Revolutionizing Threat Hunting in Communication Networks: Introducing a Cutting-Edge Large-Scale Multiclass Dataset

Published: Aug. 13, 2024

The rapid advancements in digital technologies are revolutionizing our world, bringing forth new possibilities and opportunities every second. This has created a huge concern regarding the security of systems connected to network. Since amounts data traveling through worldwide networks, many threats have become priority consider. Traditional network uses rule-based methods detect abnormalities, these struggle survive with evolving malicious activities that becoming increasingly advanced. In this paper, we develop threat-hunting model for communication networks introduce novel, cutting-edge, large-scale multiclass dataset improve cognition suspicious traffic networks. paper dives into effective collection preprocessing ensure high learning curve intelligent models, especially those trained on fine data. proposed newly generated contains up-to-date samples features available public help reduce effect upcoming cyberattacks machine methods. Specifically, 6 million 60 collected organized two balanced classes: 50% normal anomaly (attack) traffic. Furthermore, is composed 15 different attacks including MITM-ARP-SPOOFING attack, SSH-BRUTE FORCE FTP-BRUTE DDOS-ICMP, DDOS-RAWIP DDOS-UDP DOS EXPLOITING-FTP FUZZING ICMP FLOOD SYN-FLOOD PORT SCANNING REMOTE CODE EXECUTION SQL INJECTION XSS attack. expected contribute positively We will work automating detection any empower organizations.

Language: Английский

Organizational Readiness for Artificial Intelligence (AI) in Network Security

Advances in human resources management and organizational development book series, Journal Year: 2024, Volume and Issue: unknown, P. 205 - 246

Published: Nov. 15, 2024

This chapter explores the essential organizational and cultural prerequisites for successfully integrating Artificial Intelligence (AI) into network security. research employs a qualitative methodology, including comprehensive literature review, to analyze internal needs address ethical considerations such as bias, privacy, fairness. study examines impact of culture on acceptance effectiveness AI-based solutions. It emphasizes significance end-user trust in AI-driven security alerts. The findings highlight necessity readiness adaptation effective implementation AI security, concluding that approach is maximizing AI's potential enhancing measures. will benefit cybersecurity professionals, leaders, policymakers seeking understand navigate complexities integration

Language: Английский

Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks

Algorithms, Journal Year: 2023, Volume and Issue: 16(12), P. 549 - 549

Published: Nov. 29, 2023

As computer networks become increasingly important in various domains, the need for secure and reliable becomes more pressing, particularly context of blockchain-enabled supply chain networks. One way to ensure network security is by using intrusion detection systems (IDSs), which are specialised devices that detect anomalies attacks network. However, these vulnerable data poisoning attacks, such as label distance-based flipping, can undermine their effectiveness within In this research paper, we investigate effect on a system several machine learning models, including logistic regression, random forest, SVC, XGB Classifier, evaluate each model via F1 Score, confusion matrix, accuracy. We run three times: once without any attack, with flipping randomness 20%, distance threshold 0.5. Additionally, tests an eight-layer neural accuracy metrics classification report library. The primary goal provide insights into models By doing so, aim contribute developing robust tailored specific challenges securing blockchain-based

Language: Английский