Mobile ad-hoc network (MANET) is a self-deliberate data network, where all nodes behave like host or router. MANET is a collection of number of mobile nodes or devices that randomly generate a temporary network. Security is the fundamental requirement in MANET due to its behavior of changing topology, open medium and lack of centralized authentication. This leads to various security attacks in mobile ad hoc network and violate the criteria of routing mechanism. Mobile Ad-hoc network doesn't need backbone infrastructure support and it is very reliable and also contains the routable networking environment. In this paper, the effect of flooding attack in AODV based network is explained. The network parameters like Throughput, Packet Delivery Fraction (PDF) and End to End Delay are compared with normal network (without flooding attack) and a network with one or more flooder nodes. The performance of network parameters is compared in all the three scenarios. We have proposed a scheme which is finds single or number of malicious nodes in the network and drops fake packets.

The pandemic caused by the COVID-19 virus is the most serious current threat to the public's health. For the purpose of identifying patients with Covid-19, Chest X-Rays have proven to be an indispensable imaging modality for the hospital. Nevertheless, radiologists are needed to commit a significant amount of time to their interpretation. It is possible to diagnose and triage cases of Covid-19 effectively and rapidly with the assistance of precise computer systems that are powered by Machine Learning techniques. Machine Learning techniques such as Deep Feature Extraction can help detect the disease with improved precision and speed when used in conjunction with X-Ray images of the lung. This helps to alleviate the problem of lack of testing kits. Using the U-Net for Semantic image segmentation for lung segmentation and deep feature extractionbased strategy that was suggested in this research, it is possible to differentiate between patients who have contracted the Covid-19 virus, pneumonia and healthy people. XGBoost and recursive feature extraction based proposed methodology is evaluated using 20 different Pre-Trained deep learning based models including EfficientNet variations and it is observed that the maximum detection accuracy, precision, recall specificity, and F1-score are achieved when EfficientNetB1 is used to extract deep features. The respective values for these metrics are 97.6%, 0.964, 0.964, and 0.982. These findings lend credence to the efficiency of the proposed methodology.

Mobile ad hoc network (MANET) is constructed from various number of nodes, that can be move anywhere and at any time, without any infrastructure. MANETs use wireless connections to connect various networks, without any fixed infrastructure or any centralized administration. Due to this nature of MANET, Ad hoc networks are open to different types of security attacks. The gray hole attack is the attack performed by the node called malicious node, which forwards and drops the selective packets only. Here, in this paper, we have proposed an algorithm which detects and eliminates the gray hole attack using Dynamic Credit Based Technique using AODV routing protocol. The gray hole node is detected based on credit value, which increases or decreases. The simulation results are compared with different situation and attempt to improve the performance of AODV protocol for the parameters like Packet Delivery Fraction, Throughput and End-to-End delay.

Computer network security is now a days gaining popularity among network users. Organizations are spending more time and money for securing their information. Security is also more considered by the network researchers due to the importance of network security has grown unbelievably. Finite Automata or the state machine is a mathematical model to designing computer software and sequential logic circuits. FSA uses pattern for filtering. A pattern is a group of characters that exist along with the malicious programs. Pattern matching is the process of matching the incoming packet contents with the known patterns of the malware. In this paper we have tried to explain the firewall which improves the security with faster firewall mechanism. Our proposed solution provides filtering according to the keyword and port number. Also we have proposed new feature for the LAN users that is any user can interact with the other user of the same server. We have tried to propose a firewall which is dynamic where we can change the filtering rules. Previous work is limited when there is dynamic changes needed to the firewall. Also the important improvement is related to the time duration. Our proposed solution with FSA (Finite State Automata) regular expression takes less time for filtering of the packet compare to the algorithm which doesn't use the FSA.

No abstract