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Agile software design and development methodologies have been gaining rigorous attention in the software engineering research community since their early introduction in the mid-nineties in addition to being highly adopted by the software development industry. In the last 15 years, an excessive number of research studies have been conducted on agile methods, a great number of notable methods have been proposed and various surveys have been presented by many researchers. In this study, the authors intend to conduct a literature survey study of the surveys of the different agile methodologies ranging from January 2000 to December 2015 using an intuitive research methodology called "Compare and Review" (CR). Furthermore, these survey papers were classified into four major categories according to their area of study. Additionally, the newly proposed agile methodologies that have not been addressed yet in any other literature review were reviewed and compared in terms of where the changes that they proposed lay on the SDLC.

Spam is no longer just commercial unsolicited email messages that waste our time, it consumes network traffic and mail servers' storage. Furthermore, spam has become a major component of several attack vectors including attacks such as phishing, cross-site scripting, cross-site request forgery and malware infection. Statistics show that the amount of spam containing malicious contents increased compared to the one advertising legitimate products and services. In this paper, the issue of spam detection is investigated with the aim to develop an efficient method to identify spam email based on the analysis of the content of email messages. We identify a set of features that have a considerable number of malicious related features. Our goal is to study the effect of these features in helping the classical classifiers in identifying spam emails. To make the problem more challenging, we developed spam classification models based on imbalanced data where spam emails form the rare class with only 16.5% of the total emails. Different metrics were utilized in the evaluation of the developed models. Results show noticeable improvement of spam classification models when trained by dataset that includes malicious related features.

Radio Frequency Identification (RFID) is one of the most popular Automatic Identification and Data Capture (AIDC) technologies that facilitate objects identification and information exchange over relatively small and widely separated entities. In this paper, the main aim is to address the privacy and security challenges that RFID Access Control Systems face and solve these challenges without relying on back-end database but only the RF subsystem.

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Advancements in machine learning and artificial intelligence have been widely utilised in the security domain, including but not limited to intrusion detection techniques. With the large training datasets of modern traffic, intelligent algorithms and powerful machine learning tools, security researchers have been able to greatly improve on the intrusion detection models and enhance their ability to detect malicious traffic more accurately. Nonetheless, the problem of detecting completely unknown security attacks is still an open area of research. The enormous number of newly developed attacks constitutes an eccentric challenge for all types of intrusion detection systems. Additionally, the lack of a standard definition of what constitutes an unknown security attack in the literature and the industry alike adds to the problem. In this paper, the researchers reviewed the studies on detecting unknown attacks over the past 10 years and found that they tended to use inconsistent definitions. This formulates the need for a standard consistent definition to have comparable results. The researchers proposed a new categorisation of two types of unknown attacks, namely Type-A, which represents a completely new category of unknown attacks, and Type-B, which represents unknown attacks within already known categories of attacks. The researchers conducted several experiments and evaluated modern intrusion detection systems based on shallow and deep artificial neural network models and their ability to detect Type-A and Type-B attacks using two well-known benchmark datasets for network intrusion detection. The research problem was studied as both a binary and multi-class classification problem. The results showed that the evaluated models had poor overall generalisation error measures, where the classification error rate in detecting several types of unknown attacks from 92 experiments was 50.09%, which highlights the need for new approaches and techniques to address this problem.

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