User Agent Research Articles

Analyzing and comparing the security of self-sovereign identity management systems through threat modeling

The concept of Self-Sovereign Identity (SSI) promises to strengthen the security and user-centricity of identity management. Since any secure online service relies on secure identity management, we comparatively analyze the intrinsic security of SSI. Thus, we adopt a hybrid threat modeling approach comprising STRIDE, attack trees, and ratings towards this unique context. Data flow diagrams of the isolated, centralized and the SSI model serve as the foundation for the assessment. The evolution of the paradigms shows an increasing complexity in security zones and communication paths between the components. We identified 35 threats to all SSI components and 15 protection measures that reduce the threats’ criticality. As a result, our research shows that the SSI paradigm’s threat surface is significantly higher compared to the traditional models. Besides the threat assessment on model level, the adapted methodology can evaluate a specific implementation. We analyzed uPort with a restricted scope to its user agent. Thus, 2 out of 10 threats were not properly addressed, leading to potential spoofing, denial, or repudiation of identity actions.

Software Agents in the Practice of Law

The paper is devoted to the analysis of such legal technology as the use of software agents in law practice. The paper substantiates the relevance of the research topic, gives the concept of a software agent, and provides their classification. Special attention is paid to intelligent software agents, their types used in the implementation of legal activities. The paper provides examples of intelligent software agents-bots used in legal practice; user agents, predictive agents and data search agents that select and analyze information in large data warehouses in order to systematize it, classify and identify trends in individual indicators. It is concluded that the use of software agents in legal practice can be considered as a kind of legal technology in the event that it entails a legally significant result and certain legal consequences. A software agent acts as one of the structural elements of legal technology — a means of carrying out legal activity, a means of achieving a legal result, and the very use of this tool in legal practice is a legal technology. This technology is assigned to certain types depending on various classification criteria. Based on the materials of domestic and foreign legal practice, the author provides specific examples of harm caused by activities carried out with the help of software agents. The author outlines the problems of using software agents in law practice that require further examination.

An Investigative Approach to identify Loop Hole and Data Leak vulnerabilities in a World’s Largest Greenfield VoLTE Mobile Network Operator JIO through Network Packet Dissection and Penetration

Apply Digital forensic technique to diagnose the cyber security threat to the Advanced IP based Mobile telephony Networks. Indian Telecom Market having a huge subscriber base. The Market adapts the new technology Long Term Evolution (LTE), VoLTE, IP Multimedia Subsystem. Hacking the Mobile Network is a major threat to the telecom ecosystem impacts the subscribers and operator seriously. The paper illustrates investigation on the strategy to Dissect and penetrate into JIO’s Network to identify the loop holes and data leaks in the Network pipe. Using the loop holes, the network may subject to attacks, hijacked, data stealth. We have followed out a grey hat technique to an extent, to continue the process and explore network nodal entry gaps to access the systems and flood anonymous traffic in to the network. We may require the Network operator’s permission to dig deeper, investigate further provide an authenticated report. The research and the reports of this paper is carried out by thorough understanding of user limits privileges provided by the network to a normal subscriber. It’s very clear the access is open to every subscriber and not crossed the network boundary limits. The investigation results bring an alarm to telecom network and its stake holders. With recent advancements like 5G, WSN, IOT, IoE, SDN-NFV the stakeholders keep increasing and expanding brings industrial convergence. With this aspect the stakeholder’s groups of telecom imbibes a huge impact across the industries and normal life. This research approach is just a sample on JIO’s Network, this can be carried out on all the Networks in the Indian Market or global Market to ensure no gaps in their network security. The idea is to sniff packets in the network through user agents like Mobile phones and do a deep packet investigation along with the understanding of network interfaces and protocols. Identify the gaps in the Core networks interfaces and the entities in the Network through call flow scenarios PS Mobile originating/ Terminating call and SMS tracing. The paper describes the initial phase of Dissecting and penetrating the Network. By having the operators Permission, we may drill down deeper investigation in this approach and analyses the crucial impacts, damage and Collateral damages to their Network. KEYWORDS Cyber Security, Digital Forensic, Mobile Network security, Network Penetration, Packet sniffing, Deep Packet Inspection.

