Biblio

In view of the problem that the health status and safety monitoring of the traditional intelligent home are mainly dependent on the manual inspection, this paper introduces the intelligent home-based remote monitoring system by introducing the Internet-based Internet of Things technology into the intelligent home condition monitoring and safety assessment. The system's Android remote operation based on the MVP model to develop applications, the use of neural networks to deal with users daily use of operational data to establish the network data model, combined with S3C2440A microcontrollers in the gateway to the embedded Linux to facilitate different intelligent home drivers development. Finally, the power line communication network is used to connect the intelligent electrical appliances to the gateway. By calculating the success rate of the routing nodes, the success rate of the network nodes of 15 intelligent devices is 98.33%. The system can intelligent home many electrical appliances at the same time monitoring, to solve the system data and network congestion caused by the problem can not he security monitoring.

Warnings happen a lot in real transmission networks. These warnings can affect people's lives. It is significant to analyze the alarm association rules in the network. Many algorithms can help solve this problem but not considering the actual physical significance. Therefore, in this study, we mine the association rules in warning weblogs based on a sequential mining algorithm (GSP) with topology structure. We define a topology constraint from network physical connection data. Under the topology constraint, network nodes have topology relation if they are directly connected or have a common adjacency node. In addition, due to the large amount of data, we implement the hash-tree search method to improve the mining efficiency. The theoretical solution is feasible and the simulation results verify our method. In simulation, the topology constraint improves the accuracy for 86%-96% and decreases the run time greatly at the same time. The hash-tree based mining results show that hash tree efficiency improvements are in 3-30% while the number of patterns remains unchanged. In conclusion, using our method can mine association rules efficiently and accurately in warning weblogs.

Mobile Adhoc Network (MANET) are the networks where network nodes uses wireless links to transfer information from one node to another without making use of existing infrastructure. There is no node in the network to control and coordinate establishment of connections between the network nodes. Hence the network nodes performs dual function of both node as well as router. Due to dynamically changing network scenarios, absence of centralization and lack of resources, MANETs have a threat of large number of security attacks. Hence security attacks need to be evaluated in order to find effective methods to avoid or remove them. In this paper malicious behavior of Blackhole attack and Rushing attack is studied and analyzed for QoS metrics.

Mobile Ad-hoc Networks (MANETs) have gained popularity both in research and in industrial fields. This is due to their ad hoc nature, easy deployment thanks to the lack of fixed infrastructure, self-organization of its components, dynamic topologies and the absence of any central authority for routing. However, MANETs suffer from several vulnerabilities such as battery power, limited memory space, and physical protection of network nodes. In addition, MANETs are sensitive to various attacks that threaten network security like Blackhole attack in its different implementation (single and multiple). In this article, we present the simulation results of single and multiple Blackhole attack in AODV and OLSR protocols on using NS-3.27 simulator. In this simulation, we took into consideration the density of the network described by the number of nodes included in the network, the speed of the nodes, the mobility model and even we chose the IEEE 802.11ac protocol for the pbysicallayer, in order to have a simulation, which deals with more general and more real scenarios. To be able to evaluate the impact of the attack on the network, the Packet delivery rate, Routing overhead, Throughput and Average End to End delay have been chosen as metrics for performance evaluation.

To preserve the privacy of social networks, most existing methods are applied to satisfy different anonymity models, but there are some serious problems such as huge large information losses and great structural modifications of original social network. Therefore, an improved privacy protection method called k-subgraph is proposed, which is based on k-degree anonymous graph derived from k-anonymity to keep the network structure stable. The method firstly divides network nodes into several clusters by label propagation algorithm, and then reconstructs the sub-graph by means of moving edges to achieve k-degree anonymity. Experimental results show that our k-subgraph method can not only effectively improve the defense capability against malicious attacks based on node degrees, but also maintain stability of network structure. In addition, the cost of information losses due to anonymity is minimized ideally.

The increasing demand and the use of mobile ad hoc network (MANET) in recent days have attracted the attention of researchers towards pursuing active research work largely related to security attacks in MANET. Gray hole attack is one of the most common security attacks observed in MANET. The paper focuses on gray hole attack analysis in Ad hoc on demand distance vector(AODV) routing protocol based MANET with reliability as a metric. Simulation is performed using ns-2.35 simulation software under varying number of network nodes and varying number of gray hole nodes. Results of simulation indicates that increasing the number of gray hole node in the MANET will decrease the reliability of MANET.

