Biblio

In Mobile Ad-hoc Network based Internet of things (MANET-IoT), nodes are mobile, infrastructure less, managed and organized by themselves that have important role in many areas such as Mobile Computing, Military Sector, Sensor Networks Commercial Sector, medical etc. One major problem in MANET based IoT is security because nodes are mobile, having not any central administrator and are also not reliable. So, MANET-IoT is more defenseless to denial-of-service attacks for-example Blackhole, Wormhole, Gray-hole etc. To compare the performance of network under different attacks for checking which attack is more affecting the performance of network, we implemented Blackhole and Wormhole attack by modifying AODV routing protocol in NS-3. After preprocessing of data that is obtained by using Flow-monitor module, we calculated performance parameters such as Average Throughput, Average Packet Delivery Ratio, Average End to End Delay, Average Jitter-Sum and compared it with no. of nodes in MANET-IoT network. Throughput and goodput performance of each node in the network is also calculated by using Trace metric module and compared with each node in the network. This approach is also very helpful for further research in MANET-IoT Security.

Mobile ad hoc network (MANET) is a kind of mobile multi-hop network which can transmit data through intermediate nodes, it has been widely used and become important since the growing of the market of Internet of Things (IoT). However, the transmissions on MANET are vulnerable, it usually suffered with many internal or external attacks, and the research on security topics of MANET are becoming more and more hot recently. Blackhole Attack is one of the most famous attacks to MANET. In this paper, we focus on the Blackhole Attack in AODV protocol, and use NS-3 network simulator to study the impact of Blackhole Attack on network performance parameters, such as the Throughput, End-to-End Delay and Packet Loss Rate. We further analyze the changes in network performance by adjusting the number of blackhole nodes and total nodes, and the movement speed of mobile nodes. The experimental results not only reflect the behaviors of the Blackhole Attack and its damage to the network, but also provide the characteristics of Blackhole Attacks clearly. This is helpful to the research of Blackhole Attack feature extraction and MANET security measurement.

Current procedures for anomaly detection in self-organizing mobile communication networks use network-centric approaches to identify dysfunctional serving nodes. In this paper, a user-centric approach and a novel methodology for anomaly detection is proposed, where the Quality of Experience (QoE) metric is used to evaluate the end-user experience. The system model demonstrates how dysfunctional serving eNodeBs are successfully detected by implementing a parametric QoE model using machine learning for prediction of user QoE in a network scenario created by the ns-3 network simulator. This approach can play a vital role in the future ultra-dense and green mobile communication networks that are expected to be both self- organizing and self-healing.

The number of sensors and embedded devices in an urban area can be on the order of thousands. New low-power wide area (LPWA) wireless network technologies have been proposed to support this large number of asynchronous, low-bandwidth devices. Among them, the Cooperative UltraNarrowband (C-UNB) is a clean-slate cellular network technology to connect these devices to a remote site or data collection server. C-UNB employs small bandwidth channels, and a lightweight random access protocol. In this paper, a new application is investigated - the use of C-UNB wireless networks to support the Advanced Metering Infrastructure (AMI), in order to facilitate the communication between smart meters and utilities. To this end, we adapted a mathematical model for C-UNB, and implemented a network simulation module in NS-3 to represent C-UNB's physical and medium access control layer. For the application layer, we implemented the DLMS-COSEM protocol, or Device Language Message Specification - Companion Specification for Energy Metering. Details of the simulation module are presented and we conclude that it supports the results of the mathematical model.

To combat susceptibility of modern computing systems to cyberattack, identifying and disrupting malicious traffic without human intervention is essential. To accomplish this, three main tasks for an effective intrusion detection system have been identified: monitor network traffic, categorize and identify anomalous behavior in near real time, and take appropriate action against the identified threat. This system leverages distributed SDN architecture and the principles of Artificial Immune Systems and Self-Organizing Maps to build a network-based intrusion detection system capable of detecting and terminating DDoS attacks in progress.

Congestion Control (CC) algorithms are essential to quickly restore the network performance back to stable whenever congestion occurs. A majority of the existing CC algorithms are implemented at the transport layer, mostly coupled with TCP. Over the past three decades, CC algorithms have incrementally evolved, resulting in many extensions of TCP. A thorough evaluation of a new TCP extension is a huge task. Hence, the Internet Congestion Control Research Group (ICCRG) has proposed a common TCP evaluation suite that helps researchers to gain an initial insight into the working of their proposed TCP extension. This paper presents an implementation of the TCP evaluation suite in ns-3, that automates the simulation setup, topology creation, traffic generation, execution, and results collection. We also describe the internals of our implementation and demonstrate its usage for evaluating the performance of five TCP extensions available in ns-3, by automatically setting up the following simulation scenarios: (i) single and multiple bottleneck topologies, (ii) varying bottleneck bandwidth, (iii) varying bottleneck RTT and (iv) varying the number of long flows.

TCP congestion control has been known for its crucial role in stabilizing the Internet and preventing congestion collapses. However, with the rapid advancement in networking technologies, resulting in the emergence of challenging network environments such as data center networks (DCNs), the traditional TCP algorithm leads to several impairments. The shortcomings of TCP when deployed in DCNs have motivated the development of multiple new variants, including DCTCP, ICTCP, IA-TCP, and D2TCP, but all of these algorithms exhibit their advantages at the cost of a number of drawbacks in the Global Internet. Motivated by the belief that new innovations need to be established on top of a solid foundation with a thorough understanding of the existing, well-established algorithms, we have been working towards a comprehensive analysis of various conventional TCP algorithms in DCNs and other modern networks. This paper presents our first milestone towards the completion of our comparative study in which we present the results obtained by simulating multiple TCP variants: NewReno, Vegas, HighSpeed, Scalable, Westwood+, BIC, CUBIC, and YeAH using a fat tree architecture. Each protocol is evaluated in terms of queue length, number of dropped packets, average packet delay, and aggregate bandwidth as a percentage of the channel bandwidth.