A Machine Learning Based Intrusion Detection System for Mobile Internet of Things

Intrusion detection systems plays a pivotal role in detecting malicious activities that denigrate the performance of the network. Mobile adhoc networks (MANETs) and wireless sensor networks (WSNs) are a form of wireless network that can transfer data without any need of infrastructure for their operation. A more novel paradigm of networking, namely Internet of Things (IoT) has emerged recently which can be considered as a superset to the afore mentioned paradigms. Their distributed nature and the limited resources available, present a considerable challenge for providing security to these networks. The need for an intrusion detection system (IDS) that can acclimate with such challenges is of extreme significance. Previously, we proposed a cross layer-based IDS with two layers of detection. It uses a heuristic approach which is based on the variability of the correctly classified instances (CCIs), which we refer to as the accumulated measure of fluctuation (AMoF). The current, proposed IDS is composed of two stages; stage one collects data through dedicated sniffers (DSs) and generates the CCI which is sent in a periodic fashion to the super node (SN), and in stage two the SN performs the linear regression process for the collected CCIs from different DSs in order to differentiate the benign from the malicious nodes. In this work, the detection characterization is presented for different extreme scenarios in the network, pertaining to the power level and node velocity for two different mobility models: Random way point (RWP), and Gauss Markov (GM). Malicious activity used in the work are the blackhole and the distributed denial of service (DDoS) attacks. Detection rates are in excess of 98% for high power/node velocity scenarios while they drop to around 90% for low power/node velocity scenarios.

Enhanced MSEEC routing protocol using tabu search with static and mobile nodes in WSN

Background Background of this paper has taken place in mainly heterogeneous network in which three types of nodes are present like normal node, advance node and super node with different amount of energy. The energy of super node is greater than that of advance and normal nodes and the energy of advance nodes are also greater than that of normal nodes in the designed network. The optimization techniques have to be studied from the swarm intelligence based on the different aspects of routing. Objective: The objective of this paper is to propose a new heterogeneous protocol with the help of hybrid meta-heuristic technique. In this technique, the shortest route has been selected and forwarded the data to the sink in a minimal time span to save the energy and make the network more stable. Method: To evaluate the technique, a new hybrid technique has been created, where the data transmission is implemented from the beginning. This technique contains the route process of the algorithm which was made available through a hybrid meta-heuristic technique. Results: Simulation results show that the hybrid meta-heuristic technique has high throughput with less number of dead nodes with existing methods and also show that the efficiency and stability of new proposed protocol. Conclusion The conclusion to this paper is a novel, energy-efficient technique applied for randomly deployed sensor nodes over the wireless sensor network and enhancement has been done in stability and throughput of a new proposed algorithm in case of static as well as moving nodes.

A Novel Combination of Distributed Ledger Technologies on Internet of Things: Use Case on Precision Agriculture

Internet-of-Things (IoT) is an enabling technology for numerous initiatives worldwide such as manufacturing, smart cities, precision agriculture, and eHealth. The massive field data aggregation of distributed administered IoT devices allows new insights and actionable information for dynamic intelligent decision-making. In such distributed environments, data integrity, referring to reliability and consistency, is deemed insufficient and requires immediate facilitation. In this article, we introduce a distributed ledger (DLT)-based system for ensuring IoT data integrity which securely processes the aggregated field data. Its uniqueness lies in the embedded use of IOTA’s ledger, called “The Tangle”, used to transmit and store the data. Our approach shifts from a cloud-centric IoT system, where the Super nodes simply aggregate and push data to the cloud, to a node-centric system, where each Super node owns the data pushed in a distributed and decentralized database (i.e., the Tangle). The backend serves as a consumer of data and a provider of additional resources, such as administration panel, analytics, data marketplace, etc. The proposed implementation is highly modularand constitutes a significant contribution to the Open Source communities, regarding blockchain and IoT.

An obstacle aware mobile sink path strategy in WSN

<span>In Wireless Sensor Networks, Mobile Sink accomplishes considerable achievement on network lifetime improvement. In sensing environment, more chances to present the obstacle. But, in the mobile sink, how to identify the obstacle and make the obstacle aware path strategy is a challenging task. To overcome this problem, we propose an Obstacle Aware Mobile sink Path Strategy (OAMPS) that detects any obstacles which enter within the network lifetime and design a shortest mobile sink movement path avoiding detected obstacles. In this scheme, the mobile sink collects the sensing data from the super node then it sends the data to the base station. Here, static or moving obstacles are present when the mobile sink moves the path scheduling by spanning graph. This algorithm is introduced to discovering the obstacle avoiding shortest path. The source selects the route by the updated cuckoo search algorithm. The simulation results show that the OAMPS improved the throughput and minimized the delay in the network.</span>

Performance evaluation and resource optimization of cloud based parallel Hadoop clusters with an intelligent scheduler.

Data generated from real time information systems are always incremental in nature. Processing of such a huge incremental data in large scale requires a parallel processing system like Hadoop based cluster. Major challenge that arises in all cluster-based system is how efficiently the resources of the system can be used. The research carried out proposes a model architecture for Hadoop cluster with additional components integrated such as super node who manages the clusters computations and a mediation manager who does the performance monitoring and evaluation. Super node in the system is equipped with intelligent or adaptive scheduler that does the scheduling of the job with optimal resources. The scheduler is termed intelligent as it automatically decides which resource to be taken for which computation, with the help of a cross mapping of resource and job with a genetic algorithm which finds the best matching resource. The mediation node deploys ganglia a standard monitoring tool for Hadoop cluster to collect and record the performance parameters of the Hadoop cluster. The system over all does the scheduling of different jobs with optimal usage of resources thus achieving better efficiency compared to the native capacity scheduler in Hadoop. The system is deployed on top of OpenNebula Cloud environment for scalability.