Rule Configuration Research Articles

New sesquineolignan glycoside isomers from the aerial parts of Leonurus japonicus and their absolute configurations

The acid water-soluble fraction of the 95% EtOH extract of Leonurus japonicus exhibited significant anti-inflammatory activity by suppressing the p-ERK/ERK ratio and iNOS expression. A further phytochemical investigation of this acid water-soluble fraction led to the isolation of three previously undescribed sesquineolignan glycosides, leolignosides A–C (1–3), and their planar structures were elucidated using HR-ESI-MS and 1D and 2D NMR. To determine the complicated absolute configuration of sesquineolignan glycosides, coupling constants, NOESY data, empirical rules, enzymatic hydrolysis, and ECD data were comprehensively used. These isolated compounds were also screened for anti-inflammatory activity. Encouragingly, leolignosides A–C all suppressed LPS-induced NO overproduction in RAW 264.7 macrophages. The aglycone (3a) of leolignoside C was chosen for further investigation for its activity and the available amount. Similar to the acid water-soluble fraction, compound 3a inhibited NO overproduction and decreased the p-ERK/ERK and p-NF-κB/NF-κB ratios, indicating that 3a may exert anti-inflammatory effects through the ERK/NF-κB signaling pathway.

Authoritarian succession, rules, and conflicts: Tokayev’s gambit and Kazakhstan’s bloody January of 2022 (Qandy Qantar)

ABSTRACT The authoritarian succession literature indicates that non-democratic regimes install rules and institutions that supposedly ensure a smooth transition from ruler to a chosen successor. However, this scholarship cannot explain why some authoritarian successions happen as planned while others end in surprising violence. This article unpacks the puzzle of the transition of power from Nursultan Nazarbayev (1991–2019) to Kassym-Jomart Tokayev (2019–present) in autocratic Kazakhstan. Based on in-depth interviews with more than 15 informants and the analysis of media and secondary sources, we explore why and how the planned succession in Kazakhstan resulted in the bloodshed known as the 2022 Bloody January (Qandy Qantar). We demonstrate that two factors derailed the succession plan: the configuration of authoritarian rules and the presence of political ambitions of Tokayev. Our main argument is that the Nazarbayev succession plan led to elite disunity, escalating the political conflict into the violent 2022 Bloody January.

SecureFlow: Knowledge and data-driven ensemble for intrusion detection and dynamic rule configuration in software-defined IoT environment

There is a massive growth in the rate of heterogeneous devices configured in the Internet of Things (IoT) environment for efficient communication. The IoT devices are limited in resources, and there are no defined protocols in terms of security during communication in the IoT-based platforms. Several solutions are framed to make communication secure in the IoT ecosystem. However, the existing schemes need to be more reliable to handle the cyber threats and unwarranted incidents (such as intrusions, anomalies and attacks) coming from IoT endpoints owing to the unstructured patterns of IoT data and dynamic network conditions. Moreover, heavy cryptographic primitives have their deployment challenges due to the resource constraints of the IoT ecosystem. The dynamic nature of IoT traffic requires flexible and varied rules to handle the threats in different deployment scenarios. Therefore, a programmable interface enabled through Software-defined Networking (SDN) can handle heterogeneous threats and incidents in the IoT cyber world. Thus, in this paper, we have designed a novel framework, SecureFlow, an intrusion detection and dynamic rule configuration system based on the knowledge-based and data-driven ensemble. The proposed framework is robust and fault tolerant owing to dual-layer Intrusion Detection System (IDS) and rule configuration modules that can work without one of them. SecureFlow validated through several experiments performed through emulations in Mininet. The results depict that the proposed framework is effective and promising.

