Security is always an important issue especially in the case of computer network which is used to transfer personal/confidential information’s, ecommerce and media sharing. Since the network is closely related to operating its conditions hence a careful observation & analysis of network characteristics could describe the state of the network such as network is under specific attack or operating...

In this work we have presented a genetic algorithm approach for classifying normal connections and intrusions. We have created a serial combination of two light-weight genetic algorithm-based intrusion detection systems where each of the systems exhibits certain deficiency. In this way we have managed to mitigate the deficiencies of both of them. The model was verified on KDD99 intrusion detect...

In this paper we proposed Hierarchical Gaussian Mixture Model (HGMM) a novel type of Gaussian Mixture which detects network based attacks as anomalies using statistical preprocessing classification. This method learns patterns of normal and intrusive activities to classify that use a set of Gaussian probability distribution functions. The use of Maximum likelihood in detection phase has used th...

An intrusion detection system (IDS) is an active research topic and regarded as one of the important applications machine learning. IDS a classifier that predicts class input records associated with certain types attacks. In this article, we present review IDSs from perspective We three main challenges IDS, in general, for Internet Things (IoT), particular, namely concept drift, high dimensiona...

Technology has revolutionized into connecting “things” together with the rebirth of global network called Internet Things (IoT). This is achieved through Wireless Sensor Network (WSN) which introduces new security challenges for Information (IT) scientists and researchers. paper addresses issues in WSN by establishing potential automated solutions identifying associated risks. It also evaluates...

This work exploits the concept of one-class classifier applied to problem anomaly detection in communication networks. The article presents design an innovative algorithm based on polynomial interpolation technique and statistical analysis. method is datasets largely used scientific community for bench-marking such as KDD99, UNSW-NB15 CSE-CIC-IDS-2018, further evaluated with application a novel...

This paper represents the first step in an on-going work for designing an unsupervised method based on genetic algorithm for intrusion detection. Its main role in a broader system is to notify of an unusual traffic and in that way provide the possibility of detecting unknown attacks. Most of the machinelearning techniques deployed for intrusion detection are supervised as these techniques are g...

Recently, information security has become a key issue in information technology as the number of computer security breaches are exposed to an increasing number of security threats. A variety of intrusion detection systems (IDS) have been employed for protecting computers and networks from malicious network-based or host-based attacks by using traditional statistical methods to new data mining a...

During the last decade, anomaly detection has attracted the attention of many researchers to overcome the weakness of signature-based IDSs in detecting novel attacks, and KDDCUP’99 is the mostly widely used data set for the evaluation of these systems. As network attacks have increased in number and severity over the past few years, intrusion detection system (IDS) is increasingly becoming a cr...

Wireless Sensor Networks (WSNs) are used in many application areas including smart homes, military security applications, tracking applications and monitoring applications (oceans, animals, habitats). The number of these usage areas are increasing by day by. Currently, cyber attacks are a new battlefield and threat WSNs certainly. Protecting WSNs against attacks is an important topic and WSN ha...