Journal of Cyber Security and Mobility最新文献_第6页

An Adaptive-Feature Centric XGBoost Ensemble Classifier Model for Improved Malware Detection and Classification 一种以自适应特征为中心的XGBoost集成分类器模型用于改进的恶意软件检测和分类

Journal of Cyber Security and Mobility Pub Date : 2022-12-31 DOI: 10.32604/jcs.2022.031889

J. Pavithra, S. Selvakumarasamy

{"title":"An Adaptive-Feature Centric XGBoost Ensemble Classifier Model for Improved Malware Detection and Classification","authors":"J. Pavithra, S. Selvakumarasamy","doi":"10.32604/jcs.2022.031889","DOIUrl":"https://doi.org/10.32604/jcs.2022.031889","url":null,"abstract":"Machine learning (ML) is often used to solve the problem of malware detection and classification and various machine learning approaches are adapted to the problem of malware classification; still acquiring poor performance by the way of feature selection, and classification. To manage the issue, an efficient Adaptive Feature Centric XG Boost Ensemble Learner Classifier “AFC-XG Boost” novel algorithm is presented in this paper. The proposed model has been designed to handle varying data sets of malware detection obtained from Kaggle data set. The model turns the process of XG Boost classifier in several stages to optimize the performance. At preprocessing stage, the data set given has been noise removed, normalized and tamper removed using Feature Base Optimizer “FBO” algorithm. The FBO would normalize the data points as well as performs noise removal according to the feature values and their base information. Similarly, the performance of standard XG Boost has been optimized by adapting Feature selection using Class Based Principle Component Analysis “CBPCA” algorithm, which performs feature selection according to the fitness of any feature for different classes. Based on the selected features, the method generates regression tree for each feature considered. Based on the generated trees, the method performs classification by computing Tree Level Ensemble Similarity “TLES” and Class Level Ensemble Similarity “CLES”. Using both method computes the value of Class Match Similarity “CMS” based on which the malware has been classified. The proposed approach achieves 97% accuracy in malware detection and classification with the less time complexity of 34 seconds for 75000 samples","PeriodicalId":37820,"journal":{"name":"Journal of Cyber Security and Mobility","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83080459","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}

引用次数: 1

Monitoring and Identification of Abnormal Network Traffic by Different Mathematical Models 基于不同数学模型的网络异常流量监测与识别

Journal of Cyber Security and Mobility Pub Date : 2022-12-03 DOI: 10.13052/jcsm2245-1439.1153

Bing Bai

引用次数: 0

A Comparative Analysis of Digital Forensic Investigation Tools on Facebook Messenger Applications Facebook Messenger应用程序上数字法医调查工具的比较分析

Journal of Cyber Security and Mobility Pub Date : 2022-12-03 DOI: 10.13052/jcsm2245-1439.1151

Sunardi, Herman, Syifa Riski Ardiningtias

{"title":"A Comparative Analysis of Digital Forensic Investigation Tools on Facebook Messenger Applications","authors":"Sunardi, Herman, Syifa Riski Ardiningtias","doi":"10.13052/jcsm2245-1439.1151","DOIUrl":"https://doi.org/10.13052/jcsm2245-1439.1151","url":null,"abstract":"Technological developments make it easier for people to communicate and share information. Facebook Messenger is an instant messenger that contains multi-platform for sending text, image, sound, and video messages. Besides being used for positive purposes, this technology can also be used to carry out harmful activities. This study conducts a forensic investigation on a crime simulation in pornographic content distribution using Facebook Messenger as a communication medium on Android smartphones. Perpetrators communicate and send pornographic content in the shape of conversations, audio, and video, then delete them to eliminate traces. Every crime can leave evidence therefore after erasing the track, it can be revealed through digital forensic investigations on the smartphone devices that are used as objects to find digital evidence. The collection of evidence in this study is used four forensic tools with the research stages the National Institute of Justice (NIJ) framework. The study result can be used as evidence by investigators on handling criminal cases with the results obtained in the shape of application versions, accounts, emails, conversation, time of occurrence, pictures, audio, and video. MOBILedit Forensic Express has an accuracy of 84.85%, Wondershare Dr. Fone 36.36%, Magnet Axiom 75.76%, and Belkasoft Evidence Center 69.70%.","PeriodicalId":37820,"journal":{"name":"Journal of Cyber Security and Mobility","volume":"56 1","pages":"655-672"},"PeriodicalIF":0.0,"publicationDate":"2022-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78452312","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}

