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Detection And Mitigation Of Distributed Denial Of Service (Ddos) Attack: Application To Smart Grid Communication Networks
Full text linkThe Smart Grid is an improvement on the conventional grid that uses advanced communication methods and new technology for the production, transmission, and distribution of electrical power. The modern Smart Grid \u27s ability to function successfully depends heavily on its communication infrastructure. Today, the usage of communication technology promotes energy efficiency, coordination amongst all Smart Grid components, from generation to end users, and optimal Smart Grid functioning. The communication network of the Smart Grid exchanges data regarding the condition of its numerous integrated IEDs (intelligent electronic devices); however, there are always chances for attackers to interrupt utility resources, interfere with communication networks, or steal customers\u27 intellectual property and private information due to the different amounts of IEDs connected across Smart Grid Communication Networks.
Additionally, as Distributed Energy Resources (DER) and dynamic loads become more prevalent, phase angle values that are crucial for Phasor Measurement Units (PMUs) change, and real-time control has emerged as a key tool for tracking power system performance in today\u27s Smart Grid technology. Because of their link to the Smart Grid \u27s communication network, Phasor Measurement Units devices are now susceptible to cyberattacks. Because of the recent global security incidents and new cyberthreats, this development has created new cyber-security issues for the Smart Grid and is a very worrying issue.
The effects of Distributed Denial of Service (DDOS) assaults on PMU data transfers over Smart Grid communication networks in the form of NetFlows were carefully examined in this study. For the first time in the literature, a combination of the Secure Network Analytics (SNA) tool, Intrusion Detection System, and firewall were used to model the DDOS attack in the Smart Grid \u27s communication network. Additionally, risk reduction and good security hygiene are enhanced by employing the Secure Network Analytics (SNA) tool to establish a security baseline for the Smart Grid system.
The research findings are in contrast with those found in previous studies. Our findings demonstrated that this research strategy outperformed previous approaches in the literature in terms of mitigating and detecting DDOS attacks.
Index Terms: Detection and mitigation, distributed denial of service (DDOS) attack, distributed energy resources, firewall, intrusion detection and prevention systems, phase measurement units, Smart Grid system
Evaluating Tax Preparers’ Perception Of The Tax Cuts And Jobs Act (TCJA) Through The Lens Of The Tax Misperception Theory
No full textThe Tax Cuts and Jobs Act (TCJA) of 2017 represented one of the most extensive overhauls of the U.S. tax code in decades. While it is credited with reducing statutory tax burdens, the TCJA also introduced substantial complexity, which shaped tax preparers’ behavior and reshaped the tax preparation industry. Designed to stimulate economic growth through broad changes to both corporate and individual taxation, the TCJA’s impact on the profession cannot be understood without considering how preparers perceived the legislation. This study, therefore, evaluated the effects of the TCJA on tax preparers’ perceptions using the Tax Misperception Theory.
Results from Tobit regression analysis showed that perceptions were influenced primarily by personal factors such as state residency, gender, education, satisfaction with the U.S. tax system, and political ideology. Notably, experience, familiarity with the TCJA, and political party were not statistically significant. The insignificance of familiarity suggests that preparers’ judgments were not focused on the law’s content but were instead shaped by personal and ideological perspectives.
These findings indicate that even well-informed professionals are vulnerable to biases, poor framing, and unclear communication, which can lead to misperceptions of new tax laws. This is particularly striking given that most respondents had more than 20 years of tax preparation experience. The results point to the need for more effective training that not only updates technical knowledge but also communicates the goals and long-term effects of reforms. Despite the availability of continuing education and seminars, many tax preparers still perceived recent changes as negative for taxpayers.
This quantitative study applied Tax Misperception Theory to demonstrate how tax preparers form perceptions of tax reform. The theory suggests that preparers, like taxpayers, may develop negative views of tax law changes when personal factors such as demographics, education, experience, and political biases come into play. By analyzing survey responses from 151 members of the National Association of Enrolled Agents (NAEA), the study clarified the unintended professional and economic consequences of the TCJA. Overall, tax preparers reported generally favorable views of the law, though perceptions varied significantly depending on satisfaction with the tax system, political ideology, and related influences.
