Security Enforcement Research Articles

The value of international standards certification: Evidence on export and firm performance from a security enforcement on borders

Globally, economic growth is promoted through regional economic integration, and governments actively try to improve the security and efficiency of international trade and border management by optimizing the conditions for international trade. The World Trade Organization (WTO) introduced the Authorized Economic Operator (AEO) certification system to improve the efficiency of customs clearance and to ensure the security of the global supply chain. Using a sample of Taiwanese-listed firms that attained AEO certification from 2009 to 2018, this study investigates the influence of AEO certification on their export sales and corporate performance. The empirical results show that AEO-certified firms increase their ratio of export sales and the scope of export destination regions. However, AEO-certified firms can not improve their operating and market performance significantly, except for low-performance firms in the accreditation year. This study also uses the two-stage least square regressions (2SLS) method and the propensity score matching method to conduct the regression analysis. The regression results are consistent with the main findings. The findings can be used for reference by firms seeking to expand international trade and by governments to promote the country’s international trading.

Comparative analysis of criminal law enforcement of military security in Ukraine, Georgia and the Republic of Poland

Comparative analysis of criminal law enforcement of military security in Ukraine, Georgia and the Republic of Poland

Partisan bias in securities enforcement

Abstract In this article, I present a partisan theory of agency enforcement and empirically investigate the possibility of partisan bias in the enforcement of the federal securities laws. Leveraging plausibly exogenous shocks to partisan control of the U.S. Securities and Exchange Commission (SEC), I find evidence that a firm’s partisan alignment with the SEC substantially reduces the likelihood of enforcement. For a firm that is ex ante equally likely to be targeted for enforcement or not, my estimates indicate that a typical increase in partisan alignment following a change in party control of the SEC reduces the likelihood of enforcement by over 19%. Partisan alignment also appears to reduce aggregate monetary sanctions, though these estimates are less certain. By contrast, I find little evidence of partisan bias in the initial opening of investigations. These findings suggest there may be meaningful partisan bias in SEC enforcement and have important implications for democratic governance. (JEL D73, K22, K23, K41, K42, P00)

The Effect of the Federal Judicial System on Public Enforcement: Evidence from SEC Enforcement Actions

The Effect of the Federal Judicial System on Public Enforcement: Evidence from SEC Enforcement Actions

Cross-border regulatory cooperation and cash holdings: Evidence from US-listed foreign firms

We investigate the effect of cross-border regulatory cooperation on the cash holdings of firms cross-listed on US stock exchanges. The staggered adoption of the Multilateral Memorandum of Understanding (MMoU) facilitates cooperation among securities regulators around the world and expands their enforcement capabilities against foreign firms. Using a difference-in-differences design, we find that US-listed foreign firms significantly reduce their cash holdings after their home countries sign the MMoU, suggesting that the threat of increased SEC enforcement induced by regulatory cooperation disciplines corporate insiders from stockpiling cash holdings to enjoy private benefits of control. Information opacity and the cost of capital are two of the channels through which the threat of increased SEC enforcement affects cash holdings. The reduction in cash holdings is more pronounced for foreign firms from countries with weaker governance. After the MMoU, foreign firms make faster cash adjustments toward target levels, they reduce cash by making payouts, and their cash is valued more by investors.

Advanced Helmet and License Plate Recognition: A Breakthrough in Safety and Surveillance Systems

Abstract: Helmet detection and license plate recognition have emerged as crucial components in ensuring safety and security in various domains, particularly in transportation and law enforcement. This review paper presents a comprehensive survey and analysis of methodologies, techniques, and advancements in the field of helmet detection and license plate recognition using computer vision and deep learning approaches. The paper investigates diverse datasets, algorithms, and architectures utilized for accurate detection and recognition of helmets worn by individuals in critical settings and the identification of license plates on vehicles. Furthermore, it explores the challenges, limitations, and future directions within these domains. The paper also evaluates the performance metrics, technological advancements, and potential applications in real-world scenarios, emphasizing the importance of these technologies in enhancing safety measures, traffic regulation, and security enforcement systems. Through this review, we aim to provide a consolidated overview and critical analysis of existing methodologies while identifying opportunities for future research and development in helmet detection and license plate recognition.

