Journal of Positive School Psychology http://journalppw.com 2022, Vol. 6, No. 4, 473-485 © 2021 JPPW. All rights reserved Design Machine Learning BasedIntelligent Techniques for Detecting Network Attacks Shekjavid Hussain 1 ; Dr. Bechoo Lal 2 1 Research Scholar, Department of Computer science & Engineering; Shri JJT University Jhunjhunu Rajasthan 2 Assistant Professor, Department of Computer science & Engineering; Shri JJT University Jhunjhunu Rajasthan Abstract The number of Internet of Things (IoT) devices that are vulnerable to cyber-attacks is increasing at an alarming rate. As a result, network operators are placing an increasing emphasis on the control of these devices. A comprehensive packet inspection in software can be difficult, expensive, rigid, and unable to scale with current network monitoring solutions that use specialised acceleration on network switches. SDN and machine learning are used in this work to take use of the programmability offered by SDNs. Information driven models for overseeing IoT gadgets in light of their organization exercises by means of stream based telemetry. The three manners by which we have an effect: Over a six-month time frame, we gathered traffic follows from 17 genuine purchaser IoT gadgets and recognized a bunch of traffic streams (per-gadget) that portray the organization conduct of different IoT gadget types and their working states (i.e., booting, effectively collaborated with client, or being inactive). (2) We create a multi-stage design of surmising models that utilization stream levity information to make forecasts about the organization conduct of different IoT gadget types and their working states. (3) We measure the compromise among execution and cost of our methodology and clarify how our checking framework can be used in activity to identify conduct changes, all utilizing genuine traffic information to prepare our models (firmware overhaul or digital assaults).. Keywords: WSN, IoT, Cyber-attack, Security, Machine learning, 1. Introduction Machine learning (ML) and data mining (DM) methodologies for cyber security applications were surveyed in this article. As well as many applications to cyber intrusion detection challenges, the ML/DM approaches and techniques are explained. Paper discusses the difficulty of ML/DM algorithms in terms of complexity and recommends which strategies to utilise depending on what kind of cyber problem you are trying to address. When it comes to protecting computers against assault, unauthorised access or modification or even destruction, cyber security encompasses a wide range of technology as well as processes. There are two main types of cyber security systems: network and computer (host). Firewalls, antivirus software, and intrusion detection systems are all included at the very least in each of these systems (IDS). Unauthorized use, copying, change, and destruction of information systems can be discovered, determined, and identified with the aid of IDSs [1]. Internal and external invasions are two types of security breaches that have occurred (attacks from within the organization). Cyber analytics that enable IDSs fall into three broad categories: misuse-based (also known as signature-based), anomaly-based, and hybrid. By looking for patterns in previously detected assaults, misuse-based techniques can identify potential threats before they can be launched. There is no need to worry about a flood of false alarms because they are excellent at detecting established assaults. There are rules and signatures in the database that need to be