Smart detection and prevention procedure for DoS attack in MANET

A self-organized wireless communication short-lived network containing collection of mobile nodes is mobile ad hoc network (MANET). The mobile nodes communicate with each other by wireless radio links without the use of any pre-established fixed communication network infrastructure or centralized administration, such as base stations or access points, and with no human intervention. In addition, this network has potential applications in conference, disaster relief, and battlefield scenario, and have received important attention in current years. There is some security concern that increases fear of attacks on the mobile ad-hoc network. The mobility of the NODE in a MANET poses many security problems and vulnerable to different types of security attacks than conventional wired and wireless networks. The causes of these issues are due to their open medium, dynamic network topology, absence of central administration, distributed cooperation, constrained capability, and lack of clear line of defense. Without proper security, mobile hosts are easily captured, compromised, and attacked by malicious nodes. Malicious nodes behavior may deliberately disrupt the network so that the whole network will be suffering from packet losses. One of the major concerns in mobile ad-hoc networks is a traffic DoS attack in which the traffic is choked by the malicious node which denied network services for the user. Mobile ad-hoc networks must have a safe path for transmission and correspondence which is a serious testing and indispensable issue. So as to provide secure communication and transmission, the scientist worked explicitly on the security issues in versatile impromptu organizations and many secure directing conventions and security measures within the networks were proposed. The goal of the work is to study DoS attacks and how it can be detected in the network. Existing methodologies for finding a malicious node that causes traffic jamming is based on node’s retains value. The proposed approach finds a malicious node using reliability value determined by the broadcast reliability packet (RL Packet). In this approach at the initial level, every node has zero reliability value, specific time slice, and transmission starts with a packet termed as reliability packet, node who responded properly in specific time, increases its reliability value and those nodes who do not respond in a specific time decreases their reliability value and if it goes to less than zero then announced that it’s a malicious node. Reliability approach makes service availability and retransmission time

A Supervised Machine Learning Based Intrusion Detection Model for Detecting Cyber-Attacks Against Computer System

Internet usage has become essential for correspondence in almost every calling in our digital age. To protect a network, an effective intrusion detection system (IDS) is vital. Intrusion Detection System is a software application to detect network intrusion using various machine learning algorithms. The function of the expert has been lessened by machine learning approaches since knowledge is taken directly from the data. The fact that it makes use of all the features of an information packet spinning in the network for intrusion detection is weakened by the employment of various methods for detecting intrusions, such as statistical models, safe system approaches, etc. Machine learning has become a fundamental innovation for cyber security. Two of the key types of attacks that plague businesses, as proposed in this paper, are Denial of Service (DOS) and Distributed Denial of Service (DDOS) attacks. One of the most disastrous attacks on the Internet of Things (IOT) is a denial of service.  Two diverse Machine Learning techniques are proposed in this research work, mainly Supervised learning. To achieve this goal, the paper represents a regression algorithm, which is usually used in data science and machine learning to forecast the future. An innovative approach to detecting is by using the Machine Learning algorithm by mining application-specific logs. Cyber security is a way of providing their customers the peace of mind they need knowing that they have secured their information and money