How do China's villages self‐organize collective land use under the background of rural revitalization? A multi‐case study in Zhejiang, Fujian and Guizhou provinces

AbstractAs land is one of the most important production factors in rural areas, it is becoming an important challenge for rural revitalization strategy to realize land value and promote the socioeconomic development of villages through the collective action of self‐organized collective land use. Various studies have discussed the relationships among land governance, rural revitalization and self‐organization, but few studies have focused on village cadres' roles and village rules. This paper aims to establish an analytical framework to explore these two operating mechanisms of self‐organization in collective land use. Two villages in eastern China and one village in western China are selected as typical cases to demonstrate how collective land use is self‐organized by village cadres' roles and village rules. The results indicate that village cadres play roles in the entrepreneurial spirit and resource access capacity to promote village economic and social development. In the utilization of collective land for farmers to live in peace and work happily, the configuration of rules at the village level to regulate the behaviour of villagers is a qualification on demand and “see who can take part”, cost sharing and benefit sharing, graduated sanction and supervision. The analysis in this article may provide an understanding of the mechanism driving the promotion and improvement of land self‐organization, which has important policy implications for China and other developing countries adopting this mode to promote regional economic development in rural areas.

A Security Enforcement Framework for SDN Controller Using Game Theoretic Approach

Software-defined networking (SDN) has gained significant attention as the future deployment platform for the Internet and enterprise networks. The major advantages of SDN include effective traffic management, dynamic configuration of policy and flow rules, and better scalability with heterogeneous traffic requirements. However, centralized network control and the use of OpenFlow protocols introduce various security challenges for the underlying network. The attacks on the SDN controller is critical as it hosts all network control functions. Motivated by a systematic analysis of different attack scenarios in SDN using the STRIDE attack model, this article presents an effective security enforcement framework for proactive prevention of potential attacks on SDN controllers. First, based on a signaling game approach, we design a trust-based controller attack detection (TCAD) model that calculates the trust value of each incoming packet to take necessary action. Next, we propose a risk-based attack prevention (RAP) model that detects and filters malicious traffic flows in the network. Finally, we evaluate our proposed security enforcement framework on different scenarios with varying traffic requirements and by injecting attacks based on the STRIDE model. Experimental results show 95% accuracy in the potential attack detection and prevention.

Understanding transaction platform governance and conflicts: A configuration approach

Transaction platforms, such as Amazon and Uber, are emerging as central players in many segments of the global economy. Extant research has primarily focused on the impact of network effects on platform growth and rivalry, but not much is known about governance configurations in platforms and their impact on outcomes that are crucial to the long-term survival of the platform. In this study, using information asymmetry logic and the organizational governance literature derived from theories of the firm, we propose that the configurations of platform governance rules impact conflicts between platforms and sellers. Our empirical research context is the online auction and mail-order industry. Following an inductive approach, we first identify the sets of rules that platforms use in seller selection and engagement. Next, utilizing fsQCA analysis, we identify multiple configurations of governance rules that lead to different levels of platform-seller conflicts. We also find that effective configurations of governance rules are contingent upon platform strategies. Theoretical and empirical implications of the study on platform governance are discussed.