Keywords: tax misperception, taxpayer compliance, reported income, tax refunds, government revenu
(SI15-022) Five Efficient Cryptography Authentication Schemes with Functional Relation on General Spaces
No full textThis work addresses the growing demand for diversification in cryptographic schemes to secure communication. This work proposes a novel suite of algorithms, including two block ciphers (TPBlock and TAP-Block), two stream ciphers (TP-Stream and TAP-Stream), and a zero-knowledge proof scheme (F-zero knowledge proof). All schemes leverage functional relations defined over the real number space with a dimension greater than one for encryption, decryption, and key generation, offering an alternative to the number-theoretical aspects and algebraic structures commonly used in existing schemes. The main goal of this work is to introduce and propose these five novel cryptographic schemes to provide authentication and its simultaneous verification, but not to measure security strength
(SI15-140) Designing Bayesian Double Sampling Plans Based on Zero Inflated Poisson Distribution
No full textThe implementation of attribute-based sampling inspection serves as a quality control technique used across numerous industries to evaluate items or workflow processes. When the data exhibits a substantial number of zero counts, the zero-inflated Poisson (ZIP) distribution serves as an effective model for accommodating this zero-inflation. Double sampling plan (DSP) is a quality check method where the decision to approve or decline a batch comes after examining two samples, providing more conclusive information compared to a single sample plan (SSP). In practice, effective decision-making regarding submitted lots considers both within-lot and between-lot variations, which can be addressed through the use of Bayesian methodology. The Bayesian approach facilitates more informed decision-making concerning the acceptance or rejection of a batch by incorporating prior knowledge about nonconformities. This article presents the designing Bayesian DSPs when the count of nonconformities per products follows a ZIP distribution. Employing a Gamma prior to the parameter in the Poisson component, the operating characteristic (OC) and average sample number (ASN) functions are derived with reference to the predictive distribution. Examples are provided to determine Gamma - ZIP (GZIP) DSPs
(SI15-107) Mathematical Modeling of Cancer Dynamics Stability Analysis of Post Therapy Protection
No full textThe study of PSITPS mathematical model is developed to analyze cancer dynamics, focusing on post-therapy protection. The model’s solution is rigorously examined for boundedness and positivity of solutions. Equilibrium points are identified, and numerical methods are employed to study stability. Simulations using accurate cancer data demonstrate the effectiveness of post-treatment protection in controlling cancer spread and minimizing recurrence. Stability analysis confirms the model’s predictive capability, offering valuable insights into long-term patient outcomes. The results highlight the importance of continuous post-treatment care in improving survival rates and reducing disease prevalence. This study provides a framework for optimizing cancer treatment strategies by integrating mathematical modeling with empirical data
(R2125) Impact of Convective Condition and Inclined Magnetic Field on Casson Nanofluid with Bioconvection in Porous Medium
No full textThis paper presents an analysis on steady magnetohydrodynamic boundary layer flow of Casson nanofluid with microorganisms near an expandable boundary in a porous medium. The flow behaviour is analysed by incorporating impacts of magnetic field applied in a direction which makes an angle α∗ with the boundary and convective conditions in temperature, nanoparticle concentration and microorganism density at boundary. Non-dimensionalised nonlinear and coupled system of ordinary differential equations are solved by exercising a numerical algorithm termed as spectral local-linearization method. The considered numerical algorithm is found to be convergent and capable of yielding accurate results. Graphical sketches of solutions obtained are produced and explained the variations in characteristics of flow, heat transfer, nanoparticle and microorganism density. The considered model may help to understand the application of solar energy in processes of thermal engineering. This study has direct relevance to cooling of metallic plates, industries of glass and polymer, heat exchanger, improving oil recovery through microbes, creating biofilms with the help of microorganisms and developing bio-nano cooling systems, etc. This study reveals that temperature profiles are highly influenced by Biot and Eckert numbers
Knowledge Acquisition On Mass-Shooting Events Via Llms For Ai-Driven Justice
No full textMass-shooting events pose a significant challenge to public safety because they generate large volumes of unstructured textual data, making it difficult to conduct effective investigations and formulate public policy. The proposed knowledge acquisition system utilized Large Language Models (LLMs) with few-shot prompting to extract vital information from news articles, police reports, and social media content at high speed. The system used entity recognition to detect vital information, which included offenders, victims, locations, and criminal instruments that support legal investigations. An experimental study conducted on actual mass-shooting datasets showed GPT-4o performs best for mass-shooting NER. The o1-mini delivered competitive performance while using fewer resources more efficiently for basic NER applications. It is also observed that increasing the number of shots enhanced the performance of all models, but the gains were more substantial for GPT-4o and o1-mini, highlighting their superior adaptability to few-shot learning scenarios.
Index Terms — AI-driven justice, few-shot learning, information extraction, knowledge acquisition, knowledge graphs, large language models, mass shooting, named entity recognition (NER), prompt engineering