Biblio

In order to solve the problem that the ordinary intrusion detection model cannot effectively identify the increasingly complex, continuous, multi-source and organized network attacks, this paper proposes an Internet of Things attack group identification model to identify the planned and organized attack groups. The model takes the common attack source IP, target IP, time stamp and target port as the characteristics of the attack log data to establish the identification benchmark of the attack gang behavior. The model also combines the spectral clustering algorithm to cluster different attackers with similar attack behaviors, and carries out the specific image analysis of the attack gang. In this paper, an experimental detection was carried out based on real IoT honey pot attack log data. The spectral clustering was compared with Kmeans, DBSCAN and other clustering algorithms. The experimental results shows that the contour coefficient of spectral clustering was significantly higher than that of other clustering algorithms. The recognition model based on spectral clustering proposed in this paper has a better effect, which can effectively identify the attack groups and mine the attack preferences of the groups.

Cloud computing provides so many groundbreaking advantages over native computing servers like to improve capacity and decrease costs, but meanwhile, it carries many security issues also. In this paper, we find the feasible security attacks made about cloud computing, including Wrapping, Browser Malware-Injection and Flooding attacks, and also problems caused by accountability checking. We have also analyzed the honey pot attack and its procedural intrusion way into the system. This paper on overall deals with the most common security breaches in cloud computing and finally honey pot, in particular, to analyze its intrusion way. Our major scope is to do overall security, analyze in the cloud and then to take up with a particular attack to deal with granular level. Honey pot is the one such attack that is taken into account and its intrusion policies are analyzed. The specific honey pot algorithm is in the queue as the extension of this project in the future.

We present data from detailed observations of a “controlled in-the-wild” study of Brain-Computer Interface (BCI) system. During 10 days of demonstration at seven nonspecialized public events, 1563 people learned about the system in various social configurations. Observations of audience behavior revealed recurring behavioral patterns. From these observations a framework of interaction with BCI systems was deduced. It describes the phases of passing by an installation, viewing and reacting, passive and active interaction, group interactions, and follow-up actions. We also conducted semi-structured interviews with the people who interacted with the system. The interviews revealed the barriers and several directions for further research on BCIs. Our findings can be useful for designing the BCIs foxr everyday adoption by a wide range of people.