a Coca-Cola manufacturing plant in Cumilla,, Bangladesh. Established in 1986, the plant operates across four production lines, producing a range of carbonated and non-carbonated beverages. With a workforce of over 800 employees, including permanent, daily, and sales staff, the facility faces unique OHS challenges common to beverage manufacturing, such as handling hazardous materials, operating heavy machinery, and managing ergonomic risks. The research methodology integrates both qualitative and quantitative approaches combining random and judgmental sampling techniques to analyze 100 observations, representing both diverse and critical incidents. Data were collected through direct observation, surveys, and reviews of internal documents, such as safety reports and manuals, supplemented by relevant industry publications.Findings reveal a strong correlation between effective OHS practices and enhanced productivity. Key measures, such as ergonomic interventions, compliance with safety protocols, and the use of Personal Protective Equipment (PPE), significantly reduce workplace accidents and absenteeism while fostering employee efficiency and well-being. Key findings reveal effective OHS programs—such as ergonomic interventions, rigorous safety protocols, and the provision of Personal Protective Equipment (PPE)—significantly enhance productivity by reducing workplace accidents, absenteeism, and operational disruptions. This research also finds out a strong correlation between effective OHS measures and enhanced organizational performance. Employees working in environments that prioritize safety and health demonstrate higher morale, reduced absenteeism, and increased productivity. Conversely, unsafe practices and inadequate safety protocols often lead to accidents, injuries, and diminished workforce efficiency. This research contributes to the growing discourse on the economic and social benefits of OHS programs, offering valuable insights for industry leaders, policymakers, and academics. It advocates for a paradigm shift where workplace safety is viewed not merely as a regulatory requirement but as a strategic investment in organizational success.This study serves as a reference for future research and decision-making in the beverage manufacturing sector and beyond. DOI: https://doi.org/10.52783/pst.1459

Cybersecurity Challenges in the Electric Vehicle Industry: A Technical Overview

The rapid evolution of electric vehicle (EV) technology and charging infrastructure has introduced unprecedented cybersecurity challenges across the automotive sector. This article examines the current state of cybersecurity in the electrification industry, focusing on the challenges faced by EV manufacturers and charging infrastructure operators. The article reveals significant vulnerabilities in charging station security, ranging from hardware security modules to communication protocols and payment systems. The article explores the regulatory landscape, implementation challenges, and the impact on various stakeholders including manufacturers, suppliers, and end users. The article highlights the critical need for standardized security frameworks, improved incident response capabilities, and strategic investments in cybersecurity infrastructure. The article demonstrates that organizations implementing comprehensive security measures achieve substantial improvements in threat detection and incident prevention, while those lacking robust security frameworks face increased risks of breaches and operational disruptions.

Resilient Leadership in Crisis: Strategies for Navigating Economic and Operational Disruptions in a Volatile World

Resilient Leadership in Crisis: Strategies for Navigating Economic and Operational Disruptions in a Volatile World

A Novel Approach to Enhancing Active Directory Security in Academic Institutions

This research rigorously investigates the cybersecurity frameworks within academic institutions, emphasizing the pivotal role and security of Active Directory (AD) systems. By conducting an in-depth analysis of AD infrastructures across 12 universities with critical digital environments, this study scrutinizes access control mechanisms, user identity management, and network segmentation strategies. The findings reveal profound security lapses, such as excessive administrative privileges granted to 75% of non-administrative users and the absence of Demilitarized Zones (DMZs) in 80% of the institutions. Additionally, 65% of the institutions exhibited critical vulnerabilities by not integrating public devices, such as printers and laboratory computers, into the AD framework. The study further highlights the escalating threat of cryptolocker and ransomware attacks, which have increasingly targeted institutions, resulting in significant data encryption and operational disruptions. Moreover, challenges related to the deployment and management of advanced cybersecurity solutions, like CrowdStrike, underscore the complexities in maintaining up-to-date defenses. These issues are compounded by frequent update and upgrade failures, adversely impacting AD health and overall network security. This paper delineates strategic recommendations to enhance AD security, supported by empirical evidence showing a 92% improvement in defense against cyber attacks upon implementing these measures. The insights garnered from this study are aimed at fortifying the cybersecurity postures of academic institutions, thereby mitigating the escalating threats in the digital landscape.

