Related papers: Real-Time Cyberattack Detection with Offline and O…

Over the past decade, the Cyberspace has seen an increasing number of attacks coming from botnets using the Peer-to-Peer (P2P) architecture. Peer-to-Peer botnets use a decentralized Command & Control architecture. Moreover, a large number…

The development and implementation of Internet of Things (IoT) devices have been accelerated dramatically in recent years. As a result, a super-network is required to handle the massive volumes of data collected and transmitted to these…

Many IoT devices are vulnerable to attacks due to flawed security designs and lacking mechanisms for firmware updates or patches to eliminate the security vulnerabilities. Device-type identification combined with data from vulnerability…

Deep Learning based AI systems have shown great promise in various domains such as vision, audio, autonomous systems (vehicles, drones), etc. Recent research on neural networks has shown the susceptibility of deep networks to adversarial…

APT, known as Advanced Persistent Threat, is a difficult challenge for cyber defence. These threats make many traditional defences ineffective as the vulnerabilities exploited by these threats are insiders who have access to and are within…

Cybercrime is a growing threat to organizations and individuals worldwide, with criminals using sophisticated techniques to breach security systems and steal sensitive data. This paper aims to comprehensively survey the latest advancements…

Deploying Connected and Automated Vehicles (CAVs) on top of 5G and Beyond networks (5GB) makes them vulnerable to increasing vectors of security and privacy attacks. In this context, a wide range of advanced machine/deep learning based…

With the recent developments in artificial intelligence and machine learning, anomalies in network traffic can be detected using machine learning approaches. Before the rise of machine learning, network anomalies which could imply an…

Traditional reactive approach of blacklisting botnets fails to adapt to the rapidly evolving landscape of cyberattacks. An automated and proactive approach to detect and block botnet hosts will immensely benefit the industry. Behavioral…

Recently, CNN object detectors have achieved high accuracy on remote sensing images but require huge labor and time costs on annotation. In this paper, we propose a new uncertainty-based active learning which can select images with more…

Network embedding aims to learn low-dimensional representations of nodes while capturing structure information of networks. It has achieved great success on many tasks of network analysis such as link prediction and node classification.…

The security pitfalls of IoT devices make it easy for the attackers to exploit the IoT devices and make them a part of a botnet. Once hundreds of thousands of IoT devices are compromised and become the part of a botnet, the attackers use…

Machine learning techniques are currently used extensively for automating various cybersecurity tasks. Most of these techniques utilize supervised learning algorithms that rely on training the algorithm to classify incoming data into…

The rapid advancement of artificial intelligence (AI) has significantly expanded the attack surface for AI-driven cybersecurity threats, necessitating adaptive defense strategies. This paper introduces CyberSentinel, a unified, single-agent…

In recent years, cyber-security of power systems has become a growing concern. To protect power systems from malicious adversaries, advanced defense strategies that exploit sophisticated detection algorithms are required. Motivated by this,…

The side-channel attack is an attack method based on the information gained about implementations of computer systems, rather than weaknesses in algorithms. Information about system characteristics such as power consumption, electromagnetic…

Cybersecurity has recently gained considerable interest in today's security issues because of the popularity of the Internet-of-Things (IoT), the considerable growth of mobile networks, and many related apps. Therefore, detecting numerous…

Android, being the most widespread mobile operating systems is increasingly becoming a target for malware. Malicious apps designed to turn mobile devices into bots that may form part of a larger botnet have become quite common, thus posing…

The Internet of Things (IoT) has witnessed unprecedented growth, resulting in a massive influx of diverse network traffic from interconnected devices. Effectively classifying this network traffic is crucial for optimizing resource…

The increasing popularity of web-based applications has led to several critical services being provided over the Internet. This has made it imperative to monitor the network traffic so as to prevent malicious attackers from depleting the…