Titlebook: Security in Computing and Communications; 6th International Sy Sabu M. Thampi,Sanjay Madria,Jose M. Alcaraz Caler Conference proceedings 20

孵卵器

Investigating Deep Learning for Collective Anomaly Detection - An Experimental Study,aches so far proposed for DoS (Denial of Service) attack detection with the aid of collective anomaly detection are unsupervised in nature. Due to this reason, often those approaches show high false alarm rates. To reduce the high false alarm rate, we have done some experiments to investigate the su

Cpap155

BVD - A Blockchain Based Vehicle Database System,for any kind of transaction in terms of goods, services and many more. This technology serves as a secure and transparent platform for the users who wants to change their environment from a centralized to a decentralized platform. Any relevant application which benefits from a decentralized platform

Conquest

特征

Security Threats Against LTE Networks: A Survey,itives for authentication and encryption to plug the security holes of earlier generations. But the introduction of IP-based full inter-networking in LTE has increased the attack-surface. Despite strong authentication and encryption, there are still some messages being exchanged over the air, betwee

browbeat

Network Anomaly Detection Using Artificial Neural Networks Optimised with PSO-DE Hybrid,y due to the growing Internet is the driving force for coming up with new techniques for detecting anomalies in network behaviour. In this paper, Artificial Neural Network (ANN) model optimised with a hybrid of Particle Swarm Optimiser (PSO) and Differential Evolution (DE) is proposed to monitor the

indubitable

Militia

辩论的终结

Cost Based Model for Secure Health Care Data Retrieval,sers as chunks by introducing metadata concept. However, data retrieval requires authentication of users and the queries put forth by the user, for which a proper costing mechanism involving both user and query is introduced in this work using linear optimization concept.

forager

存心

Statistical Comparison of Opinion Spam Detectors in Social Media with Imbalanced Datasets,rformance of Twitter spam detection using multiple approaches of single and ensemble classifiers. Besides ensemble-based learning (Bagging and Random forest), we apply the SMOTE oversampling technique to improve detection performance especially for classifiers sensitive to imbalanced datasets.