Lightweight Deep Learning Framework to Detect Botnets in IoT Sensor Networks by using Hybrid Self-Organizing Map

In recent years, we have witnessed a massive growth of intrusion attacks targeted at the internet of things (IoT) devices. Due to inherent security vulnerabilities, it has become an easy target for hackers to target these devices. Recent studies have been focusing on deploying intrusion detection systems at the edge of the network within these devices to localize threat mitigation to avoid computational expenses. Intrusion detection systems based on machine learning and deep learning algorithm have demonstrated the potential capability to detect zero-day attacks where traditional signature-based detection falls short. The paper aims to propose a lightweight and robust deep learning framework for intrusion detection that has computational potential to be deployed within IoT devices. The research builds upon previous researches showing the demonstrated efficiency of anomaly detection rates of self-organizing map-based intrusion. The paper will contribute to the existing body of knowledge by creating a hybrid self-organizing map (SOM) for the purpose of detecting botnet attacks and analyzing its accuracy compared with a traditional supervised artificial neural network (ANN). The paper also aims to answer questions regarding the computational efficiency of our hybrid self-organizing map by measuring the CPU consumption based on time to train model. The deep learning prototypes will be trained on the NSL-KDD dataset and Detection of IoT botnet Attacks dataset. The study will evaluate the performance of a self-organizing map based k-nearest neighbor prototype with the performance of a supervised artificial neural network based on validation metrics such as confusion matrix, f1, recall, precision, and accuracy score

Pedagogical Evaluation of Cognitive Accessibility Learning Lab in the Classroom

In a study conducted by Webaim, 98.1% of sites had a detectable accessibility issue. This poses a profound challenge to the 1 billion users across the world who have a disability. This indicates that developers either are not aware of how to make the sites accessible or aware of how critical it is to make the sites usable by all users. This problem is further compounded by the lack of available resources that can educate students and future developers in making their software accessible. To address current limitations/challenges, we have developed an all-in-one immersive learning experience known as the Accessibility Learning Labs (ALL). These modules are carefully crafted to provide students a better understanding of various accessibility topics and increase awareness. They incorporate the best of all learning methods, from case studies to hands-on activities and quizzes. In this paper, we focus specifically on the cognitive module developed under the Accessibility Learning Labs. This module strives to educate students on the importance of building accessible software for users with cognitive disabilities. We discuss the pedagogical approach used to craft the components of the cognitive module and the design rationale behind the experiential activity. We investigate how the order of the reading and experiential activity affect the students\u27 understanding of the material. To do this, we perform a study involving 28 students in 2 computer science-related courses. Our findings include: (I) The accessibility improvements made in the lab have a positive impact on the students\u27 performance when compared to the inaccessible version (II) When the reading material is presented after the experiential activity, students have a better understanding of the cognitive accessibility principles

Envisioning the Future of Scholarly Communications with Digital Commons

We’d like to take you on a quick journey, starting at the very beginning to examine how institutional repositories came to be, why we think the library remains at the center of knowledge management and distribution and how the librarian community plays a pivotal role in advancing scholarly communication. We will then discuss the role Digital Commons plays in supporting this community and our ongoing commitment to furthering open access and scholarly communications

Examining the Changing Role of Influencing Factors in the Association between Food Insecurity & Obesity

About 49 million Americans – roughly 15% of entire America - live in households that lack the means to get enough nutritious food on a regular basis. Past experiences and fear of food accessibility could affect the quality of diet and eating behavior in many ways. In this study we examine long-term trends in food insecurity and obesity over a 6-year period. We specifically examine the changing role of health behaviors in the association between food insecurity and obesity. Most studies on this topic have conducted cross-sectional analysis. Examining this association over time would help us make more careful considerations in making policies. Until recently, it was assumed that the only reason for being overweight was excessive eating. Food insecurity could also cause weight gain due to adverse social and physical environments with identifiable risk factors. It is imperative to know that food security and poverty are both forms of material deficit which bring about a range of detrimental results such as excess weight gain. Food insecurity is a continuum of experiences ranging from the most extreme form, starvation, to complete food security. Changes in food security status can be temporary, cyclical, medium or long term

