MACHINE LEARNING AND GRAPH THEORY FOR ATTACK DETECTION AND ROUTE OPTIMIZATION IN RF NETWORKS

Abstract

Machine Learning and Graph Theory for Attack Detection and Route Optimization in RF Networks is the proposed invention. The proposed invention focuses on understanding the functions of Route Optimization. The invention focuses on analyzing the parameters of Attack Detection in RF Networks using algorithms of Machine Learning Approach.

Specification

Description:[0001] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0002] Machine learning (ML) is a type of artificial intelligence (AI) that allows computers to learn and improve from experience without being explicitly programmed. Machine learning (ML) uses algorithms to analyze large amounts of data, identify patterns, and make predictions. Machine learning (ML) is well-suited for situations where data is always changing, or where coding a solution would be difficult.
[0003] A number of different types of RF network attacks analysis systems that are known in the prior art. For example, the following patents are provided for their supportive teachings and are all incorporated by reference.
[0004] US9160760B2: In one embodiment, a training request is sent to a plurality of nodes in a network to cause the nodes to generate statistics regarding unicast and broadcast message reception rates associated with the nodes. The statistics are received from the nodes and a statistical model is generated using the received statistics and is configured to detect a network attack by comparing unicast and broadcast message reception statistics. The statistical model is then provided to the nodes and an indication that a network attack was detected by a particular node is received from the particular node.
[0005] Graph theory is the study of graphs, which are mathematical structures used to model pairwise relations between objects. A graph in this context is made up of vertices (also called nodes or points) which are connected by edges (also called arcs, links or lines). A distinction is made between undirected graphs, where edges link two vertices symmetrically, and directed graphs, where edges link two vertices asymmetrically. Graphs are one of the principal objects of study in discrete mathematics. The proposed invention focuses on analyzing the Attack Detection in RF Networks through algorithms of Machine Learning Approach.
[0006] Above information is presented as background information only to assist with an understanding of the present disclosure. No determination has been made, no assertion is made, and as to whether any of the above might be applicable as prior art with regard to the present invention.
[0007] In the view of the foregoing disadvantages inherent in the known types of RF network attacks analysis systems now present in the prior art, the present invention provides an improved system. As such, the general purpose of the present invention, which will be described subsequently in greater detail, is to provide a new and improved Machine learning and graph theory-based techniques for detecting the attacks and route optimization in RF networks that has all the advantages of the prior art and none of the disadvantages.
SUMMARY OF INVENTION
[0008] In the view of the foregoing disadvantages inherent in the known types of RF network attacks analysis systems now present in the prior art, the present invention provides an improved one. As such, the general purpose of the present invention, which will be described subsequently in greater detail, is to provide a new and improved Machine learning and graph theory-based techniques for detecting the attacks and route optimization in RF networks which has all the advantages of the prior art and none of the disadvantages.
[0009] The Main objective of the proposed invention is to design & implement a framework of Machine Learning techniques for analyzing the parameters of Attack Detection in RF Networks. Route Optimization in RF Networks is analyzed.
[0010] Yet another important aspect of the proposed invention is to design & implement a framework of Graph Theory techniques that will consider on understanding the functions of Route Optimization. Machine Learning and Graph Theory for Attack Detection in RF Networks is analyzed by predictive unit. The results of prediction are displayed on the display unit.
[0011] In this respect, before explaining at least one embodiment of the invention in detail, it is to be understood that the invention is not limited in its application to the details of construction and to the arrangements of the components set forth in the following description or illustrated in the various ways. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
[0012] These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages and the specific objects attained by its uses, reference should be had to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.
BRIEF DESCRIPTION OF DRAWINGS
[0013] The invention will be better understood and objects other than those set forth above will become apparent when consideration is given to the following detailed description thereof. Such description makes reference to the annexed drawings wherein:
Figure 1 illustrates the schematic view of Machine Learning and Graph Theory for Attack Detection and Route Optimization in RF Networks, according to the embodiment herein.
DETAILED DESCRIPTION OF INVENTION
