As the dependence of daily life is increasing on Internet technology, the attacks on the systems, servers are also rapidly increasing. The motives of attacks are to steal the confidential data from the systems or making the system unavailable to the authorised users. An effective approach is required to detect the intrusions to provide the defence to the Networks. First we applied the feature s...

Feature Selection in large multi-dimensional data sets is becoming increasingly important for several real world applications. One such application, used by network administrators, is Network Intrusion Detection. The major problem with anomaly based intrusion detection systems is high number of false positives. Motivated by such a requirement, we propose sv(M)kmeans: a two step hybrid feature s...

With the vulgarization of Internet, the easy access to its resources and the rapid growth in the number of computers and networks, the security of information systems has become a crucial topic of research and development especially in the field of intrusion detection. Techniques such as machine learning and data mining are widely used in anomaly-detection schemes to decide whether or not a mal...

Current network security is becoming increasingly important, and intrusion detection an effective method to protect the from malicious attacks. This study proposes algorithm FLTrELM based on federated transfer learning extreme machine improve effect of detection, which implements data aggregation through facilitates construction personalized for all organizations. first builds a model solve pro...

The Internet of Things (IoT) integrates billions self-organized and heterogeneous smart nodes that communicate with each other without human intervention. In recent years, IoT based systems have been used in improving the experience many applications including healthcare, agriculture, supply chain, education, transportation traffic monitoring, utility services etc. However, node heterogeneity r...

An intrusion detection system, often known as an IDS, is extremely important for preventing attacks on a network, violating network policies, and gaining unauthorized access to network. The effectiveness of IDS highly dependent data preprocessing techniques classification models used enhance accuracy reduce model training testing time. For the purpose anomaly identification, researchers have de...

In the last decade, number of attacks on internet has grown significantly, and types vary widely. This causes huge financial losses in various institutions such as private government sectors. One efforts to deal with this problem is by early detection attacks, often called IDS (instruction system). The intrusion system was deactivated. An Intrusion Detection System (IDS) a hardware or software ...

Modern smart grids are built based on top of advanced computing and networking technologies, where condition monitoring relies secure cyberphysical connectivity. Over the network infrastructure, transported data containing confidential information, must be protected as vulnerable subject to various cyberattacks. Various machine learning classifiers were proposed for intrusion detection in grids...

One of the main problems in Network Intrusion Detection comes from constant rise of new attacks, so that not enough labeled examples are available for the new classes of attacks. Traditional Machine Learning approaches hardly address such problem. This can be overcome with Zero-Shot Learning, a new approach in the field of Computer Vision, which can be described in two stages: the Attribute Lea...

The recent development of cloud computing offers various services on demand for organization and individual users, such as storage, shared space, networking, etc. Although Cloud Computing provides advantages it remains vulnerable to many types attacks that attract cyber criminals. Distributed Denial Service (DDoS) is the most common type attack computing. Consequently, professionals security ex...