The blackhole attack is one of the simplest yet effective attacks that target the AODV protocol. Blackhole attackers exploit AODV parameters in order to win route requests, and thus, attract traffic, which they subsequently capture and drop. However, the first part of the attack is often neglected in present literature, while the majority of attempts in detection focus only on the second part of the attack (i.e., packet drop). This paper provides a comprehensive analysis of the blackhole attack, focusing not only on the effects of the attack, but also on the exploitation of the route discovery process. As a result, a new critical attack parameter is identified (i.e., blackhole intensity), which quantifies the relation between AODV's sequence number parameter and the performance of blackhole attacks. In addition, a novel blackhole detection mechanism is also proposed. This mechanism utilizes a dynamic threshold cumulative sum (CUSUM) test in order to detect abrupt changes in the normal behavior of AODV's sequence number parameter. A key advantage of the proposed mechanism is its ability to accurately detect blackhole attacks with a minimal rate of false positives, even if the malicious node selectively drops packets.

Nowadays, the majority of web platforms in the Internet originate either from CMS to easily deploy websites or by web applications frameworks that allow developers to design and implement web applications. Considering the fact that CMS are intended to be plug and play solutions and their main aim is to allow even non-developers to deploy websites, we argue that the default hashing schemes are not modified when deployed in the Internet. Also, recent studies suggest that even developers do not use appropriate hash functions to protect passwords, since they may not have adequate security expertise. Therefore, the default settings of CMS and web applications frameworks play an important role in the security of password storage. This paper evaluates the default hashing schemes of popular CMS and web application frameworks. First, we formulate the cost time of password guessing attacks and next we investigate the default hashing schemes of popular CMS and web applications frameworks. We then apply our framework to perform a comparative analysis of the cost time of password guessing attacks between the various CMS and web application frameworks. Finally, considering that intensive hash functions consume computational resources, we analyze hashing schemes from a different perspective. That is, we investigate if it is feasible and under what conditions to perform slow rate denial of service attacks from concurrent login attempts. Through our study we have derived a set of critical observations. The conjecture is that that the security status of the hashing schemes calls for changes with new security recommendations and updates to the default security settings.

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Introduction: Individuals and healthcare providers need to trust that the EHRs are protected and that the confidentiality of their personal information is not at stake. Aim: Within CrowdHEALTH project, a security and privacy framework that ensures confidentiality, integrity, and availability of the data was developed. Methods: The CrowdHEALTH Security and Privacy framework includes Privacy Enhancing Technologies (PETs) in order to comply with the GDPR EU laws of data protection. CrowdHEALTH deploys OpenID Connect, an authentication protocol to provide flexibility, scalability, and lightweight user authentication as well as the attribute-base access control (ABAC) mechanism which supports creating efficient access control policies. Results: CrowdHEALTH integrates ABAC with OpenID Connect to build an effective and scalable base for end-users' authorization. CrowdHEALTH's security and privacy framework interacts with other CrowdHEALTH's components, for instance the Big Data Platform, that depends on user authentication and authorization. CrowdHEALTH users are able to access the CrowdHEALTH's database based on the result of an ABAC request. Moreover, due to the fact that the CrowdHEALTH system requires proofs during the interactions with data producers of low trust or low reputation level, the requirements for the Trust and Reputation Model have been identified. Conclusion: The CrowdHEALTH Integrated Holistic Security and Privacy framework meets the security criteria for an e-health cross-border system, due to the adoption of security mechanisms, such as user authentication, user authorization, access control, data anonymization, trust management and reputation modelling. The implemented framework remains to be tested to ensure its robustness and to evaluate its performance. The holistic security and privacy framework might be adapted during the project's life circle according to new legislations.