РАЗРАБОТКА СИСТЕМЫ МОНИТОРИНГА БЕЗОПАСНОСТИ ИНФОРМАЦИОННОЙ СРЕДЫ ПРЕДПРИЯТИЯ

The relevance The urgency of ensuring information security (IS) is due to the increase in the volume of information circulating in local area networks (LAN) and the expansion of the range of tasks solved with the help of information systems (IS), there is a problem associated with an increase in the number of threats and increased vulnerability of information resources. This is due to the action of factors such as: expanding the range of tasks solved by the IP; increasing the complexity of information processing algorithms; increasing the volume of processed information; complication of LAN software and hardware components, and, accordingly, an increase in the likelihood of errors and vulnerabilities; increased aggressiveness of external data sources (global networks); the emergence of new types of threats. Traditional means of ensuring the information security of corporate networks include antiviruses, vulnerability detectors, firewalls and intrusion detectors. The functions of such information security systems are specific and solve individual tasks of ensuring the information security of a corporate network and, as a rule, can be overcome with the teamwork of a qualified group of violators. Such groups are aware of the capabilities of the security tools used in the corporate network, respond to updates of the vulnerability database of widely used software (software) published in special publications and the Internet, promptly exchange the results of probing the protection of the attacked corporate network and flexibly change the tactics of the invasion. Aim of research Implementation of Zabbix SCS monitoring system and pfsense firewall/ router with implementation of information traffic management. Research methods: — Zabbix programming environment; — pfsense programming environment. Results This article discusses the creation of the Zabbix monitoring system and the pfsence system at the AST LLC enterprise, Bashkortostan Republic, Sterlitamak. As a result of the configuration and creation of rules, as well as the implementation of SCS monitoring, the enterprise receives a system of protection against unauthorized access and a system of protection against corporate network attacks.

Transaction Platform Governance and Conflicts: A Configuration Approach

Transaction platforms, such as Amazon and Uber, are emerging as central players in many segments of the global economy. Extant research has primarily focused on the impact of network effects on platform growth and rivalry, and very little scholarly work has studied governance systems of platforms. Building on property rights theory, we propose that governance rules influence conflicts between platforms and seller partners. We test our proposition in the online auction and mail-order (SIC: 5961) industry. We first identify the sets of rules that platforms use in seller selection and engagement based on configuration theory. Next, utilizing fsQCA methodology, we identify multiple configurations of governance rules that lead to low platform-seller conflict. We also find that divergent configurations are contingent upon platform strategies. Theoretical and empirical implications of the study on platform governance and property rights are discussed.

A computational model of Ostrom's Institutional Analysis and Development framework

The Institutional Analysis and Development (IAD) framework developed by Elinor Ostrom and colleagues provides great conceptual clarity on the immensely varied topic of social interactions. In this work, we propose a computational model to examine the impact that any of the variables outlined in the IAD framework has on the resulting social interactions. Of particular interest are the rules adopted by a community of agents, as they are the variables most susceptible to change in the short term. To provide systematic descriptions of social interactions, we define the Action Situation Language (ASL) and provide a game engine capable of automatically generating formal game-theoretical models out of ASL descriptions. Then, by incorporating any agent decision-making models, the connection from a rule configuration description to the outcomes encouraged by it is complete. Overall, our model enables any community of agents to perform what-if analysis, where they can foresee and examine the impact that a set of regulations will have on the social interaction they are engaging in. Hence, they can decide whether their implementation is desirable.

A Fuzzing-Based Method for Testing Rules in Intrusion Detection Systems in 6G Networks

With the growing development of 6G techniques, it is more challenging than ever to tune rules in the signature-based network intrusion detection system (NIDS). Often, network managers have to tailor the ruleset manually. However, rules are prone to be problematic because of improper rule configuration. Moreover, the 6G network poses new challenges and requirements in rule verification. Hence, to evaluate the soundness of NIDS rules in 6G networks, we propose a new fuzzing solution for testing rules of signature-based NIDS. To achieve stateful fuzzing, we used a tunable responder to generate context-specific responses to construct network states. Moreover, fuzzing strategies (e.g., signature combination, repetition, and obfuscation) tailored for NIDS rules are designed to ensure effectiveness and rule coverage in fuzzing. With our prototype implementation, we find problems in the detecting rules of NIDSs, including conflicted rule, performance degradation, and NIDS bypassing, which seriously threaten the security of 6G networks.