MODELLING AND FAULT DIAGNOSIS APPROACH FOR PROTON EXCHANGE MEMBRANE FUEL CELL SYSTEMS INCORPORATING AMBIENT CONDITIONS

Proton exchange membrane fuel cell (PEMFC), as a source of electrical power, provides numerous benefits such as zero carbon emission and high reliability as compared to wind and solar energy. PEMFC operates at very low temperature, high power density, and has very high durability as compared to other fuel cells. Being a non-linear power source with high sensitivity to ambient conditions variation, the prediction of PEMFC voltage and temperature is a complicated issue. The most common PEMFC models are classified as mechanistic models, semi-empirical models, and purely empirical methods. The mechanistic models are complex and require differential equations to predict the voltage and temperature of PEMFC. However, the semi-empirical models are less complicated and can be used easily for the online prediction of PEMFC outputs. Therefore, the first part of this thesis attempt to model the voltage of PEMFC using simple and effective semi-empirical equations. The initial feature of the proposed technique is to incorporate the features of a mechanistic model with less complex equations. The model considers the internal currents and the internal voltage drop associated with the PEMFC. Besides, activation and concentration voltage drops are addressed based on theoretical functions. Thus, the proposed model provides an additional benefit that not only does the output voltage model satisfy the voltage for both loaded and unloaded conditions but also the component voltage drops waveforms match with the theoretical waveforms given in the mechanistic models. The second part of the thesis focuses on modelling the PEMFC temperature. Previously most temperature models use complex equations incorporating PEMFC output voltage which is not a good option as the temperature must be predicted using only load current and ambient temperature. The model proposed in this thesis is developed through an algorithm that tracks the online changes in the load current and ambient temperature. It provides the accurate temperature of PEMFC by using a simple first-order equation with the help of a tracking algorithm. Quantum lig tening search algorithm (QLSA) is used for the optimization of constant parameters for both voltage and temperature models. The PEMFC performance is affected by factors such as variations in ambient temperature, pressure, and air relative humidity and thus they are vital for predicting PEMFC performance. The thesis also attempts to directly predict the variations in PEMFC voltage under varying ambient conditions at different load resistance. For this purpose, statistical analysis is used to propose empirical equations that can predict the variations in PEMFC voltage for varying ambient conditions. In this context of the model development, the parameters which are significantly varying with ambient changes are identified with the help of statistical regression analysis and represented as ambient temperature and air relative humidity dependent parameters. The enhanced semi-empirical voltage model is verified by performing experiments on both the Horizon and NEXA PEMFC systems under different conditions of ambient temperature and relative humidity with root mean square error (RMSE) less than 0.5. Results obtained using the enhanced model are found to closely approximate those obtained using PEMFCs under various operating conditions, and in both cases, the PEMFC voltage is observed to vary with changes in the ambient and load conditions. Inherent advantages of the proposed PEMFC model include its ability to determine membrane-water content and water pressure inside PEMFCs. The membrane-water content provides clear indications regarding the occurrence of drying and flooding faults. For normal conditions, this membrane water content ranges between 12.5 to 6.5 for the Horizon PEMFC system. Based on simulation results, a threshold membrane water- content level is suggested as a possible indicator of fault occurrence under extreme ambient conditions. Limits of the said threshold are observed to be useful for fault diagnosis within the PEMFC systems

The curious case of urban population in Pakistan

In their examination of recently released census data, Saad Khan and Dr Muhammad Adeel write that not only has the urban population been undercounted, but that there exists an urban bias that is affecting vital service delivery to the rural and peri-urban populations in Pakistan

The role of Artificial Intelligence in digital forensics:Case studies and future directions

The increase in digital evidence, especially in cases involving Indecent Images of Children (IIOC), presents a pressing challenge for law enforcement agencies. In this article, we discuss two of the most prominent types of Artificial Intelligence (AI) and how they can be used in digital forensic processes, providing examples, and highlighting potential challenges that are likely to be experienced in developing and adopting AI. The two main types are of Data-Driven Model (DDM) age classification and Model-Based Reasoning (MBR), and in this article, examples for both are provided and discussed in the contents of IIOC investigations

Agent-based modeling of a price information trading business

We describe an agent-based simulation of a fictional (but feasible) information trading business. The Gas Price Information Trader (GPIT) buys information about real-time gas prices in a metropolitan area from drivers and resells the information to drivers who need to refuel their vehicles. Our simulation uses real world geographic data, lifestyle-dependent driving patterns and vehicle models to create an agent-based model of the drivers. We use real world statistics of gas price fluctuation to create scenarios of temporal and spatial distribution of gas prices. The price of the information is determined on a case-by-case basis through a simple negotiation model. The trader and the customers are adapting their negotiation strategies based on their historical profits. We are interested in the general properties of the emerging information market: the amount of realizable profit and its distribution between the trader and customers, the business strategies necessary to keep the market operational (such as promotional deals), the price elasticity of demand and the impact of pricing strategies on the profit.Comment: Extended version of the paper published at Computer and Information Sciences, Proc. of ISCIS-26, 201