Exhaustive distributed intrusion detection system for UAVs attacks detection and security enforcement (E-DIDS)

Distributed intrusion detection systems (DIDS) are a specialized subset of conventional IDSs designed for implementation in distributed environments. Each IDS is integrated into distinct entities within a monitored network, potentially distributed across various locations. These participating IDSs can be configured to detect either a particular or multiple attack types. Although DIDS has found extensive application in diverse IoT systems, its utilization in unmanned aerial vehicles (UAVs) still needs to be explored. Consequently, it is imperative to devise a comprehensive framework tailored explicitly for UAVs. It combines multiple detection units to enhance security. Based on the insights gained from previous studies, we propose an exhaustive DIDS for UAVs security enforcement in this paper. Our proposed solution offers a robust and scalable security approach. Through distributing the workload across interconnected IDSs deployed on the UAV, our solution was optimized for UAVs attacks detection to achieve high detection performance while reducing the complexity. To the best of our insight, there is no recorded DIDS for UAVs security, and attack detection has been proposed and evaluated. Furthermore, our paper provides a detailed analysis, outlining the development basis and the achieved results. We performed multiple experiments over different cases using different datasets. The achieved experimental results demonstrate that the proposed IDS has significantly high accuracy detection and low loss rates. Our proposed E-DIDS efficiently detects multiple attacks on different UAVs subsets with good global accuracy that reached 98.6% and low resource consumption.

A model transformation based security policy automatic management framework for software-defined networking

Software-defined networking (SDN) has been utilized to enforce the security of traditional networks. However, the existing SDN-based security enforcement mechanisms rely heavily on the security policies containing the underlying information of data plane, such as MAC address, IP address or switch ports. These security policies need to be specifically developed by network operators and loaded into the control plane manually. With increasing the scale of underlying network, the existing security policy management mechanisms confront more and more challenges. The security policy transformation for SDN networks is to research how to transform the high-level security policy without containing the underlying information into the practical flow entries used by Openflow switches automatically, thereby implementing the automatic management of security policies. To achieve this objective, we propose a model transformation based security policy automatic management framework for software-defined networking in this paper. Leveraging its functional modules, the framework can solve the problems of how to find a connected path for each access control rule of security policy model (SPM) in data plane, how to transform the connected path into the system model of flow entries, as well as how to generate the practical flow entries according to the system model of flow entries. In order to validate the effectiveness and performance of framework, we implement the framework by leveraging POX controller and Mininet emulator. The experimental results illustrate the framework can transform SPM into practical flow entries, synchronously perceive the modifications caused by cutting down one connected path or changing SPM, and continuously keep the data plane holding the security properties defined by SPM at runtime.

The Importance of Institutional Arrangement to Safeguard Maritime Security and Safety in Indonesia: The Case of Marine Security and Safety Agency

Introduction: Multiple agencies approach has been adopted by the Indonesian government to ensure maritime security and safety. The involvement of multiple actors potentially leads to overlapping authorities and an uncoordinated operation. For stakeholders at sea, such approach is inefficient, ineffective and creates confusion. The situation needs to be addressed to make more a coordinated, efficient, and effective method. This paper tries to address such an issue through institutional law lens.Purposes of the Research: This paper aims to answer three important questions (1) How is the enforcement of security and safety at sea so far? (2) What are the implications as the many institutions involved in the enforcement of safety and security at sea? and (3) What kind of institutional arrangements to be made in the future so that law enforcement at sea becomes effective and efficient?.Methods of the Research: This study utilizes a normative legal approach. It exercises legal doctrines, relevant laws and regulations. It is also suplemented by relevant literatures, books, journals, and media coverages.Results of the Research: This paper finds that the enforcement of security and safety at sea adopts multi agencies where many institutions involved in maintaining security and safety at sea. The fact that multiple institutions simultaniously participate in law enforcement at sea, potentially create overlapping and lack of coordination among them. This also creates confusion and disadvantage to the stakeholders. Therefore, it is necessary to reorganize the current institutional arrangements to minimize the problems on the ground.

The Importance of Institutional Arrangement to Safeguard Maritime Security and Safety in Indonesia: The Case of Marine Security and Safety Agency

Introduction: Multiple agencies approach has been adopted by the Indonesian government to ensure maritime security and safety. The involvement of multiple actors potentially leads to overlapping authorities and an uncoordinated operation. For stakeholders at sea, such approach is inefficient, ineffective and creates confusion. The situation needs to be addressed to make more a coordinated, efficient, and effective method. This paper tries to address such an issue through institutional law lens.Purposes of the Research: This paper aims to answer three important questions (1) How is the enforcement of security and safety at sea so far? (2) What are the implications as the many institutions involved in the enforcement of safety and security at sea? and (3) What kind of institutional arrangements to be made in the future so that law enforcement at sea becomes effective and efficient?.Methods of the Research: This study utilizes a normative legal approach. It exercises legal doctrines, relevant laws and regulations. It is also suplemented by relevant literatures, books, journals, and media coverages.Results of the Research: This paper finds that the enforcement of security and safety at sea adopts multi agencies where many institutions involved in maintaining security and safety at sea. The fact that multiple institutions simultaniously participate in law enforcement at sea, potentially create overlapping and lack of coordination among them. This also creates confusion and disadvantage to the stakeholders. Therefore, it is necessary to reorganize the current institutional arrangements to minimize the problems on the ground.

