Enhancement of Security in RFID using RSA Algorithm

A huge revolution has occurred in Radio Frequency Identification (RFID) technologies during the past decades. More vendors are involved, and have invested in this technology, which promises wholesale changes across a broad spectrum of business activities. Currently, RFID systems are usually available in low, high, ultra-high, and microwave frequencies with passive, semi-passive (or semi-active) and active transponders or tags. Tags might be either Chipless, or contain a microchip with read only, or read and write memory. The component controlling communication in a RFID system is called a reader or interrogator, which can be stationary or portable depending on the application. In order for the tags to transmit their data, the tags must be in the reader’s field or interrogation zone, and receive the necessary energy (in form of radio waves) from the reader. Although promising, RFID is not without its challenges, which arise from both a technological and usage point of View (IT Pro, 2005).  A common concern with RFID is data security. Data Security is a key area in RFID usage, it determines wholly whether this technology will be adopted fully especially in this part of the world (Eastern and Central Africa) for business processes and automation. For this technology to be utilized fully and realized then the users of the system MUST be assured of their data’s security.People who use devices that carry personal financial information, such as credit card or other ID numbers, do not want others to access their accounts. These are significant security vulnerabilities in RFID. Some researchers have proposed schemes that would require tags to authenticate readers, thus transmitting information only to authorized readers.This research paper addresses the security challenge in RFID by proposing RSA algorithm as a viable solution to encrypt data over transmission and also authenticate the reader and the tags. Keywords: RFID, Security, authenticate, Data Security

Factors Affecting Acceptance, Adoption and Use of Online SNS by Seniors

The use of Online Social Networks is higher among the youth as compared to the use of the same by seniors in Kenya. Online social networking has the potential to enrich the lives of seniors by providing them with an easy way to stay in touch with friends and family. Software Engineers and designers are anxious to capture the needs of this demographic through this new channel. Yet very little is known about what influences seniors to use online social networks in relation to development and design of this online social networks.This study uses results from a pilot study conducted in some Universities in Kenya as well as theory and literature to identify and examine what key factors influence seniors to accept and use online social networks then map the factors to enhance an already existing model Unified Theory of Acceptance and Use of Technology (UTAUT) used to explain Acceptance and Use of technology. The enhanced model that emerged describes the key factors that influence acceptance and use. Specifically the model indicates that perceived privacy, security and trust, proclivity to give and get information, content of Online Social Networking Sites(SNS) are some of the key factors that influence seniors to use online social networks. The enhanced model is a first step of an ongoing research project that aims to provide software engineers and designers with the requirements of seniors in Online Social Networks. Keywords: Online Social Networks, Elderly, UTAUT, SNS, Online Social Networking Use and Acceptance

A Kenyan perspective on the use of animals in science education and scientific research in Africa and prospects for improvement

Introduction: Animal experimentation is common in Africa, a region that accords little priority on animal protection in comparison to economic and social development. The current study aimed at investigating the prevalence of animal experimentation in Kenya, and to review shortfalls in policy, legislation, implementation and enforcement that result in inadequate animal care in Kenya and other African nations. Methods: Data was collected using questionnaires, administered at 39 highly ranked academic and research institutions aiming to identify those that used animals, their sources of animals, and application of the three Rs. Perceived challenges to the use of non-animal alternatives and common methods of euthanasia were also queried. Data was analyzed using Epidata, SPSS 16.0 and Microsoft Excel. Results: Thirty-eight (97.4%) of thirty-nine institutions reported using animals for education and/or research. Thirty (76.9%) institutions reported using analgesics or anesthetics on a regular basis. Thirteen (33.3%) institutions regularly used statistical methods to minimize the use of animals. Overall, sixteen (41.0%) institutions explored the use of alternatives to animals such as cell cultures and computer simulation techniques, with one (2.6%) academic institution having completely replaced animals with computer modeling, manikins and visual illustrations. The commonest form of euthanasia employed was chloroform administration, reportedly in fourteen (29.8%) of 47 total methods (some institutions used more than one method). Twenty-eight (71.8%) institutions had no designated ethics committee to review or monitor protocols using animals. Conclusion: Animals are commonly used in academic and research institutions in Kenya. The relative lack of ethical guidance and oversight regarding the use of animals in research and education presents significant concerns

A Metrics-based Framework for Estimating the Maintainability of Object-Oriented Software

