Biblio

Internet of Things (IoT) distributed secure data management system is characterized by authentication, privacy policies to preserve data integrity. Multi-phase security and privacy policies ensure confidentiality and trust between the users and service providers. In this regard, we present a novel Two-phase Incentive-based Secure Key (TISK) system for distributed data management in IoT. The proposed system classifies the IoT user nodes and assigns low-level, high-level security keys for data transactions. Low-level secure keys are generic light-weight keys used by the data collector nodes and data aggregator nodes for trusted transactions. TISK phase-I Generic Service Manager (GSM-C) module verifies the IoT devices based on self-trust incentive and server-trust incentive levels. High-level secure keys are dedicated special purpose keys utilized by data manager nodes and data expert nodes for authorized transactions. TISK phase-II Dedicated Service Manager (DSM-C) module verifies the certificates issued by GSM-C module. DSM-C module further issues high-level secure keys to data manager nodes and data expert nodes for specific purpose transactions. Simulation results indicate that the proposed TISK system reduces the key complexity and key cost to ensure distributed secure data management in IoT network.

Botnets have been a serious threat to the Internet security. With the constant sophistication and the resilience of them, a new trend has emerged, shifting botnets from the traditional desktop to the mobile environment. As in the desktop domain, detecting mobile botnets is essential to minimize the threat that they impose. Along the diverse set of strategies applied to detect these botnets, the ones that show the best and most generalized results involve discovering patterns in their anomalous behavior. In the mobile botnet field, one way to detect these patterns is by analyzing the operation parameters of this kind of applications. In this paper, we present an anomaly-based and host-based approach to detect mobile botnets. The proposed approach uses machine learning algorithms to identify anomalous behaviors in statistical features extracted from system calls. Using a self-generated dataset containing 13 families of mobile botnets and legitimate applications, we were able to test the performance of our approach in a close-to-reality scenario. The proposed approach achieved great results, including low false positive rates and high true detection rates.

The future of ambient assisted living (AAL) especially eHealthcare almost depends on the smart objects that are part of the Internet of things (IoT). In our AAL scenario, these objects collect and transfer real-time information about the patients to the hospital server with the help of Wireless Mesh Network (WMN). Due to the multi-hop nature of mesh networks, it is possible for an adversary to reroute the network traffic via many denial of service (DoS) attacks, and hence affect the correct functionality of the mesh routing protocol. In this paper, based on a comparative study, we choose the most suitable secure mesh routing protocol for IoT-based AAL applications. Then, we analyze the resilience of this protocol against DoS attacks. Focusing on the hello flooding attack, the protocol is simulated and analyzed in terms of data packet delivery ratio, delay, and throughput. Simulation results show that the chosen protocol is totally resilient against DoS attack and can be one of the best candidates for secure routing in IoT-based AAL applications.