Cloud Security Using Fine-Grained Efficient Information Flow Tracking

This study provides a comprehensive review and comparative analysis of existing Information Flow Tracking (IFT) tools which underscores the imperative for mitigating data leakage in complex cloud systems. Traditional methods impose significant overhead on Cloud Service Providers (CSPs) and management activities, prompting the exploration of alternatives such as IFT. By augmenting consumer data subsets with security tags and deploying a network of monitors, IFT facilitates the detection and prevention of data leaks among cloud tenants. The research here has focused on preventing misuse, such as the exfiltration and/or extrusion of sensitive data in the cloud as well as the role of anonymization. The CloudMonitor framework was envisioned and developed to study and design mechanisms for transparent and efficient IFT (eIFT). The framework enables the experimentation, analysis, and validation of innovative methods for providing greater control to cloud service consumers (CSCs) over their data. Moreover, eIFT enables enhanced visibility to assess data conveyances by third-party services toward avoiding security risks (e.g., data exfiltration). Our implementation and validation of the framework uses both a centralized and dynamic IFT approach to achieve these goals. We measured the balance between dynamism and granularity of the data being tracked versus efficiency. To establish a security and performance baseline for better defense in depth, this work focuses primarily on unique Dynamic IFT tracking capabilities using e.g., Infrastructure as a Service (IaaS). Consumers and service providers can negotiate specific security enforcement standards using our framework. Thus, this study orchestrates and assesses, using a series of real-world experiments, how distinct monitoring capabilities combine to provide a comparatively higher level of security. Input/output performance was evaluated for execution time and resource utilization using several experiments. The results show that the performance is unaffected by the magnitude of the input/output data that is tracked. In other words, as the volume of data increases, we notice that the execution time grows linearly. However, this increase occurs at a rate that is notably slower than what would be anticipated in a strictly proportional relationship. The system achieves an average CPU and memory consumption overhead profile of 8% and 37% while completing less than one second for all of the validation test runs. The results establish a performance efficiency baseline for a better measure and understanding of the cost of preserving confidentiality, integrity, and availability (CIA) for cloud Consumers and Providers (C&P). Consumers can scrutinize the benefits (i.e., security) and tradeoffs (memory usage, bandwidth, CPU usage, and throughput) and the cost of ensuring CIA can be established, monitored, and controlled. This work provides the primary use-cases, formula for enforcing the rules of data isolation, data tracking policy framework, and the basis for managing confidential data flow and data leak prevention using the CloudMonitor framework.

A novel and efficient framework for in-vehicle security enforcement

The Internet of Vehicles (IoV) has garnered significant popularity thanks to rapid technological advancements and the widespread availability of the Internet. IoV encompasses vehicles with multiple electronic control units (ECUs) interconnected via advanced intra-vehicle networks. These networks play a crucial role in managing various vehicle functions, with the Controller Area Network (CAN) being of particular significance. However, the increased adoption of intelligent vehicles has also led to a rise in cyberattacks in the intra-vehicular network. These existing vulnerabilities pose significant security and user safety challenges. Extensive efforts have been dedicated to enhancing intra-vehicular network security. Yet, there are open concerns with limited progress made by academic and industry experts on effectively detecting CAN bus attacks. These concerns are essential to ensure intelligent vehicles’ overall security and safety. It requires collaboration between academia, industry, and the development of innovative solutions to fortify vehicular networks against evolving cyber threats. Current intra-vehicular security systems need more robustness and need to consider intelligent methodologies in their proposed works. To overcome all these limitations, This paper introduces a novel intrusion detection framework for in-vehicle networks based on integrating federated learning with transfer learning, improving the detection capabilities of individual vehicles within the network. Our framework employs a trusted authority for secure communication, a cloud server for model distribution and aggregation, and leverages the CAN bus for data collection and training. It guarantees that vehicles start with standardized baseline models and refine them through transfer learning, resulting in a collective intelligence-based final IDS engine for ongoing improvement. We used the Maximum Mean Discrepancy (MMD) to select data from the source domain similar to the target domain. The selected data is more likely to contain patterns and features useful for detecting intrusions in the target domain, leading to better generalization and detection capabilities.