Time,  effort  and  money  required  in  maintaining software has always  been  considered  greater  than  its  development  time. Also, its ambiguity in forecast at early stage of software development makes the process more complicated. The early estimation of maintainability will significantly help software designers to adjust the software product, if there is any fault, in early stages of designing. By doing this; time, effort and money required in maintaining software will be lessened. Although Object Oriented Software Development (OOSD) approach is equipped for enhancing software maintainability, a method for finding out if the required level of maintenance is being achieved amid the development process is required. This can be accomplished through measurement. This paper outlines the need and importance of maintainability at design phase and develops a Metrics-Based Maintainability Estimation Framework for Object-Oriented software(MEFOOS) that estimates the maintainability of object oriented software components in regard of their Understandability, Modifiability and Portability—which are the sub-attributes of maintainability. Keywords: Software maintenance, Software Maintainability, maintainability model, Software Metrics, Software Component DOI: 10.7176/JIEA/9-4-02 Publication date:June 30th 201

Using Keystroke Dynamics and Location Verification Method for Mobile Banking Authentication.

With the rise of security attacks on mobile phones, traditional methods to authentication such as Personal Identification Numbers (PIN) and Passwords are becoming ineffective due to their limitations such as being easily forgettable, discloser, lost or stolen. Keystroke dynamics is a form of behavioral biometric based authentication where an analysis of how users type is monitored and used in authenticating users into a system. The use of location data provides a verification mechanism based on user’s location which can be obtained via their phones Global Positioning System (GPS) facility. This study evaluated existing authentication methods and their performance summarized. To address the limitations of traditional authentication methods this paper proposed an alternative authentication method that uses Keystroke dynamics and location data. To evaluate the proposed authentication method experiments were done through use of a prototype android mobile banking application that captured the typing behavior while logging in and location data from 60 users. The experiment results were lower compared to the previous studies provided in this paper with a False Rejection Rate (FRR) of 5.33% which is the percentage of access attempts by legitimate users that have been rejected by the system and a False Acceptance Rate (FAR) of 3.33% which is the percentage of access attempts by imposters that have been accepted by the system incorrectly, giving an Equal Error Rate (EER) of 4.3%.The outcome of this study demonstrated keystroke dynamics and location verification on PINs as an alternative authentication of mobile banking transactions building on current smartphones features with less implementation costs with no additional hardware compared to other biometric methods. Keywords: smartphones, biometric, mobile banking, keystroke dynamics, location verification, securit

Feature Based Data Anonymization for High Dimensional Data

Information surges and advances in machine learning tools have enable the collection and storage of large amounts of data. These data are highly dimensional.  Individuals are deeply concerned about the consequences of sharing and publishing these data as it may contain their personal information and may compromise their privacy. Anonymization techniques have been used widely to protect sensitive information in published datasets. However, the anonymization of high dimensional data while balancing between privacy and utility is a challenge. In this paper we use feature selection with information gain and ranking to demonstrate that the challenge of high dimensionality in data can be addressed by anonymizing attributes with more irrelevant features. We conduct experiments with real life datasets and build classifiers with the anonymized datasets. Our results show that by combining feature selection with slicing and reducing the amount of data distortion for features with high relevance in a dataset, the utility of anonymized dataset can be enhanced. Keywords: High Dimension, Privacy, Anonymization, Feature Selection, Classifier, Utility DOI: 10.7176/JIEA/9-2-03 Publication date: April 30th 201

Lack of Awareness by End Users on Security Issues Affecting Mobile Banking: A Case Study of Kenyan Mobile Phone End Users

The use of mobile phones in African has seen a formidable growth. The use of mobile phones to perform business and financial transactions seems to be on the increase as well. The rise in use of mobile phones to perform financial transactions also increases the risks associated with such transactions and especially man in the middle attacks. These compounded with lack of awareness among users means that they (the users) are highly exposed to such attacks. Due to the popular use of mobile banking in Kenya and the third world in particular, securing communication between the mobile device and the back end server has become a fundamental issue. This is due to the fact that hackers have the ability to steal banking information using various techniques, particularly the duping of mobile phone users to believe that they are communicating with a genuine program from their bank while in reality a user is simple giving away sensitive information to the hacker. This paper aims to investigate the level of awareness among users of mobile banking transactions in regards to man in the middle attacks and whether the awareness or lack of it can increase or deter such attacks Key words: mobile phones, Mobile banking services, Security, man in the middle attack

