The pervasiveness of wireless communications networks is advancing particularly in metropolitan areas. Broadband computer networks as IEEE 802.11 are seriously competing with cellular network technologies such as UMTS and HSDPA. Unfortunately, this increased mobility comes with privacy and security related issues. We are currently in the process of identifying possible attacks on the privacy of wireless network users, since the development of effective countermeasures is only possible with a thorough understanding of such attacks.

User Tracking Based on Behavioral Fingerprints

Cloud computing is a model for networking universal, easy and on demand access to a set of computing resources that can be quickly interfering with minimum labor, or require the service provider to be provisioned.

A new method for evaluating cloud computing user behavior trust

.................................................................................................................................. iii ACKNOWLEDGEMENTS ............................................................................................................ v LIST OF TABLES ....................................................................................................................... viii LIST OF FIGURES ........................................................................................................................ x

Detecting Insider and Masquerade Attacks by Identifying Malicious User Behavior and Evaluating Trust in Cloud Computing and IoT Devices

Example implementations relate to static program analysis. For example, an apparatus includes a processor to perform static program analysis on a set of processor executable instructions associated with an object-relational mapping (ORM) framework. The first set of processor executable instructions includes an object. The processor is also to generate a propagation path of the object based on an execution flow of the object. The propagation path includes a first node and a second node. The first node corresponds to a first ORM operation to store the object in a database. The second node corresponds to a second ORM operation to retrieve the object from the database. The second node is linked to the first node based on a common attribute of the object. In response to a determination that the propagation path includes a sink, the processor is to output a security risk warning.

Static program analysis in an object-relational mapping framework

In role based trust management, a trust domain will decide the trustworthiness of a user by assigning roles to the user according to submitted certificates. However, the userspsila trustworthiness should be different after they enforced different behaviors although being assigned the same role. A behavior-aware role based trust management model is presented in this paper. Role is extended to include variables that represent cumulative behaviors. Behavior-aware credentials are introduced to describe the trust movement policy driven by enforced behaviors. Role Reputation and domain reputation are also defined using behavior variables (BV) that represent the current trust status. The reputation-aware credentials describe the trust movement policy driven by the change of reputation. Some optimization mechanisms of implementing the model are discussed finally.

Behavior-Aware Role Based Trust Management

Pervasive computing systems are becoming widespread as the next generation of computing systems. In pervasive computing systems, the users are frequently hopping from one computing space to another, which makes user profile management an essential component of the system. Because such a dynamic-populated computing system has to be new corner-friendly, malicious users with bad reputation to a system may take advantage of the system by claiming themselves as new users. In addition, user identity can also be compromised by controlling the victim node via normal invasions or even by inside identity thefts. A valid user identity is the foundation and prerequisite of trust. Failures to correctly identify a user are catastrophic to trust management system. Traditional intrusion detection systems can only detect an attack when it already happens. But before the intrusion the attackers may have some specific behavioral signatures indicating of their suspicious identities. We need a more powerful and practical system to identify these suspicious behaviors and signal an early warning to stop them. In this paper we assume that user identity is compromised either by attacking and controlling a node or by malicious users' claiming themselves as new users. We introduced a novel user behavior identification and profile management system to detect the suspicious behaviors, and a dynamically triggered reaction module to monitor the suspicious node and collect the necessary evidences

Dichao Peng

Papers

User Behavior Identification for Trust Management in Pervasive Computing Systems