Using U.S. Business Registry Data to Corroborate Corporate Identity: Case Study of the Legal Entity Identifier

This paper offers a fresh perspective on fundamental issues in using official incorporation records to corroborate the identity of corporate entities by comparing two publicly-available sets of information, namely, business registry incorporation records and reference data from the Legal Entity Identifier (LEI) system, with some focus on the monitoring function performed by LEI issuers as agents for LEI data users. Three modes of analysis are used to consider these issues, high-level analysis of LEI system data about U.S. entities with LEIs, interviews conducted with U.S. business registries, and entity-level comparisons of business registry and LEI records for entities with LEIs incorporated in the states of Ohio and Massachusetts. The fresh perspective provided here includes attention to key comparison issues such as truncation of Legal Names in official records; significant state-level variation in requirements to provide business address information in incorporation records or periodic reports; recognition that some key business register data may not be readily available or available only at a cost; whether in this context enhancements can be made to the expectations for, and disclosures by, LEI issuers in their monitoring role; and to what extent the high incidence of non-renewal of LEIs might play a role in the quality of LEI reference data. The paper develops measures of scope and degree for many key issues that can arise in using business registry information within an identity-corroboration context. The exceptional transparency of the LEI system allows for detailed comparisons that connect its data quality and value proposition with its sources and methods.

An Autonomous Multi-Agent Framework using Quality of Service to prevent Service Level Agreement Violations in Cloud Environment

Cloud is a specialized computing technology accommodating several million users to provide seamless services via the internet. The extension of this reverenced technology is growing abruptly with the increase in the number of users. One of the major issues with the cloud is that it receives a huge volume of workloads requesting resources to complete their executions. While executing these workloads, the cloud suffers from the issue of service level agreement (SLA) violations which impacts the performance and reputation of the cloud. Therefore, there is a requirement for an effective design that supports faster and optimal execution of workloads without any violation of SLA. To fill this gap, this article proposes an automatic multi-agent framework that ensures the minimization of the SLA violation rate in workload execution. The proposed framework includes seven major agents such as user agent, system agent, negotiator agent, coordinator agent, monitoring agent, arbitrator agent and the history agent. All these agents work cooperatively to enable the effective execution of workloads irrespective of their dynamic nature. With effective execution of workloads, the proposed model also resulted in an advantage of minimized energy consumption in data centres. The inclusion of a history agent within the framework enabled the model to predict future requirements based on the records of resource utilization. The proposed model followed the Poisson distribution to generate random numbers that are further used for evaluation purposes. The simulations of the model proved that model is more reliable in reducing SLA violations compared to the existing works. The proposed method resulted in an average SLA violation rate of 55.71% for 1200 workloads and resulted in an average energy consumption of 47.84kWh for 1500 workloads.

Hierarchical Multi-Agent Deep Reinforcement Learning for Energy-Efficient Hybrid Computation Offloading

Mobile edge computing (MEC) provides an economical way for the resource-constrained edge users to offload computational workload to MEC servers co-located with the access point (AP). In this article, we consider a hybrid computation offloading scheme that allows edge users to offload workloads by using active RF communications and backscatter communications. We aim to maximize the overall energy efficiency subject to the completion of all workload by jointly optimizing the AP's beamforming and the users' offloading decisions. Considering a dynamic environment, we propose a hierarchical multi-agent deep reinforcement learning (H-MADRL) framework to solve this problem. The high-level agent resides in the AP and optimizes the beamforming strategy, while the low-level user agents learn and adapt individuals' offloading strategies. To further improve the learning efficiency, we propose a novel optimization-driven learning algorithm that allows the AP to estimate the low-level users' actions by solving approximate optimization problem efficiently. Then, the action estimation can be shared with all users and drive them to update individuals' actions independently. Simulation results reveal that our algorithm can improve the system performance by 50%. The learning efficiency and reliability are also improved significantly comparing to the model-free learning methods.

