Mohd Zalisham Jali

Quantifying the Effect of Graphical Password Guidelines for Better Security

Authentication using images or graphical passwords is one of the possible alternatives for traditional authentication based upon passwords. This study aims to investigate the practicality of giving guidelines or advice to users before they start choosing their image passwords, the effectiveness of using a smaller tolerance (clickable areas) and the optimum combination of click and image passwords. An alternative graphical prototype known as the Enhanced Graphical Authentication Scheme (EGAS) was developed in order to achieve these aims which implemented two different types of data collection (internal and external). From the findings, both internal and external groups indicated that the implementation of guidelines alone cannot guarantee the security of image passwords created by participants; but, in combination with other usability measurements this study has shown positive outcomes.

A Review of Feature Extraction Optimization in SMS Spam Messages Classification

Spam these days has become a definite nuisance to mobile users. Provision of Short Messages Services (SMS) has been intruded, in line with an advancement of mobile technology by the emergence of SMS spam. This issue has not only cause distressing situation but also other serious threats such as money loss, fraud, and false news. The focus of this study is to excavate the features extraction in classifying SMS spam messages at users’ end. Its objective is to study the discriminatory control of the features and considering its informative or influence factor in classifying SMS spam messages. This study has been conducted by gathering research papers and journals from numerous sources on the subject of spam classification. The discovery offers a motivational effort for further execution in a wider perspective of combating spam such as measurement of spam’s risk level.

A New SMS Spam Detection Method Using Both Content-Based and Non Content-Based Features

SMS spamming is an activity of sending ‘unwanted messages’ through text messaging or other communication services; normally using mobile phones. Nowadays there are many methods for SMS spam detection, ranging from the list-based, statistical algorithm, IP-based and using machine learning. However, an optimum method for SMS spam detection is difficult to find due to issues of SMS length, battery and memory performances. Hoping to minimize the aforementioned problems, this paper introduces another detection variance that is based on common characters used when sending SMS (i.e. numbers and symbols), SMS length and keywords. To verify our work, the proposed features were stipulated into five different algorithms and then, tested with three different datasets for their ability to detect spam. From the conduct of experiments, it can be suggested that these three features are reasonable to be used for detecting SMS spam as it produced positive results. In the future, it is anticipated that the proposed algorithm will perform better when combined with machine learning techniques.

Web Accessibility Challenges

Despite the importance of web accessibility in recent years, websites remain partially or completely inaccessible to certain sectors of the population. This is due to several reasons, including web developers’ little or no experience in accessibility and the lack of accurate information about the best ways to quickly and easily identify accessibility problems using different Accessibility Evaluation Methods (AEMs). This paper surveys accessibility literature and presents a general overview of the primary challenges of accessibility barriers on websites. In this sense, we critically investigate main challenges forms related to accessibility including standards and guidelines (WCAG 2.0), during website’s design and development and during evaluation. Finally, a set of recommendations such as enforcing accessibility legislations are presented to overcome some challenges.

A classification on brain wave patterns for Parkinson’s patients using WEKA

In this paper, classification of brain wave using real-world data from Parkinson’s patients in producing an emotional model is presented. Electroencephalograph (EEG) signal is recorded on eleven Parkinson’s patients. This paper aims to find the “best” classification for brain wave patterns in patients with Parkinson’s disease. This work performed is based on the four phases, which are first phase is raw data and after data processing using statistical features such as mean and standard deviation. The second phase is the sum of hertz, the third is the sum of hertz divided by the number of hertz, and last is the sum of hertz divided by total hertz. We are using five attributes that are patients, class, domain, location, and hertz. The data were classified using WEKA. The results showed that BayesNet gave a consistent result for all the phases from multilayer perceptron and K-Means. However, K-Mean gave the highest result in the first phase. Our results are based on a real-world data from Parkinson’s patients.

