Abstract Due to the increasing growth of Internet and its widespread application, number attacks on network has also increased. Therefore, maintaining security using intrusion detection systems is critical importance. The connection between devices leads a large data being generated saved. era “big data” emerges over time. This paper presents new method for selecting effective features based co...

Wireless sensor networks (WSNs) are an emerging military and civilian technology that uses sensors. Sensor hierarchical chaotic in remote, unmonitored sites. pose unique security threats due to their public location wireless transmission. WSNs vulnerable various routing attacks, including Black holes, Sybil, sinkholes wormholes. In this paper, we proposed advanced intrusion detection systems ba...

Kajal Rai Research Scholar, Department of Computer Science and Applications, Panjab University, Chandigarh, India Email: [email protected] M. Syamala Devi Professor, Department of Computer Science and Applications, Panjab University, Chandigarh, India Email: [email protected] Ajay Guleria System Manager, Computer Center, Panjab University, Chandigarh, India Email: [email protected] -------------------...

Software-defined networking (SDN) is a new paradigm that allows developing more flexible network applications. SDN controller, which represents a centralized controlling point, is responsible for running various network applications as well as maintaining different network services and functionalities. Choosing an efficient intrusion detection system helps in reducing the overhead of the runnin...

Increase in volume and intensity of network attacks, forcing the business systems to revamp their network security solutions in order to avoid huge financial losses. Intrusion Detection Systems are one of the most essential security solutions in order to ensure the security of any network. Considering huge volumes of network data and complex nature of intrusions, the performance optimization of...

Cyber crimes and malicious network activities have posed serious threats to the entire internet and its users. This issue is becoming more critical, as network-based services, are more widespread and closely related to our daily life. Thus, it has raised a serious concern in individual internet users, industry and research community. A significant amount of work has been conducted to develop in...

As the communication industry has connected distant corners of the globe using advances in network technology, intruders or attackers have also increased attacks on networking infrastructure commensurately. System administrators can attempt to prevent such attacks by using intrusion detection tools and systems. In recent years Machine Learning (ML) algorithms has been gaining popularity in Intr...

Network intrusion detection focuses on classifying network traffic as either normal or attack carrier. The classification is based information extracted from the flow packets. This a complex problem with unbalanced datasets and noisy data. work extends classic radial basis function (RBF) neural by including it policy in an offline reinforcement learning algorithm. With this approach, all parame...

A network intrusion detection model that fuses a convolutional neural and gated recurrent unit is proposed to address the problems associated with low accuracy of existing models for multiple classification intrusions class imbalance data detection. In this model, hybrid sampling algorithm combining Adaptive Synthetic Sampling (ADASYN) Repeated Edited nearest neighbors (RENN) used sample proces...

The rise of the new generation cyber threats demands more sophisticated and intelligent defense solutions equipped with autonomous agents capable learning to make decisions without knowledge human experts. Several reinforcement methods (e.g., Markov) for automated network intrusion tasks have been proposed in recent years. In this paper, we introduce a detection method, which combines Q-learnin...