July effect in hospitalized cirrhosis patients: A US nationwide study using difference-in-differences analysis.

The July effect in US teaching hospitals has been studied with conflicting results. We aimed to evaluate the effect of physician turnover in July on the clinical outcomes of patients hospitalized with cirrhosis. We utilized the Nationwide Inpatient Sample database (2016-2019) to identify patients hospitalized with cirrhosis and liver-related complications (variceal bleeding, hepatorenal syndrome, acute-on-chronic liver failure). We used difference-in-differences analysis to compare teaching and non-teaching hospital differences in mortality and length of stay (LOS) in May and July, and trends in outcomes in other months before and after July. We included 78,371 hospitalizations in teaching and 23,518 in non-teaching hospitals in May and July. Teaching hospital admissions had overall higher complication rates and mortality compared to non-teaching hospitals. We did not find a difference in mortality between teaching and non-teaching hospitals in all cirrhotic patients (adjusted odds ratio 1.01, 95%CI [0.88-1.16]) or in those with severe complications (0.87, [0.72-1.06]). There was greater LOS in July vs. May in teaching hospitals relative to non-teaching hospitals for all patients with cirrhosis (adjusted rate ratio 1.03, 95%CI [1.02-1.05]) and for those with severe complications (1.19, [1.17-1.21]). The months after July were associated with longer LOS in teaching hospitals, with the effect gradually diminishing over the subsequent months. Our study suggests trainee turnover in July did not affect mortality, but lengthened hospital stays for patients with cirrhosis, highlighting the need for effective supervision of new trainees and strategies to mitigate operational disruptions for improved clinical management.

Risk Management of Information Security in Inaportnet Using ISO/IEC 27005:2018

This study aims to analyse information security risks in the Inaportnet system at the Port Authority Class II Tanjung Buton using the ISO/IEC 27005:2018 standard. The system is a digital innovation designed to expedite port services but faces significant challenges in information security. The first step involved identifying assets within the Inaportnet system, followed by recognizing potential threats and vulnerabilities associated with these assets. This process is crucial as it lays the groundwork for understanding where risks may arise. The research employs the Failure Mode and Effects Analysis (FMEA) method to identify, assess, and prioritise risks based on assets, threats, vulnerabilities, and existing controls. A total of 17 risks were identified, categorized from "very low" to "low" priority levels. The highest risk involves operational disruption due to sudden power outages, with an RPN score of 72. This study proposes risk mitigation recommendations, including Systems connected to the internet that are vulnerable to cyberattacks, such as hacking or malware, which can result in data theft or service disruptions. Therefore, it is essential to implement firewalls and intrusion detection systems to safeguard the network against external threats. The findings provide practical guidance for improving the information security and operational reliability of the Inaportnet system. By implementing these mitigations, the Port Authority is expected to enhance the reliability of port services and protect critical information.

Berth Allocation and Quay Crane Assignment Considering the Uncertain Maintenance Requirements

The strategic optimization of a container terminal’s quayside assets, including the berth and quay cranes, is crucial for maximizing their deployment and utilization. The interrelated and complex challenges of Berth Allocation (BAP) and Quay Crane Scheduling (QCSP) are fundamental to enhancing the resilience of container ports, as berths and quay cranes constitute essential infrastructure. Efficient berth allocation and quay crane scheduling can mitigate operational disruptions, even in the face of maintenance or failures, thereby improving both operational reliability and resilience. However, previous studies have often overlooked the uncertainty associated with quay crane maintenance when planning these operations. This paper aims to minimize vessel turnaround time by accounting for the uncertain in quay crane maintenance activities. To address this novel problem, we propose a proactive-reactive method that incorporates a reliability-based model into the Swarm Optimization with Differential Evolution (SWO-DE) algorithm. Computational results confirm the practical relevance and effectiveness of our proposed solution methods for container terminals.

Systematic Risk Analysis and Mitigation Strategies for Near-Miss Events in Interventional Operating Room Nursing.

