The objective of this research is to propose an efficient soft computing approach with high detection rates and low false alarms while maintaining low cost and shorter detection time for intrusion detection. Our results were promising as they showed the new proposed system, hybrid feature selection approach of Linear Discriminant Analysis and Genetic Algorithm (GA) called Genetic Linear Discrim...

Communication intrusion detection in Advanced Metering Infrastructure (AMI) is an eminent security technology to ensure the stable operation of Smart Grid. However, methods based on traditional machine learning are not appropriate for high-dimensional features and dealing with data imbalance communication traffic AMI. To solve above problems, we propose scheme by combining feature dimensionalit...

Anomaly detection from Big Cybersecurity Datasets is very important; however, this a challenging and computationally expensive task. Feature selection (FS) an approach to remove irrelevant redundant features select subset of features, which can improve the machine learning algorithms’ performance. In fact, FS effective preprocessing step anomaly techniques. This article’s main objective quantif...

With the developing technology, need for dissemination and protection of information is becoming increasingly important. Recently, attacks on systems have increased significantly. In addition to rise in number attacks, different types pose a great threat systems. As result these institutions users suffer serious damages. At this point, Intrusion Detection Systems (IDS) very important position. ...

In order to construct an IDS that is both computationally effective and efficient, the goal of this work pinpoint significant decreased input characteristics. For this, we use information gain, gain ratio, correlation-based feature selection examine effectiveness three common techniques. NSL KDD dataset identify assaults on four attack types: Probe (information gathering), DoS (denial service),...

An advanced metering infrastructure (AMI) system plays a key role in the smart grid (SG), but it is vulnerable to cyberattacks. Current detection methods for AMI cyberattacks mainly focus on data center or distributed independent node. On one hand, difficult train an excellent intrusion model self-learning other large amounts of are shared over network and uploaded central node training. These ...

Malicious attacks are becoming more prevalent due to the growing use of Internet Things (IoT) devices in homes, offices, transportation, healthcare, and other locations. By incorporating fog computing into IoT, can be detected a short amount time, as distance between IoT is smaller than cloud. Machine learning frequently used for detection huge data available from devices. However, problem that...

Today’s internets are made up of nearly half a million different networks. In any network connection, identifying the attacks by their types is difficult task as may have various connections, and number vary from few to hundreds connections. To solve this problem, novel hybrid IDS called NID-Shield proposed in manuscript that classifies dataset according attack types. Furthermore, names found c...