Visible to the public Biblio

Filters: Author is Nawaz, A.  [Clear All Filters]
Shakeel, M., Saeed, K., Ahmed, S., Nawaz, A., Jan, S., Najam, Z..  2020.  Analysis of Different Black Hole Attack Detection Mechanisms for AODV Routing Protocol in Robotics Mobile AdHoc Networks. 2020 Advances in Science and Engineering Technology International Conferences (ASET). :1–6.
Robotics Mobile Ad-hoc Networks (MANETs) are comprised of stations having mobility with no central authority and control. The stations having mobility in Robotics MANETs work as a host as well as a router. Due to the unique characteristics of Robotics MANETs such type of networks are vulnerable to different security attacks. Ad-hoc On-demand Distance Vector (AODV) is a routing protocol that belongs to the reactive category of routing protocols in Robotics MANETs. However, it is more vulnerable to the Black hole (BH) attack that is one of the most common attacks in the Robotics MANETs environment. In this attack during the route disclosure procedure a malicious station promotes itself as a most brief path to the destination as well as after that drop every one of the data gotten by the malicious station. Meanwhile the packets don't reach to its ideal goal, the BH attack turns out to be progressively escalated when a heap of malicious stations attack the system as a gathering. This research analyzed different BH finding as well as removal mechanisms for AODV routing protocol.
Nawaz, A., Gia, T. N., Queralta, J. Peña, Westerlund, T..  2019.  Edge AI and Blockchain for Privacy-Critical and Data-Sensitive Applications. 2019 Twelfth International Conference on Mobile Computing and Ubiquitous Network (ICMU). :1—2.
The edge and fog computing paradigms enable more responsive and smarter systems without relying on cloud servers for data processing and storage. This reduces network load as well as latency. Nonetheless, the addition of new layers in the network architecture increases the number of security vulnerabilities. In privacy-critical systems, the appearance of new vulnerabilities is more significant. To cope with this issue, we propose and implement an Ethereum Blockchain based architecture with edge artificial intelligence to analyze data at the edge of the network and keep track of the parties that access the results of the analysis, which are stored in distributed databases.