The tremendously growing problem of phishing e-mail, also known as spam including spear phishing or spam borne malware, has demanded a need for reliable intelligent anti-spam e-mail filters. This survey paper describes a focused literature survey of Artificial Intelligence (AI) and Machine Learning (ML) methods for intelligent spam email detection, which we believe can help in developing appropriate countermeasures. In this paper, we considered 4 parts in the email’s structure that can be used for intelligent analysis: (A) Headers Provide Routing Information, contain mail transfer agents (MTA) that provide information like email and IP address of each sender and recipient of where the email originated and what stopovers, and final destination. (B) The SMTP Envelope, containing mail exchangers’ identification, originating source and destination domains\users. (C) First part of SMTP Data, containing information like from, to, date, subject – appearing in most email clients (D) Second part of SMTP Data, containing email body including text content, and attachment. Based on the number the relevance of an emerging intelligent method, papers representing each method were identified, read, and summarized. Insightful findings, challenges and research problems are disclosed in this paper. This comprehensive survey paves the way for future research endeavors addressing theoretical and empirical aspects related to intelligent spam email detection.

Threats are potential events, intentional or not, that compromise the confidentiality, integrity, and/or availability of information systems. Defending against threats and attacks requires actionable threat intelligence. Using this intelligence to minimise risk, requires a systematic methodology or framework that recognises every possible threat scenario. This can be done with Threat Modelling (TM), which assists with identifying, understanding and providing visibility of threats affecting an organisation. The focus of this study is to determine TM limitations, strengths, and any perceivable gaps. It has also focused on identifying any possible enhancements that may improve TM performance and efficiency when modelling sophisticated attacks such as Advanced Persistent Threats (APT).

Breast cancer is the most diagnosed cancer in Australia with crude incidence rates increasing drastically from 62.8 at ages 35-39 to 271.4 at ages 50-54 (cases per 100,000 women). Various researchers have proposed methods and tools based on Machine Learning and Convolutional Neural Networks for assessing mammographic images, but these methods have produced detection and interpretation errors resulting in false-positive and false-negative cases when used in the real world. We believe that this problem can potentially be resolved by implementing effective image pre-processing techniques to create training data for Deep-CNN. Therefore, the main aim of this research is to propose effective image pre-processing methods to create datasets that can save computational time for the neural network and improve accuracy and classification rates. To do so, this research proposes methods for background removal, pectoral muscle removal, adding noise to the images, and image enhancements. Adding noise without affecting the quality of details in the images makes the input images for the neural network more representative, which may improve the performance of the neural network model when used in the real world. The proposed method for background removal is the “Rolling Ball Algorithm” and “Huang’s Fuzzy Thresholding”, which succeed in removing background from 100% of the images. For pectoral muscle removal “Canny Edge Detection” and “Hough’s Line Transform” are used, which removed muscle from 99.06% of the images. “Invert”, “CTI_RAS” and “ISOCONTOUR” lookup tables (LUTs) were used for image enhancements to outline the ROIs and regions within the ROIs.

Sulphate-reducing bacteria (SRB) are dominant species causing corrosion of various types of materials. However, they also play a beneficial role in bioremediation due to their tolerance of extreme pH conditions. The application of sulphate-reducing bacteria (SRB) in bioremediation and control methods for microbiologically influenced corrosion (MIC) in extreme pH environments requires an understanding of the microbial activities in these conditions. Recent studies have found that in order to survive and grow in high alkaline/acidic condition, SRB have developed several strategies to combat the environmental challenges. The strategies mainly include maintaining pH homeostasis in the cytoplasm and adjusting metabolic activities leading to changes in environmental pH. The change in pH of the environment and microbial activities in such conditions can have a significant impact on the microbial corrosion of materials. These bacteria strategies to combat extreme pH environments and their effect on microbial corrosion are presented and discussed.