Optimizing Energy Management in Microgrids Based on Different Load Types in Smart Buildings

This paper presents an energy management strategy (EMS) based on the Stackelberg game theory for the microgrid community. Three agents or layers are considered in the proposed framework. The microgrid cluster (MGC) refers to the agent that coordinates the interactions between the microgrids and the utility grid. The microgrid agent manages the energy scheduling of its own consumers. The third agent represents the consumers inside the microgrids. The game equilibrium point is solved between different layers and each layer will benefit the most. First, an algorithm performs demand response in each microgrid according to load models in smart buildings and determines the load consumption for each consumer. Then, each microgrid determines its selling price to the consumers and the amount of energy required to purchase from the utility grid to achieve the maximum profit. Finally, the balance point will be obtained between microgrids by the microgrid cluster agent. Moreover, the proposed method uses various load types at different times based on real-life models. The result shows that considering these different load models with demand response increased the profit of the user agent by an average of 22%. The demand response is implemented by the time of use (TOU) model and real-time pricing (RTP) in the microgrid.

An anonymity-preserving mobile user authentication protocol for global roaming services

In this article, we put forward an anonymity-preserving mobile user authentication protocol for global roaming services. It deals with Mutual Authentication and Key Agreement (MAKA) between Mobile User (MU) and Foreign Agent (FA). It replaces the traditional Elliptic Curve Computational Diffie–Hellman Problem (ECCDHP) by Elliptic Curve Factorization Problem (ECFP) to reduce the computational overhead of the authentication system while keeping the system security unaltered. It can preserve user anonymity and conditional privacy using session-wise unique anonymous identities of MU, where each anonymous identity hides the real identity in such a way so that only Home Agent (HA) can recompute it. Besides, our protocol also concentrates on the anonymity and conditional privacy of FA. It also provides a simplified revocation strategy which minimizes the revocation and re-registration costs of the authentication system. We validate the security features through informal and formal security analysis. Finally, we present a performance comparison, and discuss the efficiency of the proposed protocol.

Energy Pricing and Management for the Integrated Energy Service Provider: A Stochastic Stackelberg Game Approach

As a retailer between the energy suppliers and end users, the integrated energy service provider (IESP) can effectively coordinate the energy supply end and the energy use end by setting energy prices and energy management. Because most of the current research focuses on the pricing of electricity retailers, there are few studies on IESP energy pricing and management, which are still at the initial stage. At the same time, the existing research often does not consider the impact of demand response (DR) and uncertainties, such as natural gas and electricity wholesale prices, on the pricing of IESP. It is necessary to model the DR and uncertainties in the integrated energy system. Aiming at the inadequacy of the existing research and to address the energy pricing and management of IESP, this paper develops a two-stage stochastic hierarchical framework, which comprehensively considers the DR strategy of the user end, characteristics of the electricity/gas/heat storage and the uncertainties of electricity and gas wholesale prices. The proposed hierarchical model for energy pricing and management is a two-layer model: the upper layer is the problem of maximizing the benefits of IESP, and the lower layer is the problem of minimizing the energy cost of user agents. Through the complementary transformation, the linearization method and the strong duality principle in the optimization theory, the model is transformed into a mixed-integer linear programing (MILP) problem, which can be easily solved by the off-shelf commercial solver. Finally, the simulation results are provided to demonstrate the interactive operation between the IESP and user agent through energy prices setting, DR strategy and energy management.

Data-Driven Threat Analysis for Ensuring Security in Cloud Enabled Systems.

Cloud computing offers many benefits including business flexibility, scalability and cost savings but despite these benefits, there exist threats that require adequate attention for secure service delivery. Threats in a cloud-based system need to be considered from a holistic perspective that accounts for data, application, infrastructure and service, which can pose potential risks. Data certainly plays a critical role within the whole ecosystem and organisations should take account of and protect data from any potential threats. Due to the variation of data types, status, and location, understanding the potential security concerns in cloud-based infrastructures is more complex than in a traditional system. The existing threat modeling approaches lack the ability to analyse and prioritise data-related threats. The main contribution of the paper is a novel data-driven threat analysis (d-TM) approach for the cloud-based systems. The main motivation of d-TM is the integration of data from three levels of abstractions, i.e., management, control, and business and three phases, i.e., storage, process and transmittance, within each level. The d-TM provides a systematic flow of attack surface analysis from the user agent to the cloud service provider based on the threat layers in cloud computing. Finally, a cloud-based use case scenario was used to demonstrate the applicability of the proposed approach. The result shows that d-TM revealed four critical threats out of the seven threats based on the identified assets. The threats targeted management and business data in general, while targeting data in process and transit more specifically.