AI Based Self Learning Intelligent Information Leak Protection System for TI Companies using LSTM

Preventing the leak of sensitive information, also popularly known as data leak or data loss to an unauthorized recipient, is the primary goal of an organization's information security system. A data leak can occur through multiple channels. While it may not always be possible to prevent it entirely, measures can be taken to minimize the possibility of the occurrence. Like all other financial institutions, TI companies collect sensitive personal information of their customers for business purposes. This information is often categorized into three primary types; NPI, PII, and PI are the designated types in the descending order of sensitivity. The detection of sensitive documents and redaction of sensitive information is required if it is needed to be shared. Inspection of such digital documents to find any sensitive information is by far a human-driven process, and thus time-consuming and costly. An and costly. An intelligent and robust system is required where the content is analysed by state-of-the-art data mining, statistical and machine learning techniques from various data dimensions. An AI based self-learning Intelligent Information Leak Protection System using LSTM is proposed in the project that mines and extracts information and categorizes the document images, to SD or NSD, based on the presence of NPI and PII semantic signatures without any explicit rule configuration. The system is designed to be used proactively as an early warning system to tag the SD images while resting in the data store. It can also act as a real-time checkpoint for the information loss by the documents in transit or use. The proposed model prescribes an information loss protection mechanism using a binary classifier based on the state-of-the-art LSTM technique within the paradigm of Artificial Intelligence.

Operation Anomaly Monitoring of Customer Service Data Analysis Platform Based on Improved FP-Growth Algorithm

Aiming at the problems of long time-consuming monitoring and poor monitoring accuracy in traditional customer service data analysis platform operation abnormality monitoring methods, a customer service data analysis platform operation abnormality monitoring method based on the improved FP-Growth algorithm is designed. Obtain customer service data sets, classify data types, filter customer behavior, identify the operating status of the data analysis platform, improve the FP-Growth algorithm to build a rule configuration model, set the platform safety factor threshold, and keep the reconstruction error of customer service data to a minimum Within the scope, optimize the abnormal monitoring mode. The experimental results show that the average recovery time of the proposed customer service data analysis platform operation anomaly monitoring method is 5.239s, and the average platform operation anomaly accuracy rate is 97.3%, indicating that the customer service data analysis platform integrated with the improved FP-Growth algorithm operates abnormally The monitoring method performs better.

SECURING DATA NETWORK FOR GROWING BUSINESS VPN ARCHITECTURES CELLULAR NETWORK CONNECTIVITY

Private networks allow organizations to leverage, customize and dedicate LTE network capabilities to their own service needs. These networks are controlled and managed locally and can be optimized for specific network services and applications. Often they are used because there may be gaps in coverage where there is no cellular connectivity. Enterprises and businesses running on light application systems, using small bandwidth and requiring fast deployment such as ATMs, vending machines, digital signage kiosks and small store branches We propose a method and concept and implementation, in the form of a Data Network Security Configuration For Business Growth VPN architecture Mobile Network Connectivity for various businesses. By implementing products and services by developing the configuration and model of VPN Tunneling Protocol rules, using the EOIP Protocol and SSTP Protocol methods and virtualization schemes using the VLAN Bridging method on Wide Area Network (WAN) network connectivity. This method takes advantage of the LTE features found on the RB751U-2hnd and then integrates Huawei Mobile Broadband LTE. We also present a general Service Level Agreement (SLA) and an open source tools-based SLA network system Zabbix. Then configure the VPN Tunneling Protocol and its features on the RB751U-2hnd using Paramiko Network Automation. The focus of the results of this research is that by utilizing the available tools we will build a VPN with system monitoring facilities with the results achieved are network performance and availability and show that the design we build can be used for private connections for growing businesses. the results obtained from testing for 1 month for the 3 providers used, for the average throughput value with VPN tunneling applied TSEL 14.2 Mbps, ISAT 13.9 Mbps and XL 13.5 Mbps. The SLA value obtained is based on acceptable criteria, TSEL 91.5%, ISAT 91.8% and XL 90.6%.