Internet of Things is nowadays growing faster than ever before. Operators are planning or already creating dedicated networks for this type of devices. There is a need to create dedicated solutions for this type of network, especially solutions related to information security. In this article we present a mechanism of security-aware routing, which takes into account the evaluation of trust in devices and packet flows. We use trust relationships between flows and network nodes to create secure SDN paths, not ignoring also QoS and energy criteria. The system uses SDN infrastructure, enriched with Cognitive Packet Networks (CPN) mechanisms. Routing decisions are made by Random Neural Networks, trained with data fetched with Cognitive Packets. The proposed network architecture, implementing the security-by-design concept, was designed and is being implemented within the SerIoT project to demonstrate secure networks for the Internet of Things (IoT).

Vehicular Ad-hoc NETwork (VANET) is a vital transportation technology that facilitates the vehicles to share sensitive information (such as steep-curve warnings and black ice on the road) with each other and with the surrounding infrastructure in real-time to avoid accidents and enable comfortable driving experience.To achieve these goals, VANET requires a secure environment for authentic, reliable and trusted information dissemination among the network entities. However, VANET is prone to different attacks resulting in the dissemination of compromised/false information among network nodes. One way to manage a secure and trusted network is to introduce trust among the vehicular nodes. To this end, various Trust Models (TMs) are developed for VANET and can be broadly categorized into three classes, Entity-oriented Trust Models (ETM), Data oriented Trust Models (DTM) and Hybrid Trust Models (HTM). These TMs evaluate trust based on the received information (data), the vehicle (entity) or both through different mechanisms. In this paper, we present a comparative study of the three TMs. Furthermore, we evaluate these TMs against the different trust, security and quality-of-service related benchmarks. Simulation results revealed that all these TMs have deficiencies in terms of end-to-end delays, event detection probabilities and false positive rates. This study can be used as a guideline for researchers to design new efficient and effective TMs for VANET.

In modern cyber-physical systems and wireless sensor networks the complexity of crisis management processes is caused by a variety of software/hardware assets and communication protocols, the necessity of their collaborative function, possible inconsistency of data flows between particular devices and increased requirements to cyber-physical security. A crisis management oriented model of a communicational mobile network is constructed. A general architecture of network nodes by the use of XBee circuits, Arduino microcontrollers and connecting equipment are developed. An analysis of possible cyber-physical security events on the base of existing intruder models is performed. A series of experiments on modeling attacks on network nodes is conducted. Possible ways for attack revelations by means of components for security event collection and data correlation is discussed.

Wireless sensor network operate on the basic underlying assumption that all participating nodes fully collaborate in self-organizing functions. However, performing network functions consumes energy and other resources. Therefore, some network nodes may decide against cooperating with others. Node misbehavior due to selfish or malicious reasons or faulty nodes can significantly degrade the performance of mobile ad-hoc networks. To cope with misbehavior in such self-organized networks, nodes need to be able to automatically adapt their strategy to changing levels of cooperation. The problem of identifying and isolating misbehaving nodes that refuses to forward packets in multi-hop ad hoc networks. a comprehensive system called Audit-based Misbehavior Detection (AMD) that effectively and efficiently isolates both continuous and selective packet droppers. The AMD system integrates reputation management, trustworthy route discovery, and identification of misbehaving nodes based on behavioral audits. AMD evaluates node behavior on a per-packet basis, without employing energy-expensive overhearing techniques or intensive acknowledgment schemes. AMD can detect selective dropping attacks even if end-to-end traffic is encrypted and can be applied to multi-channel networks.

In this paper, an algorithm is proposed to automatically produce hierarchical graph-based representations of maritime shipping lanes extrapolated from historical vessel positioning data. Each shipping lane is generated based on the detection of the vessel behavioural changes and represented in a compact synthetic route composed of the network nodes and route segments. The outcome of the knowledge discovery process is a geographical maritime network that can be used in Maritime Situational Awareness (MSA) applications such as track reconstruction from missing information, situation/destination prediction, and detection of anomalous behaviour. Experimental results are presented, testing the algorithm in a specific scenario of interest, the Dover Strait.