An integrated model for optimal water resources allocation under “3 Redlines” water policy of the upper Hanjiang river basin

Study regionThe upper Hanjiang river basin, which is a crucial water source for the middle route of the South-to-North Water Diversion Project in China. Study focusThis paper attempts to present a new integrated model for optimal water resources allocation in a typical river basin. In this model, the complex adaptive system framework is applied at the macro level, and the system dynamics simulates the inner response of water user agent at the micro level and China’s “3 Redlines” water policy is considered in the optimal module. New hydrological insights for the regionAn integrated model is proposed for optimal water resources allocation of the upper Hanjiang river basin in the planning years 2025 and 2030, and the NSGA-II algorithm is executed to obtain the Pareto optimal solution set for the optimal allocation of water resources. The results show that there is a significant competitive relationship between environmental objective and economic objective. Water resources tend to flow to industrial water user agent driven by the adaptation to economic objective, to agricultural water user agent and municipal water user agent driven by the adaptation to environmental objective, which can be used for decision-making and provide guidance for further development of water resources allocation.

Relational Agents for the Homeless with Tuberculosis Experience: Providing Social Support Through Human–agent Relationships

In human–computer interaction (HCI) research, relational agents (RAs) are increasingly used to improve social support for vulnerable groups including people exposed to stigmas, alienation, and isolation. However, technical support for tuberculosis (TB) patients, one such vulnerable group, remains insufficient due to the nature of the infectious disease and difficulties in accessing the homeless community. To derive design considerations for developing RAs targeting homeless TB patients, we conducted an empirical study on the patients. Data were collected through participatory observations and interviews and were processed using deductive thematic analysis. The patients’ environmental and behavioral characteristics were classified, which showed that understanding these factors in the design of an RA is important because the patients’ perception, attitudes, and expectations towards the agent are shaped by (and also shape) their environmental and behavioral characteristics, which consequently affect the nature of relationships formed between them. Therefore, we drew the following design considerations: (1) protection of privacy is a prerequisite to the use of an RA for homeless TB patients and can be addressed from both short-term (technical) and long-term (sociotechnical) perspectives; (2) the homeless group emphasized affective support from the agent, suggesting that relationships per se are already valuable to people who have been socially isolated and stigmatized; (3) consideration of the past memories in selecting social cues can facilitate the exchange of affective expressions in user–agent interaction; and (4) an RA should clarify to its interlocuters its identity as a machine to avoid confusing people with low technological literacy.

Fuzzy multi-agent assistance system for elderly care based on user engagement

An intelligent system intended to provide assistance to an elderly user in their home is presented. This multi-agent system is designed to monitor the interaction between the user and other agents, and regulate itself based on the estimated level of mental engagement of the user in order to contribute to help them maintain adequate levels of awareness and interaction with their home environment. The agents are devices programmed to preserve the security, comfort and health of the user, who is also conceptualized as one of the agents in the system. The user engagement level is estimated through fuzzy inference, that is, by means of a set of fuzzy membership functions and rules applied over the interaction of the user with different categories of agents. The system incorporates several dozen agents and is tested using two different datasets of real interactions between users and devices in their home environments. A uniform parameter configuration yielded very positive results for diverse scenarios and subsequently the parameters of the system were automatically optimized to show that improved performance, in the form of a more efficient system regulation and increased certainty in adjustment estimation, is obtained.

