Browsing by Author "Muketha, Geoffrey Muchiri"

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Advances in composite integer factorization
(International Knowledge Sharing Platiform, 2013-01-01) Wanambisi, Adrin W.; Aywa, Shem; Maende, Cleophas; Muketha, Geoffrey Muchiri
In this research we propose a new method of integer factorization. Prime numbers are the building blocks of arithmetic. At the moment there are no efficient methods (algorithms) known that will determine whether a given integer is prime or and its prime factors[1]. This fact is the basis behind many of the cryptosystems currently in use.
Algebraic approach to composite integer factorization
(European Centre for Research, Training and Development : IJMSS, 2013-03-01) Wanambisi, Adrin W.; Aywa, Shem; Maende, cleophas; Muketha, Geoffrey Muchiri
There various algorithms that can factor large integers but very few of these algorithms run in polynomial time. This fact makes them inefficient. The apparent difficulty of factoring large integers is the basis of some modern cryptographic algorithms. In this paper we propose an algebraic approach to factoring composite integer. This approach reduces the number of steps to a finite number of possible differences between two primes.
Interface features, program complexity and memorability as indicators of learnability of mobile social software
(International Journal of Science and Research, 2017) Masese, Nelson Bogomba; Muketha, Geoffrey Muchiri; Mbuguah, Samuel Mungai
Social Computing aims to support the tendency of humans to interact with mobile devices. Technology reinforces this interaction by producing appropriate responses that then lead to improved communication between humans and computational devices. Although latest developments in mobile phone technologies have opened the way for a new generation of mobile social applications that allow users to interact and share information, there is still very limited user support information on how to use different applications. This problem either increases the learning curve of the users, thereby adversely affects their overall efficiency. The main purpose of this paper is to analyze factors that affect the learnability of mobile social software. A sample of 361 respondents was selected, with 345 respondents returning feedback. Primary data was collected through the use of questionnaires and interviews targeting mobile social users in Nakuru County Kenya. Three social networks were used, namely, WhatsApp, Facebook and Twitter. Data analysis was done using descriptive statistics. Findings indicate that interface features affect learnability across the three social networks, with learnability of WhatsApp turning out to be higher than that of compared to Facebook and Twitter. Findings also indicate that more than 60% program supports compatibility with other applications while 59.4% of the respondents agreed that maintaining language is cheap across the three social networks. Other findings indicate that WhatsApp’s memorability is easy to execute compared to that of Facebook and Twitter.
A probabilistic data encryption scheme
(international journal sharing platiform ; Journal of Natural Sciences Research, 2013-01-01) Wanambisi, Adrin W.; Maende, cleophas; Muketha, Geoffrey Muchiri; Aywa, Shem
In this paper the author presents a probabilistic encryption scheme that is polynomially secure and has the efficiency of deterministic schemes. From the theoretical construction of Brands and Gill (1996), it is clear that the proof of Pseudo randomness of the quadratic residue generator is complete if it can be shown that there exists a one-way function under the possible assumption that it is infeasible to solve the quadratic residuacity problem provided the factorization of the composite integer is unknown
A review of algorithms for determination of attackability metrics
(Journal of Emerging Trends in Computing and Information Sciences, 2014) Mbuguah, Samuel Mungai; Muketha, Geoffrey Muchiri; Wabwoba, Franklin
Attackability is a concept proposed recently in literature to measure the extent that a software system or service could be the target of a successful attack. A Holistic predictive attackability metrics model has been proposed in our previous study. Metrics derived from this model, their theoretical and empirical validation were proposed and evaluated. The method of measurement of these metrics is largely manual this paper illustrates algorithms that can be adopted with suitable tools to automate the collections of the attackability metrics.
A review of mobile social software awareness and utilization
(International Journal of Science and Research, 2016) Masese, Nelson; Mbuguah, Samuel Mungai; Muketha, Geoffrey Muchiri
Social software comprises a wide range of different types of activities, The most familiar are likely to be internet discussion forums, social networking and dating sites Mobile computing technologies and social software have given new challenges to technology enhanced awareness. Simple awareness system include knowing how the given system works. The objectives of the paper include To establish the level of awareness of mobile social systems, To identify commonly used tools in mobile social systems and To establish the level of utilization of using mobile social systems. This paper reviews the awarness of mobile social softare that includes Facebook,Whattapp, Twitter and linkedIn, Instagram. Primary data was used drawn from mobile social users in Nakuru County Kenya. The sample size was 361 respondents but 345 respodents returned the feed back , both descriptive and inferential statistics was used . It is evident from the study that out of More than 53.3% of respondents use WhatApp while 31.9% use facebook ,7.8% use twitter while Linkedin has lower ratings of 7.0%. The study also reveals that most of the respodents are aware of the services they utilize the service for chatting purpose with 80% followed by Messaging with 18.6 % while research work is the last one with 11%.
Social attackability metrics for software systems
(International Journal of Information and Communication Technology Research, 2013) Mbuguah, Samuel Mungai; Mwangi, Waweru; Song, Pang Chol; Muketha, Geoffrey Muchiri
Software based system have become ubiquitous in modern day activities. Software system based system are being increasing attacked, leading to the need for software system administrators, and managers to have some metrics at predicting the social engineering attackability of a such system. Researchers have identified seven human traits/attributes that make human susceptible to social engineering attacks. Yet they did not model nor come up metrics. The author has published a conceptual a holistic predictive attackability metric model and corresponding metrics to assist the system designers. The model considers the technical metrics based on cohesion, coupling and complexity as used to predict attackability. It also consider the social metrics based on human traits that make the human operators become susceptible to social engineering attacks. The identified human traits are dishonesty, social compliance, Kindness,Time pressure, Herd mentality, greed/need and distraction. This paper considers only the social metrics part of the model.To measure human traits the authors relies on the HEXACO model and Big Five personality trait models. In these model the personality trait are measured using a ranking scale based on Lickert scale. Hence each trait is measured as a percentile. However, for purpose of this paper, to postulate the metric the author considered the discrete case. Why the value of trait take either a value of “1” or “0”. To determine the relationship between traits between and attackability experts were asked to assess the trait versus attackability from which after aggregating for all traits a social attackability metrics was determined. To determine the predictive social attackability metrics each trait was considered to be equally likely to occur and hence a probability of 1/7 and this acts as factor to transform the social attackability metric into predictive attackability metrics.