Related papers: Mitigating Botnet Attack Using Encapsulated Detect…

The web bots have been blamed for consuming large amount of Internet traffic and undermining the interest of the scraped sites for years. Traditional bot detection studies focus mainly on signature-based solution, but advanced bots usually…

With a growing increase in botnet attacks, computer networks are constantly under threat from attacks that cripple cyber-infrastructure. Detecting these attacks in real-time proves to be a difficult and resource intensive task. One of the…

A bot is a piece of software that is usually installed on an infected machine without the user's knowledge. A bot is controlled remotely by the attacker under a Command and Control structure. Recent statistics show that bots represent one…

In the age of the Internet, people's lives are increasingly dependent on today's network technology. Maintaining network integrity and protecting the legitimate interests of users is at the heart of network construction. Threat detection is…

Internet security is getting less secure because of the existing of botnet threats. An attack plan can only be planned out to take down the botnet after the monitoring activities to understand the behaviour of a botnet. Nowadays, the…

A recent study has found that malicious bots generated nearly a quarter of overall website traffic in 2019 [100]. These malicious bots perform activities such as price and content scraping, account creation and takeover, credit card fraud,…

The distributed denial of service (DDoS) attack is detrimental to the industrial Internet of things (IIoT) as it triggers severe resource starvation on networked objects. Recent dynamics demonstrate that it is a highly profitable business…

Botnets are computer networks controlled by malicious actors that present significant cybersecurity challenges. They autonomously infect, propagate, and coordinate to conduct cybercrimes, necessitating robust detection methods. This…

The Internet as we know it Today, comprises several fundamental interrelated networks, among which is the Internet of Things (IoT). Despite their versatility, several IoT devices are vulnerable from a security perspective, which renders…

We employ network embedding to detect money laundering in financial transaction networks. Using real anonymized banking data, we model over one million accounts as a directed graph and use it to refine previously detected suspicious cycles…

As the online service industry has continued to grow, illegal activities in the online world have drastically increased and become more diverse. Most illegal activities occur continuously because cyber assets, such as game items and cyber…

Botnets could autonomously infect, propagate, communicate and coordinate with other members in the botnet, enabling cybercriminals to exploit the cumulative computing and bandwidth of its bots to facilitate cybercrime. Traditional detection…

The acceptance of Internet of Things (IoT) applications and services has seen an enormous rise of interest in IoT. Organizations have begun to create various IoT based gadgets ranging from small personal devices such as a smart watch to a…

Bots constitute a significant portion of Internet traffic and are a source of various issues across multiple domains. Modern bots often become indistinguishable from real users, as they employ similar methods to browse the web, including…

The proliferation of IoT systems, has seen them targeted by malicious third parties. To address this, realistic protection and investigation countermeasures need to be developed. Such countermeasures include network intrusion detection and…

As the internet continues to be populated with new devices and emerging technologies, the attack surface grows exponentially. Technology is shifting towards a profit-driven Internet of Things market where security is an afterthought.…

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…

Botnet detection based on machine learning have witnessed significant leaps in recent years, with the availability of large and reliable datasets that are extracted from real-life scenarios. Consequently, adversarial attacks on machine…

Deep neural networks (DNNs) are vulnerable to adversarial attack which is maliciously implemented by adding human-imperceptible perturbation to images and thus leads to incorrect prediction. Existing studies have proposed various methods to…

Botnet detection is a critical step in stopping the spread of botnets and preventing malicious activities. However, reliable detection is still a challenging task, due to a wide variety of botnets involving ever-increasing types of devices…