Presentation attack detection for face recognition on smartphones: a comprehensive review

Even though the field of Face Presentation Attack Detection (PAD) has been around for quite a long time, but still it is quite a new field to be implemented on smartphones. Implementation on smartphones is different because the limited computing power of the smartphones when compared to computers. Presentation Attack for a face recognition system may happen in various ways, using photograph, video or mask of an authentic user’s face. The Presentation Attack Detection system is vital to counter those kinds of intrusion. Face presentation attack countermeasures are categorized as sensor level or feature level. Face Presentation Attack Detection through the sensor level technique involved in using additional hardware or sensor to protect recognition system from spoofing while feature level techniques are purely software-based algorithms and analysis. Under the feature level techniques, it may be divided into liveness detection; motion analysis; face appearance properties (texture analysis, reflectance); image quality analysis (image distortion); contextual information; challenge response. There are a few types of research have been done for face PAD on smartphones. They also have released the database they used for their testing and performance benchmarking

Poseidon: a 2-tier Anomaly-based Network Intrusion Detection System

We present Poseidon, a new anomaly based intrusion detection system. Poseidon is payload-based, and presents a two-tier architecture: the first stage consists of a Self-Organizing Map, while the second one is a modified PAYL system. Our benchmarks on the 1999 DARPA data set show a higher detection rate and lower number of false positives than PAYL and PHAD

Poseidon: a 2-tier Anomaly-based Intrusion Detection System

We present Poseidon, a new anomaly based intrusion detection system. Poseidon is payload-based, and presents a two-tier architecture: the first stage consists of a Self-Organizing Map, while the second one is a modified PAYL system. Our benchmarks on the 1999 DARPA data set show a higher detection rate and lower number of false positives than PAYL and PHAD

Data analytics for modeling and visualizing attack behaviors: A case study on SSH brute force attacks

In this research, we explore a data analytics based approach for modeling and visualizing attack behaviors. To this end, we employ Self-Organizing Map and Association Rule Mining algorithms to analyze and interpret the behaviors of SSH brute force attacks and SSH normal traffic as a case study. The experimental results based on four different data sets show that the patterns extracted and interpreted from the SSH brute force attack data sets are similar to each other but significantly different from those extracted from the SSH normal traffic data sets. The analysis of the attack traffic provides insight into behavior modeling for brute force SSH attacks. Furthermore, this sheds light into how data analytics could help in modeling and visualizing attack behaviors in general in terms of data acquisition and feature extraction

A survey on online monitoring approaches of computer-based systems

This report surveys forms of online data collection that are in current use (as well as being the subject of research to adapt them to changing technology and demands), and can be used as inputs to assessment of dependability and resilience, although they are not primarily meant for this use