No abstract

One of the most interesting fields nowadays is forensics. This field is based on the works of scientists who study evidence to help the police solve crimes. In the domain of computer science, the crimes within computer forensics are usually network attacks, and most attacks are over the email (the case of this study). Email has become a daily means of communication which is mainly accessible via internet. People receive thousands of emails in their inboxes and mail servers (in which people can find emails in those lists). The aim of this study is to secure email users by building an automatic checking and detecting system on servers to filter the bad emails from the good ones. In this paper, the authors will do a study based on a new method of emails clustering to extract the bad and good ones. The authors use the gain information technique like an algorithm of clustering, whose principle is to calculate the importance of each attribute (in this study, the authors talk about the attributes that constitute the email) to draw the importance tree and at the end extract the clusters.

Recent advances in Information and Communication Technologies have a significant impact on all sectors of the economy worldwide. Digital Agriculture appeared as a consequence of the democratisation of digital devices and advances in artificial intelligence and data science. Digital agriculture created new processes for making farming more productive and efficient while respecting the environment. Recent and sophisticated digital devices and data science allowed the collection and analysis of vast amounts of agricultural datasets to help farmers, agronomists, and professionals understand better farming tasks and make better decisions. In this paper, we present a systematic review of the application of data mining techniques to digital agriculture. We introduce the crop yield management process and its components while limiting this study to crop yield and monitoring. After identifying the main categories of data mining techniques for crop yield monitoring, we discuss a panoply of existing works on the use of data analytics. This is followed by a general analysis and discussion on the impact of big data on agriculture.

The extraction of the user activity is one of the main goals in the analysis of digital evidence. In this paper we present a methodology for extracting this activity by comparing multiple Restore Points found in the Windows XP operating system. The registry copies represent a snapshot of the state of the system at a certain point in time. Differences between them can reveal user activity from one instant to another. The algorithms for comparing the hives and interpreting the results are of high complexity. We develop an approach that takes into account the nature of the investigation and the characteristics of the hives to reduce the complexity of the comparison and result interpretation processes. The approach concentrates on hives that present higher activity and highlights only those differences that are relevant to the investigation. The approach is implemented as a software tool that is able to compare any set of offline hives and categorise the results according to the user needs. The categorisation of the results, in terms of activity will help the investigator in interpreting the results. In this paper we present a general concept of result categorisation to prove its efficiency on Windows XP, but these can be adapted to any Windows versions including the latest versions.