Anti-inflammatory activity of selected plants used by the Ilkisonko Maasai, Kenya

Background: The Ilkisonko Maasai are a Kenyan pastoralist community that uses indigenous plants for the management of pain and inflammatory conditions such as arthritis. Objectives: The purpose of this study was to validate the use of plants for medicinal purposes by the Ilkisonko Maasai through testing for anti-inflammatory activity using the carrageenan-induced rat paw oedema assay. Methodology: The methanol extracts (400 mg/kg body weight) of Rhus natalensis (bark), Acacia drepanolobium (bark), Acacia nilotica (bark), Acacia reficiens (bark), Acacia robusta (bark), Grewia villosa (bark), Ximenia americana (bark and leaves) and Rhus vulgaris (leaves) were evaluated for in vivo anti-inflammatory efficacy using the carrageenan-induced rat paw oedema assay. Diclofenac (20 mg/kg body weight) was used as the positive control and paw volume was measured by a plethysmometer. Results: The maximum percentage inhibition (PI) of the extracts was observed as Grewia villosa (58.6% at 24 h), Rhus vulgaris (57.8% at 24 h), Acacia nilotica (55.5% at 1 h), Ximenia americana (54.5% at 1 h), Acacia drepanolobium (50.9% at 24 h), Acacia reficiens (47.6% at 1 h), Rhus natalensis (43.8% at 24 h) and Acacia robusta (37.4% at 24 h) (p < 0.05 for all PI). Diclofenac (20 mg/kg) showed a steady increase in PI from 1 h to 4 h with a maximum PI of 66.2% (p < 0.05) at 4 h and the lowest PI of 14.3% at 24 h. Conclusion: All extracts of the plants assessed exhibited anti-inflammatory activity at early phase of inflammation. Additionally, extracts of five plants, namely Rhus natalensis, Acacia drepanolobium, Acacia robusta, Grewia villosa and Rhus vulgaris showed anti-inflammatory activity at both early and late phases of inflammation. There is need for further studies to identify phytochemicals with active anti-inflammatory activity. Key words- Ilkisonko Maasai, carrageenan, inflammation, Rhus, Acacia, Grewia and Ximeni

An Improved Model for the Implementation of Web-Based Learning in Adult Secondary School Education in Kenya

The development of technology, which evolves continuously, has led to the transformation of traditional courses into web-based courses. However, as these e-learning systems grow more complex, involving numerous users with different levels of need, there is a need to have web-based learning models that adequately address such users’ needs, taking into consideration their levels of expertise, access and ability to interact with such systems. Most of the existing models present the adult learners with difficulties, as most of them have to concentrate mostly on learning the technology rather than learning the desired content. Most of the difficulties arise from the web-based learning model configurations in use in the country. The majority lack features and capabilities of highly interactive, fast-paced multimedia-supported learning currently demanded by most learners and tutors. Therefore, the main aim of this research was to devise an improved model for implementing a web-based learning programme in adult secondary school education. After analysing the existing models and establishing their operational challenges, an improved model was proposed. The proposed model was statistically tested using sample data. The results showed that recognizing both technological and user attributes along the recognized theoretical frameworks was important in increasing the users’ behavioural inclination to use the improved model. Therefore, it is recommended that more sensitization to web-based learning should be implemented by the adult education department in the Ministry of Education among adult learners in the country. It is also recommended that system developers should find ways of incorporating additional features into the model without affecting its architecture and function. Finally, there is need for future studies on the causal antecedents of the constructs presented in this research model to provide more precise practical implications

A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment

Accurate diagnosis is the key to providing prompt and explicit treatment and disease management. The recognized biological method for the molecular diagnosis of infectious pathogens is polymerase chain reaction (PCR). Recently, deep learning approaches are playing a vital role in accurately identifying disease-related genes for diagnosis, prognosis, and treatment. The models reduce the time and cost used by wet-lab experimental procedures. Consequently, sophisticated computational approaches have been developed to facilitate the detection of cancer, a leading cause of death globally, and other complex diseases. In this review, we systematically evaluate the recent trends in multi-omics data analysis based on deep learning techniques and their application in disease prediction. We highlight the current challenges in the field and discuss how advances in deep learning methods and their optimization for application is vital in overcoming them. Ultimately, this review promotes the development of novel deep-learning methodologies for data integration, which is essential for disease detection and treatment