Intrusion detection system based on bagging with support vector machine

<p>Due to their rapid spread, computer worms perform harmful tasks in networks, posing a security risk; however, existing worm detection algorithms continue to struggle to achieve good performance and the reasons for that are: First, a large amount of irrelevant data affects classification accuracy. Second, individual classifiers do not detect all types of worms effectively. Third, many systems are based on outdated data, making them unsuitable for new worm species. The goal of the study is to use data mining algorithms to detect worms in the network because they have a high ability to detect new types accurately. The proposal is based on the UNSW NB15 dataset and uses a support vector machine to train and test the ensemble bagging algorithm. To detect various types of worms efficiently, the contribution suggests combining correlation and Chi2 feature selection method called Chi2-Corr to select relevant features and using support vector machine (SVM) in the bagging algorithm. The system achieved accuracy reaching 0.998 with Chi2-Corr, and 0.989, 0.992 with correlation and chi-square separately.</p>

Review Study on Different Attack Strategies of Worm in a Network

In today’s era Internet worm is a giant threat to the network infrastructure. Although there are different strategies to sense those hazard at early stages. They detect using some signature based approach. But when novel attacks come into the structure, it is very hard to detect them as they do not have any previous signature. For those some signature based methodology is used. In our work we have reviewed different strategies of internet worm detection and prevention and this article also explores the existing techniques to automate signatures for network worms.

Reducing Results Variance in Lifespan Machines: An Analysis of the Influence of Vibrotaxis on Wild-Type Caenorhabditis elegans for the Death Criterion

Nowadays, various artificial vision-based machines automate the lifespan assays of C. elegans. These automated machines present wider variability in results than manual assays because in the latter worms can be poked one by one to determine whether they are alive or not. Lifespan machines normally use a “dead or alive criterion” based on nematode position or pose changes, without poking worms. However, worms barely move on their last days of life, even though they are still alive. Therefore, a long monitoring period is necessary to observe motility in order to guarantee worms are actually dead, or a stimulus to prompt worm movement is required to reduce the lifespan variability measure. Here, a new automated vibrotaxis-based method for lifespan machines is proposed as a solution to prompt a motion response in all worms cultured on standard Petri plates in order to better distinguish between live and dead individuals. This simple automated method allows the stimulation of all animals through the whole plate at the same time and intensity, increasing the experiment throughput. The experimental results exhibited improved live-worm detection using this method, and most live nematodes (>93%) reacted to the vibration stimulus. This method increased machine sensitivity by decreasing results variance by approximately one half (from ±1 individual error per plate to ±0.6) and error in lifespan curve was reduced as well (from 2.6% to 1.2%).

Hook Worm Detection and its Classification Techniques

Wireless Capsule endoscopy (WCE) has transformed into a by and large used demonstrative strategy to look at some fiery infections and disarranges. Customized and completely robotized hookworm recognition and characterization models are testing task because of low nature of pictures, nearness of incidental issues, complex structure of gastrointestinal and various appearances to the extent shading and surface. There are a few endeavours were made to thoroughly research the robotized hookworm discovery from WCE pictures. A definite review is taken for identifying Hookworm in Endoscopy picture and its partner pre and post preparing specialized application. A profound report on AI system and highlight extraction approaches were examined. The different advances engaged with Hookworm location utilizing neural systems alongside their sorts were additionally talked about. The significant highlights which can be utilized for extricating the one of a kind highlights were considered.