AI Based Self – Learning Intelligent Information Leak Protection System for Insurance Companies

Preventing the leak of sensitive information, also popularly known as data leak or data loss to an unauthorized recipient, is the primary goal of an organization's information security system. Like all other financial institutions, Insurance companies collect sensitive personal information of their customers for business purposes. This information is often categorized into three primary types; Nonpublic Personal Information (NPI), Personally Identifiable Information (PII), and Personal Information (PI) are the designated types in the descending order of sensitivity. While it may not always be possible to prevent it entirely, measures can be taken to minimize the possibility of the occurrence. An intelligent and robust system is required where the content is analyzed by state-of-the-art data mining, statistical and machine learning techniques from various data dimensions. An Artificial Intelligence (AI) based self-learning Intelligent Information Leak Protection System (IILPS) using Long Short Term Memory (LSTM) is proposed in the project that mines and extracts information and categorizes the document images, to Secured Documents (SD) or Non-secured documents(NSD), based on the presence of NPI and PII semantic signatures without any explicit rule configuration. The system is designed to be used proactively as an early warning system to tag the SD images while resting in the data store. It can also act as a real-time checkpoint for the information loss by the documents in transit or use. The proposed model prescribes an information loss protection mechanism using a binary classifier based on the state-of-the-art LSTM technique within the paradigm of Artificial Intelligence.

Securing Data Network For Growing Business VPN architectures Cellular Network Connectivity

Private networks allow organizations to leverage, customize and dedicate LTE network capabilities to their own service needs. These networks are controlled and managed locally and can be optimized for specific network services and applications. Often they are used because there may be gaps in coverage where there is no cellular connectivity. Enterprises and businesses running on light application systems, using small bandwidth and requiring fast deployment such as ATMs, vending machines, digital signage kiosks and small store branches We propose a method and concept and implementation, in the form of a Data Network Security Configuration For Business Growth VPN architecture Mobile Network Connectivity for various businesses. By implementing products and services by developing the configuration and model of VPN Tunneling Protocol rules, using the EOIP Protocol and SSTP Protocol methods and virtualization schemes using the VLAN Bridging method on Wide Area Network (WAN) network connectivity. This method takes advantage of the LTE features found on the RB751U-2hnd and then integrates Huawei Mobile Broadband LTE. We also present a general Service Level Agreement (SLA) and an open source tools-based SLA network system Zabbix. Then configure the VPN Tunneling Protocol and its features on the RB751U-2hnd using Paramiko Network Automation. The focus of the results of this research is that by utilizing the available tools we will build a VPN with system monitoring facilities with the results achieved are network performance and availability and show that the design we build can be used for private connections for growing businesses. the results obtained from testing for 1 month for the 3 providers used, for the average throughput value with VPN tunneling applied TSEL 14.2 Mbps, ISAT 13.9 Mbps and XL 13.5 Mbps. The SLA value obtained is based on acceptable criteria, TSEL 91.5%, ISAT 91.8% and XL 90.6%.

Interpretability of deep neural networks used for the diagnosis of Alzheimer's disease

AbstractAlzheimer's disease (AD) is a chronic brain disorder and is the most common cause of dementia. Patients suffering from AD experience memory loss, confusion, and other cognitive and behavioral complications. As the disease progresses, these symptoms become severe enough to interfere with the patient's daily life. Since AD is an irreversible disease and existing treatments can only slow down its progress, early diagnosis of AD is a key moment in fighting this disease. In this article, we propose a novel approach for diagnosing AD via deep neural networks from magnetic resonance imaging images. Additionally, we propose three new propagation rules for the layer‐wise relevance propagation (LRP) method, which is a method used for visualizing evidence in deep neural networks to obtain a better understanding of the network's behavior. We also propose various rule configurations for the LRP to achieve better interpretability of the network. Our proposed classification method achieves a 92% accuracy when classifying AD versus healthy controls, which is comparable to state‐of‐the‐art approaches and could potentially aid doctors in AD diagnosis and reduce the occurrence of human error. Our proposed visualization approaches also show improvements in evidence visualization, which helps the spread of computer‐aided diagnosis in the medical domain by eliminating the “black‐box” nature of the neural networks.