The aim of this study is to examine the characteristics of intraoperative nursing near-miss events in interventional operating rooms, systematically identify and analyze associated risks, and propose effective mitigation strategies. A retrospective study was conducted using a specially designed survey focused on nursing near-miss events in Interventional operating rooms. Records of intraoperative near-miss events voluntarily reported by medical and nursing staff between January 2023 and March 2024 were analyzed. Grey relational analysis was used to evaluate and identify the associated risk factors. A total of 81 near-miss events were reported, with the majority (50%) occurring after 8 PM. These events were categorized into 6 main types: medication errors (60.49%), issues with consumables (16.05%), tubing-related incidents (8.64%), specimen handling errors (7.4%), transfer handover issues (4.93%), and patient transport problems (2.46%). Grey relational analysis identified air embolism formation during pressurized fluid administration as the highest risk event (ξ1 = 0.369). The risk factors were ranked as follows: weak coordination ability and lack of responsibility among nurses > operational interruptions > inadequate professional capability > poor communication between medical staff and nurses > equipment malfunction > frequent emergency surgeries and a fast paced working environment. Medication administration errors are frequently encountered, with air embolisms during pressurized fluid infusion representing the most significant risk. Operational interruptions are major contributors to these errors, often influenced by the coordination skills and professional competencies of nurses. Clinically, it is crucial to enhance the identification and management of near-miss events to reduce the incidence of adverse outcomes during surgical procedures.

Improving High Availability Services Using KVM Full Virtualization

In the digital era, critical sectors like banking, ISPs, and cloud-based services depend on robust, secure networks to ensure uninterrupted service availability. Key operations such as financial transactions, customer support, and online services rely on high system uptime, essential for business continuity and customer trust. Network and server administrators play a vital role in maintaining high availability (HA) for systems like file servers, web servers, database servers, backup systems, and enterprise applications. HA infrastructures often use multiple servers working collaboratively to ensure continuous service. If one server fails, another takes over seamlessly, preventing significant disruptions. Virtualization technology enhances HA systems by hosting services on virtual machines (VMs) that can migrate between physical nodes within a cluster. This ensures uninterrupted service even during hardware failures. This paper examines the use of open-source tools for managing HA services, focusing on cost efficiency and system optimization. The Heartbeat program facilitates real-time VM migration across cluster nodes, maintaining service continuity. To improve this process, the Perf+ algorithm is introduced. Perf+ optimizes CPU performance, reduces memory usage, and minimizes downtime by transferring fewer bytes of modified memory pages during migration, reducing CPU and network load. The proposed solution is implemented in an experimental HA system to evaluate the performance of real-time VM migrations. The research analyzes the impact of these migrations on system efficiency, aiming to advance HA infrastructures in virtualized environments through optimized resource utilization and reduced operational interruptions.

Antimicrobial resistance on Ships: A Narrative review.

Antimicrobial resistance (AMR) poses a global health challenge, particularly in maritime environments where unique conditions foster its emergence and spread. Characterized by confined spaces, high population density, and extensive global mobility, ships create a setting ripe for the development and dissemination of resistant pathogens. This review aims to analyse the contributing factors, epidemiological challenges, mitigation strategies specific to AMR on ships and to propose future research directions, bridging a significant gap in the literature. Maritime environments facilitate the propagation of AMR through interconnected factors. The confined and communal nature of ships allows resistant pathogens to spread rapidly among diverse populations. The inappropriate and often empirical use of broad-spectrum antibiotics, compounded by limited access to diagnostic tools, accelerates the selection of resistant strains. Poor infection control practices, including inadequate sanitation and ineffective isolation measures, exacerbate the risks. Environmental contamination through untreated wastewater and ballast water discharge introduces resistant bacteria and genes into marine ecosystems, posing additional public health and ecological threats. Surveillance of AMR on ships is hampered by logistical barriers, including the lack of standardized protocols and reporting systems. Documented outbreaks of resistant pathogens, such as methicillin-resistant Staphylococcus aureus, underline the public health risks associated with AMR in maritime settings, alongside significant operational disruptions and environmental impacts. Despite advancements in portable diagnostic technologies, their adoption on ships remains minimal, leaving critical gaps in detection and management. Effective management of AMR in maritime settings necessitates the integration of stewardship programs tailored to shipboard environments, reinforced infection control measures, and advanced wastewater treatment.

