Denial of service attack detection using trapezoidal fuzzy reasoning spiking neural P system

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Although ‘Intrusion’ is considered to be a bitter pill to swallow due to the havoc it unleashes on the cyber space, but it has become a household name to cyber-security experts because it appears to rebuff all possible solutions! Consequent upon this, there have been unrelenting efforts to reduce its negative impacts to the lowest ebb by the introduction of various Intrusion Detection Systems (IDS). Meanwhile, Spiking Neural P (SN P) system, a variant of Membrane Computing (MC), has proved to be a versatile class of distributed parallel computing model which embeds the idea of spiking neurons into P systems. Therefore, in this work, we have explored trapezoidal Fuzzy Reasoning Spiking Neural P (tFRSN P) system, which is an extension of SN P system in attack detection. Specifically, the focus is on detecting Denial-of-Service (DoS) attack with emphasis on SYN (synchronize) flood. Consequently, KDD Cup benchmark dataset was used for evaluation in series of experiments conducted. While we obtained very low False Negatives (FN) and False Positives (FP) of 0.02% and 0.25% respectively, the True Positives/Negatives were equally very high. These results have further lent credence to the fact that MC and indeed SN P system are yet-to-be tapped goldmine as far as Intrusion Detection is concerned.

Original languageEnglish
Pages (from-to)397-404
Number of pages8
JournalJournal of Theoretical and Applied Information Technology
Volume75
Issue number3
Publication statusPublished - 1 May 2015

Fingerprint

Fuzzy Reasoning
Denial of Service
P Systems
Intrusion detection
Attack
Membranes
Membrane Computing
Parallel processing systems
Neurons
Intrusion Detection
Spiking Neurons
Parallel Computing
Distributed Computing
False Positive
Experiments
Lowest
Denial-of-service attack
Benchmark
Series
Evaluation

Keywords

  • Attack detection
  • Denial-of-service
  • Fuzzy reasoning
  • Membrane computing
  • SNP systems

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

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title = "Denial of service attack detection using trapezoidal fuzzy reasoning spiking neural P system",
abstract = "Although ‘Intrusion’ is considered to be a bitter pill to swallow due to the havoc it unleashes on the cyber space, but it has become a household name to cyber-security experts because it appears to rebuff all possible solutions! Consequent upon this, there have been unrelenting efforts to reduce its negative impacts to the lowest ebb by the introduction of various Intrusion Detection Systems (IDS). Meanwhile, Spiking Neural P (SN P) system, a variant of Membrane Computing (MC), has proved to be a versatile class of distributed parallel computing model which embeds the idea of spiking neurons into P systems. Therefore, in this work, we have explored trapezoidal Fuzzy Reasoning Spiking Neural P (tFRSN P) system, which is an extension of SN P system in attack detection. Specifically, the focus is on detecting Denial-of-Service (DoS) attack with emphasis on SYN (synchronize) flood. Consequently, KDD Cup benchmark dataset was used for evaluation in series of experiments conducted. While we obtained very low False Negatives (FN) and False Positives (FP) of 0.02{\%} and 0.25{\%} respectively, the True Positives/Negatives were equally very high. These results have further lent credence to the fact that MC and indeed SN P system are yet-to-be tapped goldmine as far as Intrusion Detection is concerned.",
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