A novel hybrid immune clonal selection algorithm for the constrained corridor allocation problem

Liu, Junqi; Zhang, Zeqiang; Chen, Feng; Liu, Silu; Li-xia, Zhu

doi:10.1007/s10845-020-01693-9

Cited by 8 publications

(1 citation statement)

References 53 publications

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“…e memory cells of the set M i ′ will be replaced by other selected cells if and only if these cells show a better quality of connection, which means that the condition f(m, g i ) < f(a, g i ), m ∈ M u ′ , a ∈ B ′ , must apply [30].…”

Section: Clone Selection and Memory Refreshmentioning

confidence: 99%

Detecting Portable Executable Malware by Binary Code Using an Artificial Evolutionary Fuzzy LSTM Immune System

Jian

Zhang

2021

Security and Communication Networks

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As the planet watches in shock the evolution of the COVID-19 pandemic, new forms of sophisticated, versatile, and extremely difficult-to-detect malware expose society and especially the global economy. Machine learning techniques are posing an increasingly important role in the field of malware identification and analysis. However, due to the complexity of the problem, the training of intelligent systems proves to be insufficient in recognizing advanced cyberthreats. The biggest challenge in information systems security using machine learning methods is to understand the polymorphism and metamorphism mechanisms used by malware developers and how to effectively address them. This work presents an innovative Artificial Evolutionary Fuzzy LSTM Immune System which, by using a heuristic machine learning method that combines evolutionary intelligence, Long-Short-Term Memory (LSTM), and fuzzy knowledge, proves to be able to adequately protect modern information system from Portable Executable Malware. The main innovation in the technical implementation of the proposed approach is the fact that the machine learning system can only be trained from raw bytes of an executable file to determine if the file is malicious. The performance of the proposed system was tested on a sophisticated dataset of high complexity, which emerged after extensive research on PE malware that offered us a realistic representation of their operating states. The high accuracy of the developed model significantly supports the validity of the proposed method. The final evaluation was carried out with in-depth comparisons to corresponding machine learning algorithms and it has revealed the superiority of the proposed immune system.

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Section: Clone Selection and Memory Refreshmentioning

confidence: 99%