Related papers: Network attack detection at flow level

Because of the open nature of the Wireless Sensor Networks (WSN), the Denial of the Service (DoS) becomes one of the most serious threats to the stability of the resourceconstrained sensor nodes. In this paper, we develop AccFlow which is…

In this paper we introduce an intrusion detection system for Denial of Service (DoS) attacks against Domain Name System (DNS). Our system architecture consists of two most important parts: a statistical preprocessor and a neural network…

An Intrusion Detection System (IDS) is a software that monitors a single or a network of computers for malicious activities (attacks) that are aimed at stealing or censoring information or corrupting network protocols. Most techniques used…

DDoS attacks are one of the most prevalent and harmful cybersecurity threats faced by organizations and individuals today. In recent years, the complexity and frequency of DDoS attacks have increased significantly, making it challenging to…

In this paper, we analyze several recent schemes for watermarking network flows that are based on splitting the flow into timing intervals. We show that this approach creates time-dependent correlations that enable an attack that combines…

The rapid expansion of Internet of Things (IoT) networks has introduced new security challenges, necessitating efficient and reliable methods for intrusion detection. In this study, a detection framework based on hyperdimensional computing…

In this work we investigate a new approach for detecting attacks which aim to degrade the network's Quality of Service (QoS). To this end, a new network-based intrusion detection system (NIDS) is proposed. Most contemporary NIDSs take a…

It is not been a long time since the advent of cloud-based technology. However, in this short period of timeseveral advantages and disadvantages have been emerged. This is a problem solving technology with some threats as well. These…

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…

In this paper we report our experiment concerning new attacks detection by a neural network-based Intrusion Detection System. What is crucial for this topic is the adaptation of the neural network that is already in use to correct…

Software-defined networking (SDN) is a promising technology to overcome many challenges in wireless sensor networks (WSN), particularly with respect to flexibility and reuse. Conversely, the centralization and the planes' separation turn…

As the number of cyberattacks and their particualr nature escalate, the need for effective intrusion detection systems (IDS) has become indispensable for ensuring the security of contemporary networks. Adaptive and more sophisticated…

Information Centric Networks (ICNs) have emerged in recent years as a new networking paradigm for the next-generation Internet. The primary goal of these networks is to provide effective mechanisms for content distribution and retrieval…

Today by growing network systems, security is a key feature of each network infrastructure. Network Intrusion Detection Systems (IDS) provide defense model for all security threats which are harmful to any network. The IDS could detect and…

Signature-based and protocol-based intrusion detection systems (IDS) are employed as means to reveal content-based network attacks. Such systems have proven to be effective in identifying known intrusion attempts and exploits but they fail…

Statistical characteristics of network traffic have attracted a significant amount of research for automated network intrusion detection, some of which looked at applications of natural statistical laws such as Zipf's law, Benford's law and…

We propose a novel approach for distributed statistical detection of change-points in high-volume network traffic. We consider more specifically the task of detecting and identifying the targets of Distributed Denial of Service (DDoS)…

This paper presents a simple yet efficient method for an anomaly-based Intrusion Detection System (IDS). In reality, IDSs can be defined as a one-class classification system, where the normal traffic is the target class. The high diversity…

Focus on Deep Neural Network based malicious and normal computer Network Traffic classification. (such as attacks, phishing, any other illegal activity and normal traffic identification). In this paper, the main idea is to review, existed…

In this paper we focus on the detection of network anomalies like Denial of Service (DoS) attacks and port scans in a unified manner. While there has been an extensive amount of research in network anomaly detection, current state of the…