Nudging folks towards stronger password choices:providing certainty is the key

Persuading people to choose strong passwords is challenging. One way to influence password strength, as and when people are making the choice, is to tweak the choice architecture to encourage stronger choice. A variety of choice architecture manipulations i.e. “nudges”, have been trialled by researchers with a view to strengthening the overall password profile. None has made much of a difference so far. Here we report on our design of an influential behavioural intervention tailored to the password choice context: a hybrid nudge that significantly prompted stronger passwords.We carried out three longitudinal studies to analyse the efficacy of a range of “nudges” by manipulating the password choice architecture of an actual university web application. The first and second studies tested the efficacy of several simple visual framing “nudges”. Password strength did not budge. The third study tested expiration dates directly linked to password strength. This manipulation delivered a positive result: significantly longer and stronger passwords. Our main conclusion was that the final successful nudge provided participants with absolute certainty as to the benefit of a stronger password, and that it was this certainty that made the difference

TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-based Intrusion Detection System

Intrusion detection systems (IDS) play a pivotal role in computer security by discovering and repealing malicious activities in computer networks. Anomaly-based IDS, in particular, rely on classification models trained using historical data to discover such malicious activities. In this paper, an improved IDS based on hybrid feature selection and two-level classifier ensembles is proposed. An hybrid feature selection technique comprising three methods, i.e. particle swarm optimization, ant colony algorithm, and genetic algorithm, is utilized to reduce the feature size of the training datasets (NSL-KDD and UNSW-NB15 are considered in this paper). Features are selected based on the classification performance of a reduced error pruning tree (REPT) classifier. Then, a two-level classifier ensembles based on two meta learners, i.e., rotation forest and bagging, is proposed. On the NSL-KDD dataset, the proposed classifier shows 85.8% accuracy, 86.8% sensitivity, and 88.0% detection rate, which remarkably outperform other classification techniques recently proposed in the literature. Results regarding the UNSW-NB15 dataset also improve the ones achieved by several state of the art techniques. Finally, to verify the results, a two-step statistical significance test is conducted. This is not usually considered by IDS research thus far and, therefore, adds value to the experimental results achieved by the proposed classifier

Cloud based testing of business applications and web services

This paper deals with testing of applications based on the principles of cloud computing. It is aimed to describe options of testing business software in clouds (cloud testing). It identifies the needs for cloud testing tools including multi-layer testing; service level agreement (SLA) based testing, large scale simulation, and on-demand test environment. In a cloud-based model, ICT services are distributed and accessed over networks such as intranet or internet, which offer large data centers deliver on demand, resources as a service, eliminating the need for investments in specific hardware, software, or on data center infrastructure. Businesses can apply those new technologies in the contest of intellectual capital management to lower the cost and increase competitiveness and also earnings. Based on comparison of the testing tools and techniques, the paper further investigates future trend of cloud based testing tools research and development. It is also important to say that this comparison and classification of testing tools describes a new area and it has not yet been done