Cognitive Radio Resource Scheduling using Multi-Agent Q-Learning for LTE

In this paper, we propose, implement, and test two novel downlink LTE scheduling algorithms. The implementation and testing of these algorithms were in Matlab, and they are based on the use of Reinforcement Learning (RL), more specifically, the Q-learning technique for scheduling two types of users. The first algorithm is called a Collaborative scheduling algorithm, and the second algorithm is called a Competitive scheduling algorithm. The first type of the scheduled users is the Primary Users (PUs), and they are the licensed subscribers that pay for their service. The second type of the scheduled users is the Secondary Users (SUs), and they could be un-licensed subscribers that don't pay for their service, device-to-device communications, or sensors. Each user whether it’s a primary or secondary is considered as an agent. In the Collaborative scheduling algorithm, the primary user agents will collaborate in order to make a joint scheduling decision about allocating the resource blocks to each one of them, then the secondary user agents will compete among themselves to use the remaining resource blocks. In the Competitive scheduling algorithm, the primary user agents will compete among themselves over the available resources, then the secondary user agents will compete among themselves over the remaining resources. Experimental results show that both scheduling algorithms converged to almost 90% utilization of the spectrum, and provided fair shares of the spectrum among users.

Who Knows I Like Jelly Beans? An Investigation Into Search Privacy

Abstract Internal site search is an integral part of how users navigate modern sites, from restaurant reservations to house hunting to searching for medical solutions. Search terms on these sites may contain sensitive information such as location, medical information, or sexual preferences; when further coupled with a user’s IP address or a browser’s user agent string, this information can become very specific, and in some cases possibly identifying. In this paper, we measure the various ways by which search terms are sent to third parties when a user submits a search query. We developed a methodology for identifying and interacting with search components, which we implemented on top of an instrumented headless browser. We used this crawler to visit the Tranco top one million websites and analyzed search term leakage across three vectors: URL query parameters, payloads, and the Referer HTTP header. Our crawler found that 512,701 of the top 1 million sites had internal site search. We found that 81.3% of websites containing internal site search sent (or leaked from a user’s perspective) our search terms to third parties in some form. We then compared our results to the expected results based on a natural language analysis of the privacy policies of those leaking websites (where available) and found that about 87% of those privacy policies do not mention search terms explicitly. However, about 75% of these privacy policies seem to mention the sharing of some information with third-parties in a generic manner. We then present a few countermeasures, including a browser extension to warn users about imminent search term leakage to third parties. We conclude this paper by making recommendations on clarifying the privacy implications of internal site search to end users.

Learning-Based Multi Attribute Network Selection in Heterogeneous Wireless Access

The rise of heterogeneous networks including macro, micro, pico, femto, and WLAN presents new challenges in optimizing user’s access to the networks. To fully utilize the capacity of such rich field of heterogeneous wireless connectivity, mobile devices should be able to select Radio Access Network (RAN) for their connection depending on their need. The proposed algorithm here is based on an autonomous agent at the mobile node and assisted by integration of distributed cloud services, e.g. the edge cloud. The selection of RAN is based on a Multi Attribute Decision Making process combined with the Reinforcement Learning that reinforces historical data collected from the edge cloud. Mobile agent is responsible for collecting data, executing the selection algorithm, possibly offloading the parts of the execution to the edge cloud, and providing feedback to the edge cloud after termination of the connection. The ultimate aim is to the design of RAN selection algorithm owned by autonomous and intelligent user agents that can significantly improve the user’s experience in terms of network coverage, data rate, latency, and battery lifetime. Through extensive simulation scenarios, we demonstrate the stability and precision of our proposed algorithm.

Multi-Agent with Multi Objective-Based Optimized Resource Allocation on Inter-Cloud

Cloud computing is the ability to provide new technologies and standard cloud services. One of the essential features of cloud computing is the provision of “unlimited” computer resources to users on demand. However, single cloud resources are generally limited and may not be able to cope with the sudden rise in user needs. Therefore, the inter-cloud concept is introduced to support resource sharing between clouds. In this system, each cloud can tap the resources of other clouds when there are not enough resources to meet the demands of the consumer. In cloud computing, allocating the available resources of service nodes to on-demand tasks is an important concern. To achieve this, in this paper, multi-agent with multi-objective optimized resource allocation on inter-cloud is proposed. The proposed algorithm is a combination of adaptive tree seed optimization (ATSO) and multi-agent. The proposed approach consists of four agents namely, user interface agent, monitoring agent, scheduler agent, and Executer agent. Initially, the user agent collects the task from the users, and the monitoring agent reports the resource list to the scheduler agent. Based on the resource list, the scheduler agent schedules the task with the help of ATSO. Finally, the executer agent, allocate the task to the resources. The performance of the proposed approach is evaluated using makespan, cost, and resource utilization.

