An analysis on the relation between users' online social networks addiction and users security concerns

Use of online social network platforms has increased over last decades. There are various activities that users can do on those platforms such as, making friends, enjoying time, making business, and education. Given activities make online social network platforms more attractive and users want to spend more time on those platforms. Although there is a massive increment in their use, they are not secure enough to fully protect their users' data and privacy. Some users are not aware of the security settings (i.e. privacy settings) since most users focus on spending time on those platforms which brings online social networks addiction into the consideration. Addiction is defined with time dependency in most of the literature works, however, calling a person as an addicted person depends on various factors. This work provides three main contributions; 1-) It clarifies the definition of addiction with a quantitative model. 2-) It provides an analysis on online social networks addiction; answers the question "whom could be called as an addicted user to those platforms" 3-)It provides an analysis on users' trusts to online social networks platforms

Fuzzy rule based classification system from vehicle-to-grid data

Vehicle-to-Grid (V2G) system is becoming a very popular concept since it has various benefits such as reducing energy consumption, being environmental friendly, bi-directional charging, and load balancing. Although, it gets highly remarkable and has many advantages, V2G system's security is extremely challenging. Any security flaw in V2G system can cause serious issues on the system. Security issues might open doors to severe damages on the system. One of the most danger damage on such systems is disclosed confidential information. This work therefore analyses what are confidential information features in a V2G system, it then analyses whether a V2G system is vulnerable to attacks or not if the system's confidential information is revealed. To do that, this study used fuzzy-classification technique in which a fuzzy system is developed. It also applied SVM and NB classification techniques in order to compare applied classification techniques in terms of their performances. Comparison results showed that fuzzy-classification technique performed better than other two techniques

Selecting the best forensic report by using a group decision making method: A case study on three forensic reports

Group decision making (GDM) techniques have been very popular to take the best and the most convenient decision from an alternative set. GDM techniques have been applied in various area of information sharing as well as forensic information sharing. In a forensic investigation process, different forensic reports are produced by different investigators. Produced forensic reports are taken to the court as a file of evidence however deciding which report should be taken to the court is a challenging. Because decision is single handed, officer makes decision. There are two main approaches s/he might follow in selection; the first one is to combine the forensic reports into one report if investigators are located in the same environment, the other one is to choose the most comprehensive report (individually decided). Both approaches may cause continuance by undue process because of insufficient evidence because reports which are not taken to the court might include the needed evidence. In order to solve such problems in forensic area, this work provides a consensus-reached GDM approach in which extended induced ordered weighted average technique is used. Three forensic master students' forensic reports are used for application of the work. The result of the application phase showed that consensus-reached GDM makes the taken decision better and more accurate