{"title":"HEMC: A Dynamic Behavior Analysis System for Malware based on Hardware Virtualization","authors":"Zhiyu Hao, Yongji Liu, Haiqiang Fei, Lei Cui, Zhenquan Ding, Huixuan Xu","doi":"10.1504/ijics.2023.10050989","DOIUrl":"https://doi.org/10.1504/ijics.2023.10050989","url":null,"abstract":"","PeriodicalId":53652,"journal":{"name":"International Journal of Information and Computer Security","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67030663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijics.2023.10055729
Synim Selimi, Blerim Rexha, Kamer Vishi
{"title":"CyberNFTs: conceptualising a decentralised and reward-driven intrusion detection system with ML","authors":"Synim Selimi, Blerim Rexha, Kamer Vishi","doi":"10.1504/ijics.2023.10055729","DOIUrl":"https://doi.org/10.1504/ijics.2023.10055729","url":null,"abstract":"","PeriodicalId":53652,"journal":{"name":"International Journal of Information and Computer Security","volume":"22 1","pages":"117-138"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67030795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijics.2023.10058109
Purushottam Kumar, S. Chandran, S. Patel
{"title":"Secure Digital Academic Certificate Verification System using Blockchain","authors":"Purushottam Kumar, S. Chandran, S. Patel","doi":"10.1504/ijics.2023.10058109","DOIUrl":"https://doi.org/10.1504/ijics.2023.10058109","url":null,"abstract":"","PeriodicalId":53652,"journal":{"name":"International Journal of Information and Computer Security","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67039580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijics.2023.132708
Yuvraj Singh Malhi, Virendra Singh Shekhawat
As a consequence of the growing number of cyberattacks on IoT devices, the need for defences like intrusion detection systems (IDSs) has significantly risen. But current IDS implementations for IoT are complex to design, difficult to incorporate, platform-specific, and limited by IoT device's resource constraints. This paper proposes a deployment-ready network IDS for IoT that overcomes the shortcomings of the existing IDS solutions and can detect 22 types of attacks. The proposed IDS provide the flexibility to work in multiple modes as per IoT device computing power, made possible via development of three machine learning-based IDS modules. The intrusion detection task has been divided at two levels: at edge devices (using two light modules based on neural network and decision tree) and at centralised controller (using a random forest and XGBoost combination). To ensure the best working tandem of developed modules, different IDS deployment strategies are also given.
{"title":"Two-level machine learning driven intrusion detection model for IoT environments","authors":"Yuvraj Singh Malhi, Virendra Singh Shekhawat","doi":"10.1504/ijics.2023.132708","DOIUrl":"https://doi.org/10.1504/ijics.2023.132708","url":null,"abstract":"As a consequence of the growing number of cyberattacks on IoT devices, the need for defences like intrusion detection systems (IDSs) has significantly risen. But current IDS implementations for IoT are complex to design, difficult to incorporate, platform-specific, and limited by IoT device's resource constraints. This paper proposes a deployment-ready network IDS for IoT that overcomes the shortcomings of the existing IDS solutions and can detect 22 types of attacks. The proposed IDS provide the flexibility to work in multiple modes as per IoT device computing power, made possible via development of three machine learning-based IDS modules. The intrusion detection task has been divided at two levels: at edge devices (using two light modules based on neural network and decision tree) and at centralised controller (using a random forest and XGBoost combination). To ensure the best working tandem of developed modules, different IDS deployment strategies are also given.","PeriodicalId":53652,"journal":{"name":"International Journal of Information and Computer Security","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136028401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijics.2023.10054850
Hajed M. Alhatlani, H. Alabdulrazzaq, M. Alenezi, Faisal A. S. AlObaid
{"title":"On the Performance of AES Algorithm Variants","authors":"Hajed M. Alhatlani, H. Alabdulrazzaq, M. Alenezi, Faisal A. S. AlObaid","doi":"10.1504/ijics.2023.10054850","DOIUrl":"https://doi.org/10.1504/ijics.2023.10054850","url":null,"abstract":"","PeriodicalId":53652,"journal":{"name":"International Journal of Information and Computer Security","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67030783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijics.2023.10056330
Sarra Cherbal, Oussama Nahnah
{"title":"SLAK: Secure Lightweight scheme for Authentication and Key-agreement in Internet of Things","authors":"Sarra Cherbal, Oussama Nahnah","doi":"10.1504/ijics.2023.10056330","DOIUrl":"https://doi.org/10.1504/ijics.2023.10056330","url":null,"abstract":"","PeriodicalId":53652,"journal":{"name":"International Journal of Information and Computer Security","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67039695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijics.2023.10057700
S. Devane, Aparna Bhonde
{"title":"Priority based security-aware virtual machine allocation policy","authors":"S. Devane, Aparna Bhonde","doi":"10.1504/ijics.2023.10057700","DOIUrl":"https://doi.org/10.1504/ijics.2023.10057700","url":null,"abstract":"","PeriodicalId":53652,"journal":{"name":"International Journal of Information and Computer Security","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67039844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijics.2023.132739
S. Masadeh, H. A. Al-Sewadi, M. A. F. Al-Husainy