Study on the Instability of Information Systems and Security Risks in the Public Administration: Case of Burkina Faso Public Administration

The purpose of this research is to show the instability and the security risks of the information system in Burkina-Faso public administration. In this paper, witnessing unsatisfactory services such as government messaging (mailer.gov.bf) as well as G-cloud services which are the government cloud were studied. The behavior of user agents on the administration’s IT infrastructures which could expose the information system to security risks was also studied. The expected result shows evidence of the weakness of the public administration information system and provides some recommendation.

Next-Generation OTT Distribution Architecture Supporting Multicast-Assisted ABR (mABR) and HTTP/3 Over QUIC

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">The potential audience for streaming media services is massive, but satisfying that kind of demand poses a huge technical challenge. Even if existing content delivery networks (CDNs) were capable of distributing such loads, the costs at that scale would remain beyond the monetary capabilities of any broadcaster. By reducing transmission costs associated with content delivery at scale, broadcasters would pay less to the CDNs, making the content cheaper to the end user. In traditional broadcasting technologies, a majority of commercially available content distribution architectures are based on a paradigm rooted in linear broadcast services, having a very basic media service composition, solely focusing on the delivery of content interpretable on a vast amount of compatible TV sets with built-in tuners.While this helped transform the industry by facilitating the transition from analogue to digital broadcast content distribution, it now leaves content providers and broadcast network operators wondering how they can remain relevant in the age of web and internet streaming. This article will take its point of departure in evolved content distribution systems and the resulting system architectures for next-generation broadcast over-the-top (OTT) live and on-demand streaming. It will focus on a solution for efficient and secure transport of well-known low-latency streaming protocols by employing QUIC as a primary transport protocol for the industry leading IP multicast architectures supporting multicast-assisted adaptive bitrate (mABR) streaming, enabled by novel HTTP/3 server push techniques. At scale, the solution could significantly reduce the amount of open streaming sessions, improving the scalability of existing audio–video streaming architectures, and reducing the wastage of scarce bandwidth. Placing physical gateway boxes within users’ home network that fully support QUIC and multicast content reception can reduce the prevalent lack of user agent support for reception of multicast QUIC content. This new model with the core transport protocol based on QUIC will be the foundation for unified content distribution architectures, spanning internet streaming, IPTV, 5G, and the latest ATSC and DVB physical layers, allowing for an efficient, IP-based delivery of linear content to thin clients.</i>

Bidding Agents for PV and Electric Vehicle-Owning Users in the Electricity P2P Trading Market

As the world strives to decarbonize, the effective use of renewable energy has become an important issue, and P2P power trading is expected to unlock the value of renewable energy and encourage its adoption by enabling power trading based on user needs and user assets. In this study, we constructed a bidding agent that optimizes bids based on electricity demand and generation forecasts, user preferences for renewable energy (renewable energy-oriented or economically oriented), and owned assets in a P2P electricity trading market, and automatically performs electricity trading. The agent algorithm was used to evaluate the differences in trading content between different asset holdings and preferences by performing power sharing in a real scale environment. The demonstration experiments show that: EV-owning and economy-oriented users can trade more favorably in the market with a lower average execution price than non-EV-owning users; forecasting enables economy-enhancing moves to store nighttime electricity in batteries in advance in anticipation of future power generation and market prices; EV-owning and renewable energy-oriented users can trade more favorably in the market with other users. EV-owning and renewable energy-oriented users can achieve higher RE ratios at a cost of about +1 yen/kWh compared to other users. By actually issuing charging and discharging commands to the EV and controlling the charging and discharging, the agent can control the actual use of electricity according to the user’s preferences.