Facilitating Meta-Theory Reasoning (Invited Paper)

Structural proof theory is praised for being a symbolic approach to reasoning and proofs, in which one can define schemas for reasoning steps and manipulate proofs as a mathematical structure. For this to be possible, proof systems must be designed as a set of rules such that proofs using those rules are correct by construction. Therefore, one must consider all ways these rules can interact and prove that they satisfy certain properties which makes them "well-behaved". This is called the meta-theory of a proof system. Meta-theory proofs typically involve many cases on structures with lots of symbols. The majority of cases are usually quite similar, and when a proof fails, it might be because of a sub-case on a very specific configuration of rules. Developing these proofs by hand is tedious and error-prone, and their combinatorial nature suggests they could be automated. There are various approaches on how to automate, either partially or completely, meta-theory proofs. In this paper, I will present some techniques that I have been involved in for facilitating meta-theory reasoning.

Rules and the Ruled: Understanding Joint Patterns of Institutional Design and Behavior in Complex Governing Arrangements

Institutions are the rules of the game that guide policy behavior. Yet, the policy and administration literature on institutions and institutional design have been developed largely in parallel to policy behavior and network literatures. Drawing insights from both, we apply the Institutional Grammar Tool along with survey data to assess how addressing collective action problems result in variations in institutional designs and how rules influence behavior. Our case is the New York City Watersheds, where New York City, local governments, the State, and the Federal government agreed to secure access to high‐quality, unfiltered drinking water for New York City. Despite a long history of conflict, the parties devised a complex governing arrangement that created credible commitments while providing for a variety of public goods. Results show that rule configurations differ depending on whether they create credible commitments or provide public goods, and that credible commitment rules guide collaboration patterns among stakeholders.

POTENTIAL DISGUISING ATTACK VECTORS ON SECURITY OPERATION CENTERS AND SIEM SYSTEMS

In this article we highlight several potential vectors of attacks that can be carried out on a monitoring capacities powered by SOC SIEM using its common features and misconfigurations. Widely spread problems like excessive amounts of false positive alerts or not absolutely accurate configuration of the correlation rules may lead to situation where an attacker is able to trigger an undesired state of the monitoring system. We’ve find three potential vectors for evasion the SIEM powered SOCs monitoring. The first vector grounds on mechanisms used to collect event data – log collectors: the malfunctioning SIEM state can be achieved with generating and submitting the bogus event data to the processing party like SIEM. Fake data flow may cause generation of mistaken alerts which can confuse the analytics stuff. The second vector employs some of the attacker’s knowledge about actual SIEM configuration – exploitation of correlation rule flaws. Taking into account the fact that correlation rules are mostly hand-written, they are prone to some logic flaws – certain detection rules may not be triggered by all of the malicious attack indicators. An attacker with knowledge about that feature may fulfill the unrecorded conditions and trick the SIEM to treat the attack flow as benign activity. The last researched vector is based on redundantly sensitive detection rules which produce a lot of false positive alarms but are not removed. An attacker may trigger the malfunctioning alarm continuously to distract the analytics stuff and perform its actions under the cover of noise. Those discussed vectors are derived from analysis of the actual SIEM installations and SOC processes used as best practices. We have no actual indicators that those attacks are carried out “in wild” at the moment of issuing of this article, but it is highly probable that those tactics may be used in the future. The purpose of this research is to highlight the possible risks for the security operation centers connected with actual processes and practices used in industry and to develop the remediation strategy in perspective.

The continuity of social times in China

Our article presents the result of a common two-year research project about social times in China. It uses French sociology theory and a case-study approach. A shoe and a steel factory including semi-structured interviews with workers (n: 25) are the basis of our analysis. Our results show the importance of the discontinuity of social times in Chinese manufacturing industries. They indicate specific configurations of rules in those industries associating subsistence time, continuity time and a projected future.