CyberCrimeClassificationUsingNLP/CaseTransformToOWL.py at main · EfstratiosLontzetidis/CyberCrimeClassificationUsingNLP · GitHub

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from owlready2 import *
from sklearn.feature_extraction.text import TfidfVectorizer
import os

def read_text_file(file_path):
    with open(file_path, 'r',encoding=("UTF-8")) as f:
        return f.read()

def case_to_owl(cases_prefix):
    print("The imported cases are implemented in the existing ontology as individuals and are associated with their respective characteristics (data properties) using NLP techniques (comparing similarity of documents) on the descriptions of the existing literature\n")
    print("****Algorithm: COSINE SIMILARITY********")
    # load ontology
    crime = get_ontology("file://CyberCrimeOntology.owl").load()

    characteristics_prefix=["./descriptions/characteristics/offender","./descriptions/characteristics/access violation","./descriptions/characteristics/victim","./descriptions/characteristics/target","./descriptions/characteristics/harm"]
    cases_prefix="./descriptions/cases"

    print("Starting to read cases from folder "+ cases_prefix)
    print("\n")
    filestochecksimilarity = []
    # for each crime find its characteristics
    for file in os.listdir(cases_prefix):
        # Check whether file is in text format or not
        file_path = cases_prefix+"/"+file
        # call read text file function
        case_name=file.replace(".txt","")
        print("CASE NAME: "+case_name)
        new_case = Thing(name=str(case_name), namespace=crime)
        case=read_text_file(file_path)
        print("*********************************")
        print("CΗARACTERISTICS: ")
        print("+++++++++++++++++++++++++++++++++")
        counter=0
        for obj in characteristics_prefix:
            if counter == 0:
                print("Offender")
            elif counter == 1:
                print("Access Violation")
            elif counter == 2:
                print("Victim")
            elif counter == 3:
                print("Target")
            else:
                print("Harm")
            print("=============================")
            for file2 in os.listdir(obj):
                file2_path = obj + "/" + file2
                filestochecksimilarity.append(file_path)
                filestochecksimilarity.append(file2_path)
                characteristic_name=file2.replace(".txt","")
                characteristic=read_text_file(file2_path)
                documents = [open(f,encoding="UTF-8").read() for f in filestochecksimilarity]
                tfidf = TfidfVectorizer(min_df=1, stop_words="english").fit_transform(documents)
                pairwise_similarity = tfidf * tfidf.T
                if (pairwise_similarity[0,1]>0.05):
                    if counter == 0:
                        if characteristic_name=="abusive user":
                            new_case.hasOffender.append('Abusive User')
                        elif characteristic_name=="cyber bully":
                            new_case.hasOffender.append('Cyber Bully')
                        elif characteristic_name=="cyber fighter":
                            new_case.hasOffender.append('Cyber Fighter')
                        elif characteristic_name=="cyber terrorist":
                            new_case.hasOffender.append('Cyber Terrorist')
                        elif characteristic_name=="entities as offenders":
                            new_case.hasOffender.append('Entities as Offenders')
                        elif characteristic_name=="hacktivist":
                            new_case.hasOffender.append('Hacktivist')
                        elif characteristic_name=="insider":
                            new_case.hasOffender.append('Insider')
                        elif characteristic_name=="online social hacker":
                            new_case.hasOffender.append('Online Social Hacker')
                        elif characteristic_name=="script kiddie":
                            new_case.hasOffender.append('Script Kiddie')
                        elif characteristic_name=="sexually deviant user":
                            new_case.hasOffender.append('Sexually Deviant User')
                        elif characteristic_name=="cyber criminal":
                            new_case.hasOffender.append('Cyber Criminal')
                    elif counter == 1:
                        if characteristic_name=="physical tampering":
                            new_case.hasAccessViolation.append('Physical Tampering')
                        elif characteristic_name=="local computer access":
                            new_case.hasAccessViolation.append('Local Computer Access')
                        elif characteristic_name=="remote computer access":
                            new_case.hasAccessViolation.append('Remote Computer Access')
                    elif counter == 2:
                        if characteristic_name=="company":
                            new_case.hasVictim.append('Company')
                        elif characteristic_name=="country":
                            new_case.hasVictim.append('Country')
                        elif characteristic_name=="individual":
                            new_case.hasVictim.append('Individual')
                    elif counter == 3:
                        if characteristic_name == "physical abuse":
                            new_case.hasTarget.append('Physical Abuse')
                        elif characteristic_name == "emotional abuse":
                            new_case.hasTarget.append('Emotional Abuse')
                        elif characteristic_name == "ict abuse":
                            new_case.hasTarget.append('ICT Abuse')
                        elif characteristic_name == "infrastructure ict abuse":
                            new_case.hasTarget.append('infrastructure ICT Abuse')
                        elif characteristic_name == "sexual abuse":
                            new_case.hasTarget.append('Sexual Abuse')
                        elif characteristic_name == "social abuse":
                            new_case.hasTarget.append('Social Abuse')
                        elif characteristic_name == "financial abuse":
                            new_case.hasTarget.append('Financial Abuse')
                    else:
                        if characteristic_name == "inchoate harm":
                            new_case.hasHarm.append('Inchoate Harm')
                        elif characteristic_name == "individual harm":
                            new_case.hasHarm.append('Individual Harm')
                        elif characteristic_name == "systemic harm":
                            new_case.hasHarm.append('Systemic Harm')
                    print(characteristic_name)
                    print("''''''''''''''''''''''''''''''''''''''''''''''''''''''")
                filestochecksimilarity.clear()
            filestochecksimilarity.clear()
            counter+=1
        print("\n")
        print("The case: "+case_name +" has successfully been imported in the ontology with their data properties.")
        print("\n")
    print("Now you can import the new ontology 'Cyber_crime_ontology_with_new_crime_cases.owl' in Protege and start the reasoner Hermi to classify your cases to their cyber crime category (see individuals by type inferred).")
    crime.save("Cyber_crime_ontology_with_new_crime_cases.owl")