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Abstract:The critical process of hiring has relatively recently been ported to the cloud. Specifically, the automated systems responsible for completing the recruitment of new employees in an online fashion, aim to make the hiring process more immediate, accurate and cost-efficient. However, the online exposure of such traditional business procedures has introduced new points of failure that may lead to privacy loss for applicants and harm the reputation of organizations. So far, the most common case of Online Recruitment Frauds (ORF), is employment scam. Unlike relevant online fraud problems, the tackling of ORF has not yet received the proper attention, remaining largely unexplored until now. Responding to this need, the work at hand defines and describes the characteristics of this severe and timely novel cyber security research topic. At the same time, it contributes and evaluates the first to our knowledge publicly available dataset of 17,880 annotated job ads, retrieved from the use of a real-life system.

With the mushrooming of wireless access infrastructures, the amount of data generated, transferred and consumed by the users of such networks has taken enormous proportions. This fact further complicates the task of network intrusion detection, especially when advanced machine learning (ML) operations are involved in the process. In wireless environments, the monitored data are naturally distributed among the numerous sensor nodes of the system. Therefore, the analysis of data must either happen in a central location after first collecting it from the sensors or locally through collaboration by viewing the problem through a distributed ML perspective. In both cases, concerns are risen regarding the requirements of this demanding task in matters of required network resources and achieved security/privacy. This paper proposes TermID, a distributed network intrusion detection system that is well suited for wireless networks. The system is based on classification rule induction and swarm intelligence principles to achieve efficient model training for intrusion detection purposes, without exchanging sensitive data. An additional achievement is that the produced model is easily readable by humans. While these are the main design principles of our approach, the accuracy of the produced model is not compromised by the distribution of the tasks B Constantinos Kolias and remains at competitive levels. Both the aforementioned claims are verified by the results of detailed experiments withheld with the use of a publicly available security-focused wireless dataset.

The IEEE 802.16 technology, commonly referred to as WiMAX, gains momentum as an option for broadband wireless communication access. So far, several research works focus on the security of the 802.16 family of standards. In this context, the contribution of this paper is twofold. First, it provides a comprehensive taxonomy of attacks and countermeasures on 802.16. Each attack is classified based on several factors, e.g. its type, likelihood of occurrence, impact upon the system etc. and its potential is reviewed with reference to the standard. Possible countermeasures and remedies proposed for each category of attacks are also discussed to assess their effectiveness. Second, a full-scale assessment study of indicative attacks that belong to broader attack classes is conducted in an effort to better comprehend their impact on the 802.16 realm. As far as we are aware of, this is the first time an exhaustive and detailed survey of this kind is attempted.