Abhilash Sonker

Plants shows crucial significance being a part in an ecological community as they maintain the atmosphere. It is back breaking to scan the flower quality by hands. It has requirement of extensive work and also it needs of huge quantity of processing time. Therefore, DIP (digital image processing) is applicable in uncovering of flower quality detection. Recognition/detection of quality includes the steps like Image Acquisition(IA), Image Pre-processing(IPP), Image Segmentation(IS), Feature Extraction(FE) and its grouping. The motive of paper is to present a roadmap on the Detection of defected and non-defected(healthy) rose flower utilizing DIP (Digital Image Processing Methods). This will help research scholars to understand computer vision applications in quality assessment of Flowers.

Mobile Ad hoc Network (MANET) is a position of mobile phone devices like which correspond surrounded by each other in excess of wireless relations lacking a preconceived communications or a essential weight. The associate nodes are self dependent pro design, function also continuation of the association by means of only hop or multi hop communication. In existing work, RSA and Triple DES algorithm are used to detect malicious nodes but we performed greedy approaches and encryption which provides much better results. This shows in the NS2 Simulation which proved that our methodology is far better than the existing work.

VANET is the network which monitoring the traffic on the road and share the information with neighbors. Movable nodes (vehicle) and fixed node (RSU) are used in VANET. This network is about the safety purpose of driver and sharing confidential information about traffic and accident. On network some vehicles do not share information, send fake requests and try to break network security. Lots of techniques are present here, to overcome by these attacks. Overcome by these issues we give artificial neural network based technique in which we used self-organized map. For trained our network, we use trace file, and these trace file work as input to self-organized map so that we provide supervised learning to our network. In this paper those malicious vehicles have been identified. We used SOM classifier for the detection of misbehavior node. In this classification the group of malicious nodes being created and for the improvement the use of Dempster-Shafer theory for finding attacker node is applied.

Port scanning is a reconnaissance phase of networking and many researchers have implemented different techniques to secure the network from port scan attacks. Intrusion Detection System (IDS) is also one of them and SNORT is an open source tool for Intrusion Detection and Prevention System. Today port scanning is a growing technology in network security to perform penetration testing and hacking and mostly researchers have focused in this field to detect stealth port scan attacks named as FIN scan, XMUS and NULL scan. To detect these attacks some of them used signature or rule-based technique and some are anomaly based techniques to improve security of network. In above techniques rules used FIN, PSH, and URG flag to detect attack but in case of idle port scan attack rules used FIN and RST flags which is also part of TCP connect() method so using this flag directly will generate the false alarm. In this paper we propose an IP identification number based detection plug-in to detect idle port scan attack. In this proposed techniques we will able to detect the idle port scan attack using FIN and RST flag with IP ID number of packet.