A Comparative Study of Traffic Generators: Applicability for Malware Detection Testbeds

Swann, Matthew; Rose, Joseph; Bendiab, Gueltoum; Shiaeles, Stavros; Savage, Nick

doi:10.20533/jitst.2046.3723.2020.0085

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…Specialists assigned numerical scores to the elements that influence the values using a measure that is highlighted in Table 2. The numerical methods (3)(4)(5)(6) are used to convert numeric values into TFNs that are identified as (l ij , mi ij , and u ij ), where l ij is lower significance, mi ij is middle significance, and u ij is uppermost significance events. Moreover, TFN ( EER REVIEW 8 of 20 The numerical methods (3)(4)(5)(6) are used to convert numeric values into TFNs that are identified as (lij, miij, and uij), where lij is lower significance, miij is middle significance, and uij is uppermost significance events.…”

Section: Fuzzy Ahp-topsis Methodologymentioning

confidence: 99%

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Effectiveness Evaluation of Different IDSs Using Integrated Fuzzy MCDM Model

et al. 2022

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Cyber-attacks are becoming progressively complicated; hence, the functional issues of intrusion-detection systems (IDSs) present ever-growing challenges. Failing to detect intrusions may jeopardize the trustworthiness of security services, such as privacy preservation, authenticity, and accessibility. To fight these risks, different organizations nowadays use a variety of approaches, techniques, and technologies to safeguard the systems’ credibility. Establishing policies and procedures, raising user awareness, implementing firewall and verification systems, controlling system access, and building computer-issue management groups are all examples of safeguarding methods. There is a lack of sufficient emphasis on the effectiveness of intrusion-detection systems. In enterprises, IDS is used to analyze the potentially dangerous activities taking place within the technological settings. The selection of efficient IDS is a challenging task for organizations. This research evaluates the impact of five popular IDSs for their efficiency and effectiveness in information security. The authors used the fuzzy analytical hierarchy process (AHP) and fuzzy technique for order performance by similarity to ideal solution (TOPSIS)-based integrated multi-criteria decision-making (MCDM) methodology to evaluate the efficacy of the popular IDSs. The findings of this research suggest that most of the IDSs appear to be highly potential tools. Even though Snort is extensively deployed, Suricata has a substantial advantage over Snort. Suricata uses multi-threading functionality in comparison to Snort to boost the processing performance.

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Section: Fuzzy Ahp-topsis Methodologymentioning

confidence: 99%

“…The traffic information is moved in bulk and then analyzed for any unusual behavior in the data. The team explores threats by using known attack signatures, as well as trends [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Effectiveness Evaluation of Different IDSs Using Integrated Fuzzy MCDM Model

et al. 2022

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“…Genesids supports stateful packet generation only, but there is no direct control of more granular packet settings, e.g., flags. On the other hand, TRex has more control that can change fields inside the packet, including the headers, trailers, payload, and flags, and intentionally send erroneous packets to test how the network reacts [74]. We considered TRex stateless configuration because it creates several packet generation streams, including IoT traffic.…”

Section: Traffic Generationmentioning

confidence: 99%

QoS Performance Measurement Through SRv6 Network Programming for Smart City IoT Traffic

Kuri¹

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Segment Routing over IPv6, also known as SRv6, is a modern networking solution that aims to improve the current Internet of Things (IoT) network's reliability, availability, and scalability. Performance measures are required to evaluate SRv6 behaviors or functions. The proposed work aims to provide real IoT traffic profiles to assess the performance of SRv6 behaviors. In particular, a three-module SRv6 programming model is proposed to measure the performance of SRv6 policy headend and endpoint behavior and ensure reliability and quality of service (QoS). Moreover, a novel finder algorithm for maximum receive rate (MRR) benchmarking is proposed, which can outperform existing techniques in terms of throughput/bandwidth performance while maintaining the same computational resources. Finally, implementation results provide insights into forwarding different IoT use-cases traffic based on the functional service requirements. That also ensures a higher usage level of existing IoT networks, minimizing the need for additional capacity and lowering network costs.

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