CCBS – a method to maintain memorability, accuracy of password submission and the effective password space in click-based visual passwords

Text passwords are vulnerable to many security attacks due to a number of reasons such as the insecure practices of end users who select weak passwords to maintain their long term memory. As such, visual password (VP) solutions were developed to maintain the security and usability of user authentication in collaborative systems. This paper focuses on the challenges facing click-based visual password systems and proposes a novel method in response to them. For instance, Hotspots reveal a serious vulnerability. They occur because users are attracted to specific parts of an image and neglect other areas. Undertaking image analysis to identify these high probability areas can assist dictionary attacks. Another concern is that click-based systems do not guide users towards the correct click-point they are aiming to select. For instance, users might recall the correct spot or area but still fail to include their click within the tolerance distance around the original click-point which results in more incorrect password submissions. Nevertheless, the Passpoints study by Wiedenbeck et al., 2005 inspected the retention of their VP in comparison with text passwords over the long term. Despite being cued-recall the successful rate of their VP submission was not superior to text passwords as it decreased from 85% (the instant retention on the day of registration) to 55% after 2 weeks. This result was identical to that of the text password in the same experiment. The successful submission rates after 6 weeks were also 55% for both VP and text passwords. This paper addresses these issues, and then presents a novel method (CCBS) as a usable solution supported by an empirical proof. A user study is conducted and the results are evaluated against a comparative study

Responsibility and non-repudiation in resource-constrained Internet of Things scenarios

The proliferation and popularity of smart autonomous systems necessitates the development of methods and models for ensuring the effective identification of their owners and controllers. The aim of this paper is to critically discuss the responsibility of Things and their impact on human affairs. This starts with an in-depth analysis of IoT Characteristics such as Autonomy, Ubiquity and Pervasiveness. We argue that Things governed by a controller should have an identifiable relationship between the two parties and that authentication and non-repudiation are essential characteristics in all IoT scenarios which require trustworthy communications. However, resources can be a problem, for instance, many Things are designed to perform in low-powered hardware. Hence, we also propose a protocol to demonstrate how we can achieve the authenticity of participating Things in a connectionless and resource-constrained environment

The Role Which Faculty Members at Al-Hussein Bin Talal University Have the Principles of Brain-Based Learning Theory

The aim of this study was to find out the extent to which the faculty members of Al-Hussein Bin Talal University have the principles of learning theory based on the brain. The study sample consisted of 70 faculty members, of which 54 were male faculty members. The researcher used the questionnaire tool to measure the extent principles of the faculty members of brain-based learning theory. The results showed the following: 1. The faculty members have knowledge of the principles of brain-based learning.It came in the first place with a high degree field (realized that every brain is unique). The field came at the last and intermediate level (I know that the brain is designed to recognize and generate patterns). 2. There are statistically significant differences for faculty members who have more than 10 years' experience to own the principles of brain-based learning theory. 3. There are no statistically significant differences due to gender in the extent of ownership faculty members at Al-Hussein Bin Talal University. Keywords: principles of brain , learning theory. DOI: 10.7176/JEP/11-5-06 Publication date: February 29th 2020

Predicting the Water Situation in Jordan Using Auto Regressive Integrated Moving Average (ARIMA) Model

Countries\u27 water security is inextricably related to their economic position. Jordan is one of the world\u27s five poorest countries regarding water resources. Climate change and water scarcity are threatening Jordan\u27s economic growth and food security. The objectives of the study are to use a statistical artificial intelligence model, which is called the Autoregressive Integrated Moving Average model to predict water productivity in Jordan and the world for the year 2021-2026, based on a real dataset from World Development Indicators from the World Bank. The study also aims to predict the total per capita share of fresh water based on the dataset from the Jordanian Department of Statistics. The dataset is divided into 70% training and 30% testing. The statistical model has an accuracy rate of 93.86%. This study can help analyze and measure the use of water resources in Jordan using artificial intelligence predictions and mitigate this regional problem

Reliability evaluation of scalable complex networks through delta-star conversion

Exact reliability evaluation of large size complex networks becomes intractable with conventional techniques due to the exponential scaling of the computation complexity as the size of network scales up. In this paper we develop a scalable model for the exact evaluation of system reliability of scalable complex networks of the n-tuple bridge type based on scaled delta-star conversion. The number of steps as well as the computation overhead is kept within practical limits as they scale up linearly with the size of the network. The proposed model enables simple numerical evaluation either manually or through spread-sheets

Women Lost, Women Found: Searching for an Arab-Islamic Feminist Identity in Nawal El Saadawi’s \u3cem\u3eTwelve Women in a Cell\u3c/em\u3e in Light of Current Egyptian Spring Events

Dr. Nawal El Saadawi, an Arab feminist, playwright, novelist, and thinker, has been one of the most controversial literary figures in Arab contemporary literature. In this paper, I examine El Saadawi’s 1984 play Twelve Women in A Cell in light of the ongoing political dissidence that gave birth to the recent Arab Spring and its intricate relation to feminist dissidence. The play published twenty-eight years ago, deals with a bizarre situation that surprisingly and sadly, is still relevant to women’s struggle within Arab-Islamic hegemony. The cell that hosts twelve Egyptian women, in El Saadawi’s play, becomes the Arabic Islamic patriarchal world, within which these women struggle as their ultimate goal becomes survival. The cell hosts many types of women like a university professor, a prostitute, an atheist, and a fundamentalist Muslim, who only have their femaleness as the common ground on which they can salvage existence and their salvation