{"title":"A message encryption scheme inspired by Sudoku puzzle","authors":"S. Masadeh, H. A. Al-Sewadi, M. A. F. Al-Husainy","doi":"10.1504/ijics.2023.132739","DOIUrl":"https://doi.org/10.1504/ijics.2023.132739","url":null,"abstract":"","PeriodicalId":53652,"journal":{"name":"International Journal of Information and Computer Security","volume":"21 1","pages":"399-413"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67040034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijics.2023.132779
Kheira Lakel, Fatima Bendella
In this era, information security plays a crucial and sensitive task as this data is potentially vulnerable such that different types of attacks may happen and affects the data. This paper presents a new hybrid cryptosystem for DNA cryptography based on GA and a coding table. The encryption algorithm provides multi-layer security (jamming with spiral matrix, generating coding table, coding of DNA characters, XOR-crossover operation) for DNA sequence. The decryption algorithm follows these steps: binary and segment the ciphertext, applied XOR-crossover operation, Transform each block to ASCII code, decoding of characters, remove jamming and generate the plaintext. The performance evaluation of this algorithm is based on confusion and diffusion, avalanche effect, and encryption time. The experimental results show that these algorithms yield an average time 0.835 ms/0.78 ms for 1,000 bases. The result shows outperformance in security and a weak correlation coefficient between ciphertexts generated and plaintext.
{"title":"A bio-inspired algorithm for enhancing DNA cryptography","authors":"Kheira Lakel, Fatima Bendella","doi":"10.1504/ijics.2023.132779","DOIUrl":"https://doi.org/10.1504/ijics.2023.132779","url":null,"abstract":"In this era, information security plays a crucial and sensitive task as this data is potentially vulnerable such that different types of attacks may happen and affects the data. This paper presents a new hybrid cryptosystem for DNA cryptography based on GA and a coding table. The encryption algorithm provides multi-layer security (jamming with spiral matrix, generating coding table, coding of DNA characters, XOR-crossover operation) for DNA sequence. The decryption algorithm follows these steps: binary and segment the ciphertext, applied XOR-crossover operation, Transform each block to ASCII code, decoding of characters, remove jamming and generate the plaintext. The performance evaluation of this algorithm is based on confusion and diffusion, avalanche effect, and encryption time. The experimental results show that these algorithms yield an average time 0.835 ms/0.78 ms for 1,000 bases. The result shows outperformance in security and a weak correlation coefficient between ciphertexts generated and plaintext.","PeriodicalId":53652,"journal":{"name":"International Journal of Information and Computer Security","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136028382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijics.2023.132735
Ranjeet Kumar Ranjan, Amit Singh
This paper proposes deep convolution neural network-based malware classification approach. The proposed work is a transfer learning approach, where we have developed multiple deep learning classification models. The classification models are built by adapting multiple pre-trained convolutional neural networks, namely; Xception, VGG19, InceptionResNetV2, MobileNet, InceptionV3, DenseNet, and ResNet50. In the current work, weights of pre-trained models are embellished by adding three fully connected (FC) layers. The proposed models have been evaluated on two different malware datasets, Microsoft and MalImg, consisting of malware images. The focus of this paper is to analyse the performance of fine-tuned CNN models for malware classification. The results of our experiments show that InceptionResNetV2 and Xception models have performed considerably well for the Microsoft dataset with accuracy equal to 96% and 95%, respectively. In the case of the MalImg dataset, InceptionResNetV2, InceptionV3, and Xception models have achieved excellent performance with an accuracy of up to 96%.
{"title":"A comparative study of deep transfer learning models for malware classification using image datasets","authors":"Ranjeet Kumar Ranjan, Amit Singh","doi":"10.1504/ijics.2023.132735","DOIUrl":"https://doi.org/10.1504/ijics.2023.132735","url":null,"abstract":"This paper proposes deep convolution neural network-based malware classification approach. The proposed work is a transfer learning approach, where we have developed multiple deep learning classification models. The classification models are built by adapting multiple pre-trained convolutional neural networks, namely; Xception, VGG19, InceptionResNetV2, MobileNet, InceptionV3, DenseNet, and ResNet50. In the current work, weights of pre-trained models are embellished by adding three fully connected (FC) layers. The proposed models have been evaluated on two different malware datasets, Microsoft and MalImg, consisting of malware images. The focus of this paper is to analyse the performance of fine-tuned CNN models for malware classification. The results of our experiments show that InceptionResNetV2 and Xception models have performed considerably well for the Microsoft dataset with accuracy equal to 96% and 95%, respectively. In the case of the MalImg dataset, InceptionResNetV2, InceptionV3, and Xception models have achieved excellent performance with an accuracy of up to 96%.","PeriodicalId":53652,"journal":{"name":"International Journal of Information and Computer Security","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136028393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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