Related papers: Intrusion Detection in Internet of Things using Co…

A significant increase in the number of interconnected devices and data communication through wireless networks has given rise to various threats, risks and security concerns. Internet of Things (IoT) applications is deployed in almost…

The exponential expansion of IoT and 5G-Advanced applications has enlarged the attack surface for DDoS, malware, and zero-day intrusions. We propose an intrusion detection system that fuses a convolutional neural network (CNN), a…

Despite decades of development, existing IDSs still face challenges in improving detection accuracy, evasion, and detection of unknown attacks. To solve these problems, many researchers have focused on designing and developing IDSs that use…

Network intrusion detection is critical for securing modern networks, yet the complexity of network traffic poses significant challenges to traditional methods. This study proposes a Temporal Convolutional Network(TCN) model featuring a…

In order to gain access to networks, different types of intrusion attacks have been designed, and the attackers are working on improving them. Computer networks have become increasingly important in daily life due to the increasing reliance…

Internet of Things (IoT) is transforming human lives by paving the way for the management of physical devices on the edge. These interconnected IoT objects share data for remote accessibility and can be vulnerable to open attacks and…

Intrusion Detection Systems (IDS) are a vital part of a network-connected device. In this paper, we develop a deep learning based intrusion detection system that is deployed in a distributed setup across devices connected to a network. Our…

As the number of IoT devices has increased rapidly, IoT botnets have exploited the vulnerabilities of IoT devices. However, it is still challenging to detect the initial intrusion on IoT devices prior to massive attacks. Recent studies have…

Security concerns for IoT applications have been alarming because of their widespread use in different enterprise systems. The potential threats to these applications are constantly emerging and changing, and therefore, sophisticated and…

Severe constraints on memory and computation characterizing the Internet-of-Things (IoT) units may prevent the execution of Deep Learning (DL)-based solutions, which typically demand large memory and high processing load. In order to…

Cyberattacks in an Internet of Things (IoT) environment can have significant impacts because of the interconnected nature of devices and systems. An attacker uses a network of compromised IoT devices in a botnet attack to carry out various…

Intrusion detection is one of the important mechanisms that provide computer networks security. Due to an increase in attacks and growing dependence upon other fields such as medicine, commerce, and engineering, offering services over a…

An application of software known as an Intrusion Detection System (IDS) employs machine algorithms to identify network intrusions. Selective logging, safeguarding privacy, reputation-based defense against numerous attacks, and dynamic…

The large number of sensors and actuators that make up the Internet of Things obliges these systems to use diverse technologies and protocols. This means that IoT networks are more heterogeneous than traditional networks. This gives rise to…

Wi-Fi networks are ubiquitous in both home and enterprise environments, serving as a primary medium for Internet access and forming the backbone of modern IoT ecosystems. However, their inherent vulnerabilities, combined with widespread…

The widespread integration of Internet of Things (IoT) devices across all facets of life has ushered in an era of interconnectedness, creating new avenues for cybersecurity challenges and underscoring the need for robust intrusion detection…

The expansion of Internet of Things (IoT) devices has increased the attack surface of networks, necessitating a robust and adaptive intrusion detection systems. Machine learning based systems have been considered promising in enhancing the…

This paper presents a study on detecting cyberattacks on industrial control systems (ICS) using unsupervised deep neural networks, specifically, convolutional neural networks. The study was performed on a SecureWater Treatment testbed…

As the number of connected IoT devices continues to grow, securing these systems against cyber threats remains a major challenge, especially in environments with limited computational and energy resources. This paper presents an…

Internet of Things (IoT) networks have become an increasingly attractive target of cyberattacks. Powerful Machine Learning (ML) models have recently been adopted to implement network intrusion detection systems to protect IoT networks. For…