SVM-Based Traffic Data Classification for Secured IoT-Based Road Signaling System

The traffic controlling systems at present are microcontroller-based, which is semi-automatic in nature where time is the only parameter that is considered. With the introduction of IoT in traffic signaling systems, research is being done considering density as a parameter for automating the traffic signaling system and regulate traffic dynamically. Security is a concern when sensitive data of great volume is being transmitted wirelessly. Security protocols that have been implemented for IoT networks can protect the system against attacks and are purely based on standard cryptosystem. They cannot handle heterogeneous data type. To prevent the issues on security protocols, the authors have implemented SVM machine learning algorithm for analyzing the traffic data pattern and detect anomalies. The SVM implementation has been done for the UK traffic data set between 2011-2016 for three cities. The implementation been carried out in Raspberry Pi3 processor functioning as an edge router and SVM machine learning algorithm using Python Scikit Libraries.

Caching on Named Data Network: a Survey and Future Research

The IP-based system cause inefficient content delivery process. This inefficiency was attempted to be solved with the Content Distribution Network. A replica server is located in a particular location, usually on the edge router that is closest to the user. The user’s request will be served from that replica server. However, caching on Content Distribution Network is inflexible. This system is difficult to support mobility and conditions of dynamic content demand from consumers. We need to shift the paradigm to content-centric. In Named Data Network, data can be placed on the content store on routersthat are closest to the consumer. Caching on Named Data Network must be able to store content dynamically. It should be selectively select content that is eligible to be stored or deleted from the content storage based on certain considerations, e.g. the popularity of content in the local area. This survey paper explains the development of caching techniques on Named Data Network that are classified into main points. The brief explanation of advantages and disadvantages are presented to make it easy to understand. Finally, proposed the open challenge related to the caching mechanism to improve NDN performance.

Per-Flow Throughput Fairness in Ring Aggregation Network with Multiple Edge Routers

Ring aggregation networks are often employed by network carriers because of their efficiency and high fault tolerance. A fairness scheme is required in ring aggregation to achieve per-flow throughput fairness and bufferbloat avoidance, because frames are forwarded along multiple ring nodes. N Rate N + 1 Color Marking (NRN + 1CM) was proposed to achieve fairness in ring aggregation networks consisting of Layer-2 Switches (SWs). With NRN + 1CM, frames are selectively discarded based on color and the frame-dropping threshold. To avoid the accumulation of a queuing delay, frames are discarded at upstream nodes in advance through the notification process for the frame-dropping threshold. However, in the previous works, NRN + 1CM was assumed to be employed in a logical daisy chain topology linked to one Edge Router (ER). The currently available threshold notification process of NRN + 1CM cannot be employed for ring networks with multiple ERs. Therefore, this paper proposes a method for applying NRN + 1CM to a ring aggregation network with multiple ERs. With the proposed algorithm, an SW dynamically selects the dropping threshold to send in order to avoid excess frame discarding. The performance of the proposed scheme was confirmed through computer simulations.

Intelligent Packet Delivery in Router Using Structure Optimized Neural Network

The Internet itself is a worldwide network connecting millions of computers and less significant networks. Computers communicated by routers. Crucial the role of a router is to our technique of communicating and computing. Routers are situated at gateways, the spaces where two or more networks connect, and are the decisive device that keeps data flow between networks and keeps the networks connected to the Internet. When data is sent between places on one network or from one network to a second network the data is always seen and intended for to the proper place by the router. The router carries out this by using headers and routing tables to establish the best path for routing the data packets. This trim down the effectiveness of edge router only when the path engaged, it will enhanced by classification method, predictable classification methods like port based ,deep packet inspection and statistical classification are give less precision. In this system structured optimized neural network is used for more precise organization. Classification output forwarded to router dynamically for intellectual packet delivery. The method will improve router competence by greater than before throughput and decreased latency.<span style="font-size: 9.0pt; line-height: 115%; font-family: 'Times New Roman','serif'; mso-fareast-font-family: 'Times New Roman'; letter-spacing: .05pt; mso-ansi-language: EN-IN; mso-fareast-language: EN-US; mso-bidi-language: AR-SA; mso-bidi-font-weight: bold; mso-bidi-font-style: italic;" lang="EN-IN">The Internet itself is a worldwide <a href="https://www.webopedia.com/TERM/I/network.htm"><span style="color: windowtext; text-decoration-line: none;">network</span></a> connecting millions of <a href="https://www.webopedia.com/TERM/I/computer.htm"><span style="color: windowtext; text-decoration-line: none;">computers</span></a> and less significant networks. Computers communicated by routers. Crucial the role of a router is to our technique of communicating and computing. Routers are situated at <a href="https://www.webopedia.com/TERM/G/gateway.html"><span style="color: windowtext; text-decoration-line: none;">gateways</span></a>, the spaces where two or more networks connect, and are the decisive device that keeps data flow between networks and keeps the networks connected to the Internet. When data is sent between places on one network or from one network to a second network the data is always seen and intended for to the proper place by the router. The router carries out this by using <a href="https://www.webopedia.com/TERM/R/header.htm"><span style="color: windowtext; text-decoration-line: none;">headers</span></a> and routing tables to establish the best path for routing the data packets. This trim down the effectiveness of edge router only when the path engaged, it will enhanced by classification method, predictable classification methods like port based ,deep packet inspection and statistical classification are give less precision. In this system structured optimized neural network is used for more precise organization. Classification output forwarded to router dynamically for intellectual packet delivery. The method will improve router competence by greater than before throughput and decreased latency.</span>

PERFORMANCE ANALYSIS OF AGENT BASED DISTRIBUTED DEFENSE MECHANISMS AGAINST DDOS ATTACKS

The current internet infrastructure is susceptible to distributed denial of service (DDoS) attacks and has no built in mechanism to defend against them. The research on these kinds of attacks and their defense is significant for the security and reliability of the internet. We have already proposed a collaborative agent based distributed DDoS defense scheme which detect and prevents against DDoS attacks in ISP (Internet Service Provider) boundaries. The actual task of defense is carried out by agents and coordinators in each ISP. The defense system works by inspecting incoming traffic on edge router and identify the happening of DDoS attacks. The agent’s implements an entropy-threshold based detection algorithm. The coordinators share attack related information with neighboring ISPs in order to achieve distributed defense. The performance of defense system is evaluated on the basis of some identified metrics. The effectiveness of the defense system is evaluated in the presence and absence of defense system. The result indicates that the proposed defense system does accurate attack detection with very few false positives and false negatives.