Intrusion detection systems (IDSs) are based on two fundamental approaches first the recognition of anomalous activities as it turns from usual behavior and second misuse detection by observing those "signatures" of those recognized malicious assaults and classification vulnerabilities. Anomaly (behavior-based) IDSs presume the difference of normal behavior beneath attacks and achieve abnormal ...

Intrusion Detection System (IDS) is an important tool for protecting the Internet of Things (IoT) networks against cyber-attacks. Traditional IDSs can only distinguish between normal and abnormal behaviors. On other hand, modern techniques identify kind attack so that appropriate reactions be carried out each type attack. However, these always suffer from class-imbalance which affects performan...

MANTs are groups of mobiles hosts that arrange themselves into a grid lacking some preexist organization where the active network environment makes it simple in danger by an attacker. A node leaves out, and another enters network, making easy to penetration. This paper aims design new method intrusion detection MANET avoiding Denial Service (DoS) basis on neural networks Zone Sampling-Based Tra...

Abstract The use of supervised Machine Learning (ML) to enhance Intrusion Detection Systems (IDS) has been the subject significant research. Supervised ML is based upon learning by example, demanding volumes representative instances for effective training and need retrain model every unseen cyber-attack class. However, retraining models in-situ renders network susceptible attacks owing time-win...

Social networks have become more and more popular in recent years. This popularity creates a need for personalization services to recommend tweets, posts (information) and celebrities organizations (information sources) to users according to their potential interest. Tencent Weibo (microblog) data in KDD Cup 2012 brings one such challenge to the researchers in the knowledge discovery and data m...

This article describes the user modeling, feature extraction and bagged decision tree methods that were used to win 2 nd place student prize and 4 th place overall in the ACM’s 2010 KDD Cup.

Measuring research impact and ranking academic achievement are important and challenging problems. Having an objective picture of research institution is particularly valuable for students, parents and funding agencies, and also attracts attention from government and industry. KDD Cup 2016 proposes the paper acceptance rank prediction task, in which the participants are asked to rank the import...

We describe the method used in our final submission to KDD Cup 2009 as well as a selection of promising directions that are generally believed to work well but did not justify our expectations. Our final method consists of a combination of a LogitBoost and an ADTree classifier with a feature selection method that, as shaped by the experiments we have conducted, have turned out to be very differ...

Intrusion Detection System (IDS) is the science of detection of malicious activity on a computer network and the basic driver for network security. It is defined as a process of monitoring the events occurring in a computer system or network and analyzing them to differentiate between normal activities of the system and behaviors that can be classified as suspicious or intrusive. In this paper ...