Juridical Study of Intellectual Property Rights (IPR) for Plant Variety Protection (PVP) in Improving Agribusiness Competitiveness to Support the Sustainability of Indonesia's Economic Development

Purpose: This research aims to examine IPR protection for plant varieties through the regulation of the Patent Law and the PVP Law and formulate an appropriate legal regime to increase agribusiness competitiveness to support sustainable economic development. This research provides solutions for breeders and inventors as well as the government for the weaknesses of the Patent Law and PVP Law in addition to formulating the right IPR legal regime.
 
 Design/methodology/data analysis: The research method uses normative juridical. Researchers analyzed through secondary data, comparative law methods, and historical juridical IPR theories. Researchers construct the formulation of an appropriate IPR regime system.
 
 Findings: Researchers formulate that the PVP Law is an IPR regime system that is more appropriate than the Patent Law to improve agribusiness competitiveness. The PVP Law can accommodate the characteristics of plant varieties, the value of traditional knowledge, and farmer exceptions. However, a new legal regime system related to traditional knowledge that can accommodate special matters is needed.
 
 Originality/value: No one has examined the improvement of the IPR regime system and the establishment of a new legal regime related to "traditional knowledge" to improve agribusiness competitiveness to support sustainable economic development.
 
 Practical implications: The government needs to reassess the PVP Law, among others: (1) need to accommodate protection arrangements related to "processes," (2) affirmation of the time limit for the protection of traditional knowledge, (3) enforcement of security and environmental sustainability, (4) authority to grant compulsory licenses. In addition, it is necessary to develop new types of laws related to the protection of traditional knowledge. This research can be applied in other countries that have the same characteristics as Indonesia.

Fraudulent financial reporting and the consequences for employees

We combine U.S. Census data with SEC enforcement actions to examine employees' outcomes, such as wages and turnover, before, during, and after periods of fraudulent financial reporting. We find that fraud firms’ employees lose about 50% of cumulative annual wages, compared to a matched sample, and the separation rate is much higher after fraud periods. Yet, employment growth at fraud firms is positive during fraud periods; these firms overbuild and hire new, lower-paid employees concurrent with the fraud, unlike firms in distress which tend to contract. When the fraud is revealed, firms shed workers, unwinding this abnormal growth and resulting in most of the negative wage consequences. Wage outcomes are particularly unfavorable in thin labor markets, and lower-wage employees, though unlikely to have perpetrated the fraud, experience more severe wage losses compared to higher-wage employees.

Registration as Consent: Patching Jarkesy's Hole in SEC Enforcement

Registration as Consent: Patching Jarkesy's Hole in SEC Enforcement

Spillover Effects of Financial Reporting Fraud on Peers: An SEC Enforcement Perspective

Spillover Effects of Financial Reporting Fraud on Peers: An SEC Enforcement Perspective

Do SEC Enforcement Actions Deter Real Earnings Manipulations of Industry Peers?

Do SEC Enforcement Actions Deter Real Earnings Manipulations of Industry Peers?

The Effect of the Federal Judicial System on Public Enforcement: Evidence from SEC Enforcement Actions

The Effect of the Federal Judicial System on Public Enforcement: Evidence from SEC Enforcement Actions

Scalable Security Enforcement for Cyber Physical Systems

Scalable Security Enforcement for Cyber Physical Systems

Machine Learning based Network Packet Classification

Network packet classification plays an important role in modern networks irrespective of host or network-based classification, serving as the foundation for efficient routing, malicious activity detection, and security enforcement. With the continuous growth of network traffic volume and complexity, traditional static rule-based classification methods have faced difficulties in scalability and adaptability. As a solution, the study decided to enforce machine learning techniques to tackle these challenges effectively. This study presents an extensive and original review of machine learning- based approaches for network packet classification. The smart Intrusion Detection System framework with network packet classification evolution looks forward to designing and deploying security systems that use various parameters for analysing current and dynamic traffic trends and are highly time-efficient in predicting intrusions. Various machine learning algorithms commonly employed in packet classification, such as decision trees, support vector machines, and neural networks are analysed and their merits and demerits are compared with their behaviour and accuracy percentage in this study. machine learning-based techniques offer an efficient and accurate network packet classification for the protection of the systems when compared to the conventional methods of packet classification. By leveraging the power of machine learning algorithms and intelligent feature selection, network administrators and Security Operation Center (SOC) analyst can enhance network performance, improve security, and the robustness of the log generated in the network.