Protecting small businesses from social engineering attacks in the digital era

In the digital era, small businesses are increasingly targeted by social engineering attacks, which exploit human vulnerabilities to gain unauthorized access to sensitive information. Tactics such as phishing, baiting, and pretexting are particularly effective against smaller enterprises due to their limited resources and often inadequate cybersecurity measures. Phishing, for example, deceives employees into revealing credentials through fraudulent emails, while baiting entices victims with promises of rewards, and pretexting manipulates individuals into divulging critical data under false pretenses. These attacks not only compromise sensitive information but also lead to significant financial losses, reputational damage, and operational disruption. The role of IT security frameworks is critical in mitigating social engineering risks for small businesses. Frameworks like the National Institute of Standards and Technology (NIST) Cybersecurity Framework and ISO 27001 provide structured approaches to identifying, assessing, and managing security risks. However, the cost and complexity of implementing such frameworks can be prohibitive for small enterprises. To address these challenges, businesses can adopt cost-effective strategies such as employee training, multi-factor authentication (MFA), and endpoint protection tools. Regularly updating software, conducting simulated phishing exercises, and leveraging cloud-based security solutions further bolster defenses without significant financial burdens. By prioritizing cybersecurity awareness and leveraging affordable solutions, small businesses can enhance their resilience against social engineering attacks. This paper explores the vulnerabilities of small enterprises, evaluates the effectiveness of IT security frameworks, and outlines pragmatic strategies tailored to their unique constraints. Ensuring robust defenses against these pervasive threats is essential for safeguarding the digital future of small businesses.

Identifying risks for effective maintenance of renewable energy plants in the new green Capital City of Indonesia

This study explores the use of Failure Mode and Effects Analysis (FMEA) to identify and mitigate risks in Solar Power Plants (PLTS) in Indonesia’s new National Capital City (IKN). As renewable energy is vital to Indonesia’s sustainability goals, managing risks in PLTS is essential for ensuring reliable and efficient energy production. The research identifies key challenges such as PV Array module failures, shading effects, and control system disruptions, which significantly impact electricity generation. Using the Risk Priority Number (RPN) methodology, the study ranks PV Array modules as the highest risk component (RPN 192), followed by Control and Management Systems (RPN 140) and PV Circuit Breakers and Video Monitoring Systems (RPN 120). These findings underline the need for targeted mitigation strategies. Recommendations include regular PV module inspections, hotspot monitoring technology, firmware updates, and enhanced fire protection systems. Preventive measures like grounding current maintenance and fire sensor upgrades further minimize operational disruptions, ensuring component durability and system efficiency. By leveraging the FMEA framework, this study systematically identifies and prioritizes risks while providing actionable solutions to enhance operational resilience. The results align with Indonesia’s vision of achieving 80 % renewable energy utilization in IKN by 2045. This research offers broader applicability for renewable energy systems in similar contexts, contributing to clean energy initiatives, reducing fossil fuel dependency, and supporting sustainable urban planning. It serves as a critical resource for integrating renewable energy into Indonesia’s green and resilient capital city vision

Supplier Relationship Management in the Digital Age: Enhancing Supply Chain Resilience Through Blockchain Technology

Digital transformation has driven companies to enhance supply chain efficiency and resilience through innovative technologies. However, challenges such as a lack of transparency, low efficiency, and risks of operational disruptions remain significant barriers in Supplier Relationship Management (SRM). This study aims to explore the role of blockchain technology in improving transparency, trust, and supply chain resilience. Using a qualitative case study approach, data were collected through in-depth interviews with supply chain managers from three companies in Indonesia and supporting document analysis. The findings reveal that blockchain implementation enhances operational efficiency by up to 35%, reducing the average process cycle time from 10 days to 6 days. Additionally, transparency levels increased by 40%, reflected in the ability of supply chain participants to monitor information flow in real time. Trust among stakeholders also improved by 25%, attributed to encrypted data security and immutable transaction records. These findings confirm that blockchain strengthens supply chain resilience through three main dimensions: response, recovery, and adaptation. The contribution of this research lies in enriching the literature on the role of blockchain in SRM, particularly in the context of supply chain resilience. Furthermore, the results provide practical guidance for companies in designing blockchain implementation strategies to mitigate operational risks and enhance stakeholder collaboration

