Il Moon

17 papers Journal 17
YearRankTypeTitle / Venue / Authors
2026 J jnl
Hybrid modelling of chemical processes: a unified framework based on deductive, inductive, and abductive inference.
Comput. Chem. Eng.
Raymoon Hwang, Jae Hyun Cho, Il Moon, Min Oh
2025 J jnl
Explainable Artificial Intelligence for Fault Diagnosis of Industrial Processes.
IEEE Trans. Ind. Informatics
Kyojin Jang, Karl Ezra Salgado Pilario, Nayoung Lee, Il Moon, Jonggeol Na
2025 J jnl
Novel inverse predictive system integrated with industrial lubricant information.
Eng. Appl. Artif. Intell.
Minseong Kim, Chonghyo Joo, Jongkoo Lim, Seungho Yeom, Il Moon, Meng Qi, Junghwan Kim
2023 J jnl
A Dynamic Soft Sensor Based on Hybrid Neural Networks to Improve Early Off-spec Detection.
Eng. Comput.
Seokyoung Hong, Nahyeon An, Hyungtae Cho, Jongkoo Lim, In-Su Han, Il Moon, Junghwan Kim
2023 J jnl
Correction to: A dynamic soft sensor based on hybrid neural networks to improve early off-spec detection.
Eng. Comput.
Seokyoung Hong, Nahyeon An, Hyungtae Cho, Jongkoo Lim, In-Su Han, Il Moon, Junghwan Kim
2023 J jnl
Dual attention-based multi-step ahead prediction enhancement for monitoring systems in industrial processes.
Appl. Soft Comput.
Nahyeon An, Seokyoung Hong, Yurim Kim, Hyungtae Cho, Jongkoo Lim, Il Moon, Junghwan Kim
2023 J jnl
Multi-objective optimization of explosive waste treatment process considering environment via Bayesian active learning.
Eng. Appl. Artif. Intell.
Sunghyun Cho, Minsu Kim, Jaewon Lee, Areum Han, Jonggeol Na, Il Moon
2023 J jnl
Multi-objective robust optimization of profit for a naphtha cracking furnace considering uncertainties in the feed composition.
Expert Syst. Appl.
Jeongdong Kim, Chonghyo Joo, Minsu Kim, Nahyeon An, Hyungtae Cho, Il Moon, Junghwan Kim
2022 J jnl
Adversarial Autoencoder Based Feature Learning for Fault Detection in Industrial Processes.
IEEE Trans. Ind. Informatics
Kyojin Jang, Seokyoung Hong, Minsu Kim, Jonggeol Na, Il Moon
2022 J jnl
Development of novel flow distribution apparatus for simulated moving bed to improve degree of mixing.
Comput. Chem. Eng.
Youngjin Kim, Taejong Kim, Chanho Park, Jaewon Lee, Hyungtae Cho, Myungjun Kim, Il Moon
2022 J jnl
Time-series clustering approach for training data selection of a data-driven predictive model: Application to an industrial bio 2, 3-butanediol distillation process.
Comput. Chem. Eng.
Yeongryeol Choi, Nahyeon An, Seokyoung Hong, Hyungtae Cho, Jongkoo Lim, In-Su Han, Il Moon, Junghwan Kim
2021 J jnl
Techno-economic assessment of carbonate melt flue gas desulfurization process.
Comput. Chem. Eng.
Juwon Lee, Hyungtae Cho, Il Moon, Igor Lubomirsky, Valery Kaplan, Junghwan Kim, Yuchan Ahn
2020 J jnl
Finding the best operating condition in a novel process for explosive waste incineration using fluidized bed reactors.
Comput. Chem. Eng.
Sunghyun Cho, Chanho Park, Jiheon Lee, Byeongil Lyu, Il Moon
2020 J jnl
Spurious activation and operational integrity evaluation of redundant safety instrumented systems.
Reliab. Eng. Syst. Saf.
Meng Qi, Yufeng Kan, Xun Li, Xiaoying Wang, Dongfeng Zhao, Il Moon
2018 J jnl
A multistream heat exchanger model with enthalpy feasibility.
Comput. Chem. Eng.
Kyungjae Tak, Hweeung Kwon, Jaedeuk Park, Jae Hyun Cho, Il Moon
2017 J jnl
Safety Analysis Embedded in Heat Exchanger Network Synthesis.
Comput. Chem. Eng.
Andreja Nemet, Jirí Jaromír Klemes, Il Moon, Zdravko Kravanja
2016 J jnl
Optimization of naphtha purchase price using a price prediction model.
Comput. Chem. Eng.
Hweeung Kwon, Byeonggil Lyu, Kyungjae Tak, Jinsuk Lee, Jae Hyun Cho, Il Moon
redb/extractors/enum.py
← Index redb/extractors/enum.py python 
from enum import Enum


class Tag(Enum):
    BASIC_PROPERTIES = "basic_properties"
    DIEC = "detect_it_easy"
    CAPA = "capa"
    MALCONTENT = "malcontent"
    DECOMPILED = "decompiled"
    HASH = "hash"
    STRINGS = "strings"
    RELATED = "relatedness" # unused
    PE_FEATURES = "pe_features"
    PE_IMPORT = "pe_import"
    PE_OVERLAY = "pe_overlay"
    PE_SECTION = "pe_section"
    PE_RESOURCE = "pe_resource"
    PE_SIGNATURE = "pe_signature"
    PE_CERTIFICATE = "pe_certificate"
    PE_SIGNER = "pe_signer"
    PE_DOTNET = "pe_dotnet"
    PE_EMBEDDED_EXTRAS = "pe_embedded_extras"
    PE_INCONSISTENCY_TESTS = "pe_inconsistency_tests"
    DOTNET_INCONSISTENCY_TESTS = "dotnet_inconsistency_tests"
    YARA = "yara"
    # Mach-O
    MACHO_FEATURES = "macho_features"
    MACHO_IMPORT = "macho_import"
    MACHO_EXPORT = "macho_export"
    MACHO_DYLIB = "macho_dylib"
    MACHO_SEGMENT = "macho_segment"
    MACHO_SIGNATURE = "macho_signature"
    MACHO_CERTIFICATE = "macho_certificate"
    MACHO_ENTITLEMENT = "macho_entitlement"
    MACHO_UNIVERSAL = "macho_universal"
    MACHO_HASHES = "macho_hashes"
    # ELF
    ELF_FEATURES = "elf_features"
    ELF_IMPORT = "elf_import"
    ELF_EXPORT = "elf_export"
    ELF_SECTION = "elf_section"
    ELF_SEGMENT = "elf_segment"
    ELF_SYMBOL = "elf_symbol"
    ELF_RELOCATION = "elf_relocation"
    ELF_NOTE = "elf_note"
    ELF_DEPENDENCY = "elf_dependency"
    # APK
    APK_FEATURES = "apk_features"
    APK_MANIFEST = "apk_manifest"
    APK_PERMISSIONS = "apk_permissions"
    APK_SIGNATURE = "apk_signature"
    APK_DEX = "apk_dex"
    APK_RESOURCES = "apk_resources"
    APK_NATIVE_LIBS = "apk_native_libs"
    APK_INCONSISTENCY_TESTS = "apk_inconsistency_tests"
    APK_DECOMPILED = "apk_decompiled"
    # JavaScript
    JS_FEATURES = "js_features"
    JS_SUSPICIOUS_APIS = "js_suspicious_apis"
    JS_STRINGS = "js_strings"
    JS_DEOBFUSCATION = "js_deobfuscation"
    JS_CONTENT = "js_content"