Recovery modeling in MPLS networks

Transmission of QoS based traffic over packet switched network typically requires resource reservation or differentiated treatment to guarantee an acceptable level of performance. But it is also essential to bound the disruption caused by failure of nodes or links for a real time traffic to a limit that is acceptable by the application. In this paper, a simulation platform models the impact of the MPLS recovery/protection schemes on the QoS traffic parameters including disruption time and number of out of order packets arriving at the destination. The simulation considers measures to alleviate drawbacks caused by recovery process

Adaptive Traffic Fingerprinting for Darknet Threat Intelligence

Darknet technology such as Tor has been used by various threat actors for organising illegal activities and data exfiltration. As such, there is a case for organisations to block such traffic, or to try and identify when it is used and for what purposes. However, anonymity in cyberspace has always been a domain of conflicting interests. While it gives enough power to nefarious actors to masquerade their illegal activities, it is also the cornerstone to facilitate freedom of speech and privacy. We present a proof of concept for a novel algorithm that could form the fundamental pillar of a darknet-capable Cyber Threat Intelligence platform. The solution can reduce anonymity of users of Tor, and considers the existing visibility of network traffic before optionally initiating targeted or widespread BGP interception. In combination with server HTTP response manipulation, the algorithm attempts to reduce the candidate data set to eliminate client-side traffic that is most unlikely to be responsible for server-side connections of interest. Our test results show that MITM manipulated server responses lead to expected changes received by the Tor client. Using simulation data generated by shadow, we show that the detection scheme is effective with false positive rate of 0.001, while sensitivity detecting non-targets was 0.016+-0.127. Our algorithm could assist collaborating organisations willing to share their threat intelligence or cooperate during investigations.Comment: 26 page

Analysis of Accident Data and Evaluation of Leading Causes for Traffic Accidents in Jordan

Road safety is a primary concern and goal of highway and traffic engineers worldwide. The road network in Jordan exhibits relatively high traffic volumes, particularly in urban areas and in the Central Business District (CBD) areas of major cities. Jordan ranks one of the top countries worldwide in terms of having higher numbers of road traffic accidents leading to a relatively high number of fatalities and injuries. In the past few years in particular, the number of registered vehicles in Jordan has considerably increased. As a result, traffic volumes and Vehicle Miles of Travel (VMT) have significantly increased leading to deteriorating traffic flows and escalating traffic congestions and jams. Consequently, the number of road traffic accidents has also noticeably increased in Jordan in the past decade. Complete analysis of statistical data obtained for traffic accidents in Jordan was conducted in this study. Evaluation of the possible leading causes of traffic accidents in Jordan was also carried out. Different possible causes along with behaviors of drivers and pedestrians were investigated and correlated with the number of traffic accidents, fatalities and injuries. Jordan was found to have accident, fatality and injury rates that are considerably higher than those of other countries in the world. Nonetheless, as rates with time, the fatality and injury rates seemed to be moving in the right direction. Yet, the number of traffic accidents, fatalities and injuries looked critical. Traffic accidents and casualties were observed to be higher in summer times. More than 90 percent of traffic accidents, fatalities and injuries occurred on roads with speed limits between 40 and 60 km/h. Pedestrians composed the highest percentage of the total numbers of fatalities and injuries. The majority of driver casualties and passenger casualties (fatalities and injuries) belonged to the age group of 18-42 years. On the other hand, the highest percentage of pedestrian casualties belonged to the age group of 0-18 years. However, about 80 percent of the casualties in Jordan were males and only 20 percent were females. “Tailgating” and “not taking safety measurements during driving” were the most two important driver behaviors in terms of traffic accidents. Yet, behaviors of “using wrong lane” and “not taking safety measurements during driving” led to the highest percentages of the total number of fatalities and injuries. The majority of the pedestrian fatalities and injuries were in fact walking on road during the time of the accident occurrence and about one third of them were walking on sidewalk. Other behaviors of drivers and pedestrians were also important and created traffic complexity and hazardous situations leading to a reduction in saturation flow rates and in capacities and causing bottleneck conditions and traffic jams; hence resulting in traffic safety concerns

Hypothesis of Interaction: Reflections on its Theoretical and Practical Contributions for Second Language Acquisition (SLA)

This article is about one of the most influential hypothesis in the fields of applied linguistics and language learning. It is based on the work of a number of scholars who contributed to the understanding of this hypothesis such as Steve Krashen, Mike Long, Teresa Pica and Merrill Swain. It starts with a brief introduction about the significance of interaction hypothesis generally in language learning in general and its central role in second language acquisition (SLA). The next section reviews some of the fundamental works and studies that have investigated the theoretical and practical understanding of this phenomenon and its relationship to learners’ achievement. It also highlights the contribution of interaction hypothesis to learning in two basic areas: noticing and feedback. There are explanatory examples presented in the following section in order to show how interactional modification techniques are used by learners. The last section presents some concluding thoughts pertaining to this topic with a focus on how it can be employed in language learning classrooms