Accurate detection of network anomalies within SNMP-MIB data set using deep learning

Naymat, Ghazi Al; Hussain, Hanan; Kasassbeh, Mouhammd Al; Dmour, Nidal Al

doi:10.1504/ijcat.2021.119606

Cited by 24 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Starting off with the Likert scale data analysis, the table below illustrates the results of the scale based on what issues each interviewee felt was most important to cover [77,78,79,80]. Each issue is rated from 1 to 5, 5 being the most important and relevant [29,30,31,32,34,35]:…”

Section: A Methodologymentioning

confidence: 99%

Studying the Metaverse Effect on its Users

Ghazal

Issa

Dmour

et al. 2022

PJETS

View full text Add to dashboard Cite

This research is set to answer the question how Metaverse can be a dangerous space for its users. Metaverse is a collection of Meta applications such as Facebook, Instagram, WhatsApp and Oculus that is integrated into virtual reality (VR) and augmented reality (AR). To answer the question, different issues that were undertaken in the history of Meta and its CEO, Mark Zuckerberg, was taken into account to see the potential negative effects it will have on end-users. The potential issues Metaverse can have includes issues with uneven power distribution, issues with cyber security and privacy threats, and negative impact on the user’s mental health. An interview was conducted and the results supported the prior claims, with an emphasis on the dangers of lack of security and privacy leaks and breaches, followed by mental health concerns, then lack of regulations, followed by spread of misinformation and abuse of power. The solutions to these issues consisted of adding and adopting better regulations, integrating a monitoring system, and including a function for tracking and limiting end-user’s screentime. Alas, technology and the internet are ever-changing and needs constant evaluation and regulations, especially if it is from a big tech-company such as Meta. A company is useless without its customers, so keeping them safe should always be a top priority.

show abstract

Section: A Methodologymentioning

confidence: 99%

Studying the Metaverse Effect on its Users

Ghazal

Issa

Dmour

et al. 2022

PJETS

View full text Add to dashboard Cite

show abstract

“…A model developed using Rule-based classifiers including DecisionTable, JRip, OneR, PART and ZeroR detected DoS attacks by leveraging the ICMP variables from the MIB, achieving 99.7% accuracy [16]. Deep learning methods like Stacked Autoencoders applied to MIB data enabled accurate network anomaly detection without complex feature engineering [17].Overall, these works establish SNMP MIB data as an invaluable input for modern AI-driven IDS, providing network visibility unparalleled by other data sources.…”

Section: A) Intrusion Detection Systemsmentioning

confidence: 96%

The Critical Role of SNMP in Enabling Network Security

alkasassbeh

2023

Preprint

View full text Add to dashboard Cite

Simple Network Management Protocol (SNMP) is extensively utilized for monitoring and managing computer networks. This paper synthesizes and reviews recent research on harnessing SNMP data for developing effective network security solutions driven by artificial intelligence and machine learning techniques. The comprehensive device-level visibility and timely data provided by SNMP offers significant potential to enable high-performance intrusion detection systems, security analytics, and real-time monitoring capabilities to protect against evolving cyber threats. However, inherent vulnerabilities in versions like SNMP v1/v2c persist, especially due to default community strings allowing uncontrolled access. A number of techniques like newer SNMPv3 standard have been proposed to improve security through encryption, authentication, and fine-grained access control. Additionally, general best practices around firewalling, traffic monitoring, and compliance audits are recommended for robust SNMP deployment. The literature reviewed demonstrates SNMP’s capabilities in enabling anomaly detection with over 99% accuracy by applying supervised learning methods like random forests and neural networks to SNMP Management Information Base (MIB) data. The network-centric visibility offered by SNMP's extensive statistics on traffic configurations and behaviors provides an invaluable advantage over packetlevel data for developing intelligent detection models. Beyond intrusion detection systems, SNMP data has also shown significant utility for security analytics like attack pattern discovery, bandwidth modeling, and large-scale threat intelligence by aggregating data from distributed network devices. Further research opportunities exist in areas like feature engineering, model optimization, and continued evaluation of evolving attacks patterns. In conclusion, with appropriate secure configurations, SNMP provides the comprehensive and real-time data needed by modern artificial intelligence security applications in a scalable manner due to its device-centric focus, standardized schema, and ubiquitous deployment

show abstract