Results 11 to 20 of about 1,396 (174)

Explainable artificial intelligence for botnet detection in internet of things [PDF]

Scientific Reports
The proliferation of internet of things (IoT) devices has led to unprecedented connectivity and convenience. However, this increased interconnectivity has also introduced significant security challenges, particularly concerning the detection and ...
Mohamed Saied, Shawkat Guirguis
doaj   +2 more sources

Examination of Traditional Botnet Detection on IoT-Based Bots [PDF]

Sensors
A botnet is a collection of Internet-connected computers that have been suborned and are controlled externally for malicious purposes. Concomitant with the growth of the Internet of Things (IoT), botnets have been expanding to use IoT devices as their ...
Ashley Woodiss-Field, Michael N. Johnstone, Paul Haskell-Dowland   +2 more
doaj   +2 more sources

Application of representation learning in detecting botnet attacks [PDF]

Scientific Reports
Botnet detection remains a perennial and critical challenge in cybersecurity. As long as the internet exists, threat actors will devise new ways to create and disguise these malicious networks, making the development of robust detection methods a task ...
Hieu Le Ngoc
doaj   +2 more sources

Adaptive secure malware efficient machine learning algorithm for healthcare data

CAAI Transactions on Intelligence Technology, EarlyView., 2023
Abstract Malware software now encrypts the data of Internet of Things (IoT) enabled fog nodes, preventing the victim from accessing it unless they pay a ransom to the attacker. The ransom injunction is constantly accompanied by a deadline. These days, ransomware attacks are too common on IoT healthcare devices.
Mazin Abed Mohammed, Abdullah Lakhan, Dilovan Asaad Zebari, Karrar Hameed Abdulkareem, Jan Nedoma, Radek Martinek, Usman Tariq, Majed Alhaisoni, Prayag Tiwari   +8 more
wiley   +1 more source

On the detection and identification of botnets [PDF]

Computers & Security, 2010
We develop and discuss automated and self-adaptive systems for detecting and classifying botnets based on machine learning techniques and integration of human expertise. The proposed concept is purely passive and is based on analyzing information collected at three levels: (i) the payload of single packets received, (ii) observed access patterns to a ...
Alexander K. Seewald, Wilfried N. Gansterer   +1 more
openaire   +2 more sources

MONDEO: Multistage Botnet Detection

CoRR, 2023
Mobile devices have widespread to become the most used piece of technology. Due to their characteristics, they have become major targets for botnet-related malware. FluBot is one example of botnet malware that infects mobile devices. In particular, FluBot is a DNS-based botnet that uses Domain Generation Algorithms (DGA) to establish communication with
Duarte Dias, Bruno Sousa, Nuno Antunes
openaire   +2 more sources

BotNet Detection on Social Media

CoRR, 2021
As our reliance on social media platforms and web services increase day by day, exploiters view these platforms as an opportunity to manipulate our thoughts ad actions. These platforms have become an open playground for social bot accounts. Social bots not only learn human conversations, manners, and presence but also manipulate public opinion, act as ...
Aniket Chandrakant Devle, Julia Ann Jose, Abhay Shrinivas Saraswathula, Shubham Mehta, Siddhant Srivastava, Sirisha Kona, Sudheera Daggumalli   +6 more
openaire   +2 more sources

Detecting a botnet in a network [PDF]

Mathematical Statistics and Learning, 2021
We formalize the problem of detecting the presence of a botnet in a network as a hypothesis testing problem where we observe a single instance of a graph. The null hypothesis, corresponding to the absence of a botnet, is modeled as a random geometric graph where every vertex is assigned a location on a
Gianmarco Bet, Kay Bogerd, Rui M. Castro, Remco van der Hofstad   +3 more
openaire   +4 more sources

Botnet Vulnerability Intelligence Clustering Classification Mining and Countermeasure Algorithm Based on Machine Learning

IEEE Access, 2019
Botnet detection, vulnerability mining and confrontation bring many challenges to network security, and become the main dangerous source threatening economic development and causing significant economic losses. To solve these problems, we combine machine
Zenan Chu, Yi Han, Kai Zhao
doaj   +1 more source

Multilayer Framework for Botnet Detection Using Machine Learning Algorithms

IEEE Access, 2021
A botnet is a malware program that a hacker remotely controls called a botmaster. Botnet can perform massive cyber-attacks such as DDOS, SPAM, click-fraud, information, and identity stealing. The botnet also can avoid being detected by a security system.
Wan Nur Hidayah Ibrahim, Syahid Anuar, Ali Selamat, Ondrej Krejcar, Ruben Gonzalez Crespo, Enrique Herrera-Viedma, Hamido Fujita   +6 more
doaj   +1 more source