Markov-modulated Bernoulli-based performance analysis for BLUE algorithm under bursty and correlated traffics

In this study, the discrete-time performance of BLUE algorithms under bursty and correlated traffics is analyzed using two-state Markov-modulated Bernoulli arrival process (BLUE-MMBP-2). A two-dimensional discrete-time Markov chain is used to model the BLUE algorithm for two traffic classes, in which each dimension corresponds to a traffic class and the parameters of that traffic class. The MMBP is used to replace the conventional and widely-used Bernoulli process (BP) in evaluating and proposing analytical models based on the BLUE algorithm. The BP captures neither the traffic correlation nor the burstiness. The proposed approach is simulated, and the obtained results are compared with that of the BLUE-BP, which can modulate a single traffic class only. The comparison is performed in terms of mean queue length (mql), average queuing delay (D), throughput, packet loss, and dropping probability (DP). The results show that during congestion, particularly heavy congestion under bursty and correlated traffics, the BLUE-MMBP-2 algorithm provides better mql, D, and DP than the BLUE-BP.

Designing a new E-Commerce authentication framework for a cloud-based environment

The functional need of executing transaction on Electronic Commerce has made security a necessary built-in feature. Also, placing the E-Commerce portal in cloud through cloud computing technologies has undoubtedly increased the security challenges. Thus necessitate a design of secured authentication framework to be employed by the web-based E-Commerce portals. Existing works on XML-based authentication and comparison with non-XML signature-based security mechanism showed the strength in employing XML digital signature approach in authenticating Business to Consumer E-Commerce. Also the review revealed the limitations in the previously applied XML signature and its application approach. Therefore, this paper aims to design a novel algorithmic security framework for authentication using XML digital signature in conjunction with PKI standard using elliptic curve in B2C cloud-based E-Commerce portal. The novel algorithmic framework will be validated through simulation with C++ as the programming language using Microsoft Windows Environment. The outcome of this research will be an improved authentication mechanism for XML digital signature.

The Design and Development of Spam Risk Assessment Prototype: In Silico of Danger Theory Variants

Now-a-days, data is flowing with various types of information and it is absolutely enormous and moreover, it is in unstructured form. These raw data is meaningless unless it is processed and analyzed to retrieve all the valuable and meaningful information. In this paper, a design and principal functionalities of the system prototype is introduced. A process of information retrieval by applying the text mining with Artificial Immune System (AIS) is proposed to discover the possible level of severity for a Short Messaging Service (SMS) spam. This is expected to be a potential tool in retrieving an implicit danger that a spam might impact to the recipients. Furthermore, the development of this tool can be considered as an emergence of another data mining tool that also exceedingly possible to be embedded with another existing tool.

Password Recovery Using Graphical Method

Authentication with images or better known as graphical password is gaining its recognition as an alternative method to authenticate users, for it is claimed that images or pictures are easier to use and remember. The same method can be applied to password recovery, with the purpose to ease the process of users in regaining their account in case of forgotten passwords. A total of 30 participants were asked to use a prototype implementation of graphical password recovery and provide feedbacks. The data gained were analyzed in terms of attempts, timing, pattern, and user feedback. Overall, it was found that participants had no problem in using graphical password recovery despite they were new to it. Most of them preferred the choice-based method, even though they agreed that it provided less security. Graphical recovery has potential to be used more widely in current technology, although more works need to be done to balance the issues of usability and security.

Comparative Analysis of Danger Theory Variants in Measuring Risk Level for Text Spam Messages

The issue of spam has been uprising since decades ago. Impact loss from various aspects has attacked the daily life most of us. Many approaches such as policy and guidelines establishment, rules and regulations enforcement, and even anti-spam tools installation appeared to be not enough to restrain the problem. To make things even worse, the spam’s recipients still easily get enticed and lured with the spam content. Hence, an advanced medium that acts as an implicit decision maker is desperately required to assist users to obstruct their eagerness responding against spam. The simulation of spam risk assessment in this paper is purposely to give some insights of how users can identify the imminent danger of received text spam. It is demonstrated by predicting the potential hazard with three different levels of risk (high, medium and low), according to its possible impact loss. A series of simulation has been conducted to visualize this concept using Danger Theory variants of Artificial Immune Systems (AIS), namely Dendritic Cell Algorithm (DCA) and Deterministic Dendritic Cell Algorithm (dDCA). The corpus of messages from UCI Machine Learning Repository has been deployed to illustrate the analysis. The outcome of these simulations verified that dDCA has consistently outperformed DCA in precisely assessing the risk level with severity concentration value for text spam messages. The findings of this work has demonstrated the feasibility of immune theory in risk measurement that eventually assisting users in their decision making.