Carbon performance and corporate financial performance during crises: Evidence from the COVID‐19 pandemic and the Global Financial Crisis

AbstractEconomic crises offer a valuable perspective on the relationship between carbon performance and corporate financial performance (CFP). Crises test corporate performance and challenge the allegedly universal synergies that developed under “normal” circumstances. However, the current research in this area is limited and has often yielded insignificant results. Drawing from a global dataset spanning 15 years, we investigate the moderating influences of two distinct crises on the interplay between carbon emission intensity and CFP. Employing fixed‐effects regression analysis, we dissect these critical periods, offering nuanced insights into their distinct impacts. The contrasting nature of the Global Financial Crisis (GFC) and the COVID‐19 pandemic is central to this study. The COVID‐19 pandemic, characterized by operational disruptions and natural resource scarcities (supply‐side shock), significantly amplified the benefits of carbon reduction strategies, highlighting the value of efficient processes and cost efficiencies. In contrast, the GFC did not exhibit a significant impact on the carbon–CFP relationship. This differentiation is attributed to the GFC's finance‐driven nature (demand‐side shock), and the resultant evolution in stakeholder preferences and organizational structures. This study extends beyond the debated territory of environmental, social, and governance (ESG) scores. It also provides a nuanced understanding of carbon performance's role subject to the unique characteristics of a crisis.

Investor Protection and Corporate Responsibility in Multinational Holding Bankruptcy: The Virtue Dragon Indonesia and China Case

This research examines the implications of holding company bankruptcy on subsidiaries in Indonesia, focusing on capital market law and investor protection. The bankruptcy of a holding company can lead to operational disruptions for its subsidiaries, affecting market confidence and devaluing investments. Investors in these subsidiaries may face risks of unfair treatment during the bankruptcy process. The study explores Indonesia's legal framework, including the bankruptcy law and regulations enforced by the Otoritas Jasa Keuangan (OJK), which are aimed at protecting investors' rights and ensuring transparency. The case of Virtue Dragon Indonesia, a subsidiary of a Chinese holding company, highlights the complexities of cross-border bankruptcy and the challenges of safeguarding minority investors. The research underscores the need for stronger legal protections for investors and suggests improvements in Indonesia’s capital market laws to enhance investor confidence and market integrity.

Cyberattacks, Operational Disruption, and Investment in Resilience Measures

With the increased frequency and magnitude of cyberattacks, policymakers and the private sector search for ways to counter this threat. One of the main initiatives suggested to achieve this goal is sharing cybersecurity-related information. Although the general belief is that information sharing can increase both industry profit and social welfare, it is unclear whether firms would voluntarily share such information. In this paper, we examine the incentives of firms to share cybersecurity-related information, how information sharing impacts investments in cyber resilience, and the aggregate impact on welfare. We find that firms only voluntarily share information in less competitive markets when the impact of the disruption is high. In all other cases, firms elect not to share information, despite potential welfare benefits. To facilitate information sharing, we investigate an exclusionary policy (i.e., sharing must be mutual) and demonstrate market conditions under which this policy incentivizes information sharing. However, when competition is intense, even the exclusionary policy is ineffective because it reduces industry profit. To inform stronger interventions, we examine firms being mandated to disclose their private cyberthreat information. We demonstrate that an opportunity does exist for such disclosure, particularly when the cost of investing in cyber resilience is high. However, policymakers must use caution with such a policy because applying this intervention when investment costs are not high leads to a steep reduction in welfare. This paper was accepted by D. J. Wu, information systems. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.00430 .