[0014] In the following detailed description, reference is made to the accompanying drawings which form a part hereof, and in which is shown by way of illustration specific embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, and it is to be understood that the embodiments may be combined, or that other embodiments may be utilized and that structural and logical changes may be made without departing from the spirit and scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined by the appended claims and their equivalents.
[0015] While the present invention is described herein by way of example using several embodiments and illustrative drawings, those skilled in the art will recognize that the invention is neither intended to be limited to the embodiments of drawing or drawings described, nor intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention covers all modification/s, equivalents and alternatives falling within the spirit and scope of the present invention as defined by the appended claims. The headings are used for organizational purposes only and are not meant to limit the scope of the description or the claims. As used throughout this description, the word "may" be used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Further, the words "a" or "a" mean "at least one" and the word "plurality" means one or more, unless otherwise mentioned. Furthermore, the terminology and phraseology used herein is solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and any additional subject matter not recited, and is not intended to exclude any other additives, components, integers or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles and the like are included in the specification solely for the purpose of providing a context for the present invention.
[0016] In this disclosure, whenever an element or a group of elements is preceded with the transitional phrase "comprising", it is understood that we also contemplate the same element or group of elements with transitional phrases "consisting essentially of, "consisting", "selected from the group consisting of", "including", or "is" preceding the recitation of the element or group of elements and vice versa.
[0017] Attack detection" refers to the process of identifying and recognizing malicious activity within a computer system or network, essentially detecting when a cyber-attack is occurring by analyzing network traffic, user behavior, and system logs to identify anomalies that could indicate a potential threat; it's a key part of cybersecurity to proactively respond to and mitigate attacks before significant damage is done.
[0018] In RF networks, "Route Optimization" refers to the process of selecting the most efficient signal path between network nodes, considering factors like signal strength, interference levels, and network topology, to ensure optimal signal transmission and minimize signal degradation across the network, essentially finding the best route for radio waves to travel to reach their destination with the highest quality possible. The proposed invention focuses on implementing the algorithms of Graph Theory Approach for studying the functions of Route Optimization.
[0019] Reference will now be made in detail to the exemplary embodiment of the present disclosure. Before describing the detailed embodiments that are in accordance with the present disclosure, it should be observed that the embodiment resides primarily in combinations arrangement of the system according to an embodiment herein and as exemplified in FIG. 1
[0020] Figure 1 illustrates the schematic view of Machine Learning and Graph Theory for Attack Detection and Route Optimization in RF Networks 100. The proposed invention 100 includes a RF networks 101 which is analysed using analysis unit 102. The machine learning unit 104 will run predictive unit 104 and predicts the possible attacks. The graph theory module 105 along with predictions will alert the analysis unit 102 at regular intervals to avoid attacks and route optimization.
[0021] In the following description, for the purpose of explanation, numerous specific details are set forth in order to provide a thorough understanding of the arrangement of the system according to an embodiment herein. It will be apparent, however, to one skilled in the art that the present embodiment can be practiced without these specific details. In other instances, structures are shown in block diagram form only in order to avoid obscuring the present invention.
, Claims:1. Machine Learning and Graph Theory for Attack Detection and Route Optimization in RF Networks, comprises of:
Machine learning unit;
Predictive unit and
Graph theory module.
2. Machine Learning and Graph Theory for Attack Detection and Route Optimization in RF Networks, according to claim 1, includes a machine learning unit, wherein the machine learning unit will run predictive unit.
3. Machine Learning and Graph Theory for Attack Detection and Route Optimization in RF Networks, according to claim 1, includes a predictive unit, wherein the predictive unit will predict the possible attacks.
4. Machine Learning and Graph Theory for Attack Detection and Route Optimization in RF Networks, according to claim 1, includes a graph theory module, wherein the graph theory module will alert the analysis unit at regular intervals to avoid attacks and route optimization.

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