Critical factors affecting digital resilience in the Sri Lankan banking sector: a systematic review

Digitalizing the Sri Lankan banking sector has heightened its vulnerability to cyber threats and operational disruptions, making digital resilience critical for maintaining continuity. This systematic literature review (SLR) employs the Technology-Organization-Environment (TOE) framework to explore and categorize the factors affecting digital resilience in the Sri Lankan Banking Sector. A review of 45 studies from 2016 to 2024 highlights three major categories: technological factors (e.g., cybersecurity measures, IT infrastructure, and emerging technologies like AI and blockchain), organizational factors (e.g., leadership, employee training, and strategic planning), and environmental factors (e.g., regulatory compliance, customer trust, and vendor management). The findings suggest that a holistic and integrated approach is necessary for enhancing digital resilience, specifically balancing technological innovation, organizational agility, and compliance with regulatory requirements. Moreover, the review identifies significant gaps in the current literature regarding Sri Lanka-specific empirical studies, recommending future research into the application of emerging technologies such as AI and blockchain to enhance resilience in developing economies. Additionally, the study proposes strategies to improve digital resilience, including strengthening cybersecurity infrastructure, fostering leadership-driven digital initiatives, updating business continuity plans, and advocating for flexible regulatory frameworks that balance innovation with security. The study underscores the need for an integrated approach to digital resilience. It suggests that future research should focus on targeted interventions to enhance resilience within the Sri Lankan banking sector and broader financial domain.

Development of algorithms for early detection of cyberattacks on networks using machine learning

Critical infrastructure protection and national security are enhanced by the security and reliability of networks. Various types of information circulate on these networks, ranging in classification from open to closed. The consequences of cyberattacks on these networks can be severe, including reputational damage, financial loss, operational disruption and data leakage. Traditional security methods, such as firewalls and anti-virus software, are becoming less effective against modern and ever-changing cyber threats. As a result, powerful network intrusion detection systems (IDS) have become indispensable for proactive detection and mitigation of cyber attacks. Machine learning has become a viable method for creating adaptive intrusion detection tools that can detect new and complex attack patterns. By learning from huge labelled network traffic datasets, ML models can understand the subtle patterns and differentiating features of normal and abnormal or malicious traffic flows. This allows them to detect possible cyber threats and intrusions that traditional signature-based IDSs cannot detect. Extracting discriminative features and training appropriate classification models from such data is a challenging task. In the presented study, we analyse the effectiveness of ML algorithms for detecting cyberattacks, in particular distributed denial of service (DDoS) attacks, in network traffic data. In the presented study, a network attack detection system is developed using ML and deep learning (DL) models and experimented on the CICIDS2017 dataset. The main objectives of the study are to develop a strategy for extracting valuable information from raw network streams; to study the impact of data preparation on the false positive rate; and to conduct a comparative analysis of ML models for cyberattack detection. The main goal of the study is to provide an understanding of the development of a reliable adaptive network intrusion detection system using ML approaches that increase cybersecurity capabilities and protect against future cyberattacks.

Detect Insider Threat with Associated Session Graph

Insider threats pose significant risks to organizational security, often leading to severe data breaches and operational disruptions. While foundational, traditional detection methods suffer from limitations such as labor-intensive rule creation, lack of scalability, and vulnerability to evasion by sophisticated attackers. Recent advancements in graph-based approaches have shown promise by leveraging behavior analysis for threat detection. However, existing methods frequently oversimplify session behaviors and fail to extract fine-grained features, which are critical for identifying subtle malicious activities. In this paper, we propose a novel approach that integrates session graphs to capture multi-level fine-grained behavioral features. First, seven heuristic rules are defined to transform user activities across different hosts and sessions into an associated session graph while extracting features at both the activity and session levels. Furthermore, to highlight critical nodes in the associated session graph, we introduce a graph node elimination technique to normalize the graph. Finally, a graph convolutional network is employed to extract features from the normalized graph and generate behavior detection results. Extensive experiments on the CERT insider threat dataset demonstrate the superiority of our approach, achieving an accuracy of 99% and an F1-score of 99%, significantly outperforming state-of-the-art models. The ASG method also reduces false positive rates and enhances the detection of subtle malicious behaviors, addressing key limitations of existing graph-based methods. These findings highlight the potential of ASG for real-world applications such as enterprise network monitoring and anomaly detection, and suggest avenues for future research into adaptive learning mechanisms and real-time detection capabilities.