引用次数: 2

Study of Encrypted Transmission of Private Data During Network Communication: Performance Comparison of Advanced Encryption Standard and Data Encryption Standard Algorithms 网络通信中私有数据的加密传输研究:高级加密标准与数据加密标准算法的性能比较

Journal of Cyber Security and Mobility Pub Date : 2022-12-03 DOI: 10.13052/jcsm2245-1439.1154

Dongliang Bian, Jun Pan, Yanhui Wang

{"title":"Study of Encrypted Transmission of Private Data During Network Communication: Performance Comparison of Advanced Encryption Standard and Data Encryption Standard Algorithms","authors":"Dongliang Bian, Jun Pan, Yanhui Wang","doi":"10.13052/jcsm2245-1439.1154","DOIUrl":"https://doi.org/10.13052/jcsm2245-1439.1154","url":null,"abstract":"The involvement of the Internet in the production of daily life has increased the demand for the security of private data on the Internet. This paper briefly introduced the principles of advanced encryption standard (AES) and data encryption standard (DES) algorithms and then conducted simulation experiments on the two encryption algorithms in a laboratory server. The results showed that both algorithms had excellent sensitivity to plaintext keys, and the sensitivity of the AES algorithm was higher; the encryption and decryption time of both algorithms increased as the file got larger, and the encryption and decryption time of the same algorithm was not much different; the encryption and decryption time of the AES algorithm was less than that of the DES algorithm for the same file, and the time taken to crack the AES-encrypted data by brute force was also much longer; during the transmission of encrypted data, as the data increased, the integrity of the ciphertext decryption by the third-party decreased, and the integrity of the AES algorithm-encrypted file was significantly smaller than that of the DES algorithm-encrypted file when it was decrypted.","PeriodicalId":37820,"journal":{"name":"Journal of Cyber Security and Mobility","volume":"24 1","pages":"713-726"},"PeriodicalIF":0.0,"publicationDate":"2022-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81037438","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}

引用次数: 0

A Combination of BB84 Quantum Key Distribution and An Improved Scheme of NTRU Post-Quantum Cryptosystem BB84量子密钥分配与NTRU后量子密码系统改进方案的组合

Journal of Cyber Security and Mobility Pub Date : 2022-12-03 DOI: 10.13052/jcsm2245-1439.1152

El Hassane Laaji, A. Azizi

引用次数: 0

Attack Mitigation and Security for Vehicle Platoon 车辆排的攻击缓解和安全

Journal of Cyber Security and Mobility Pub Date : 2022-11-07 DOI: 10.13052/jcsm2245-1439.1141

Daniel Kyalo Ndambuki, Hitmi Khalifa Alhitmi

{"title":"Attack Mitigation and Security for Vehicle Platoon","authors":"Daniel Kyalo Ndambuki, Hitmi Khalifa Alhitmi","doi":"10.13052/jcsm2245-1439.1141","DOIUrl":"https://doi.org/10.13052/jcsm2245-1439.1141","url":null,"abstract":"This research entails an investigation into enhanced attack detection techniques as a security feature in vehicular platooning. The paper evaluates critical challenges in the security of Vehicular Ad hoc Networks (VANETs) with a focus on vulnerabilities in vehicle platooning. We evaluate the possibilities of securing a platoon through enhanced attack detection following an inside attack while considering current communication-based approaches to vehicular platoon security that have been effective at isolating infected platoon members. This study proposes the use of color-shift keying (CSK) as a security tool for enhanced detection of an apparent platoon attack. We simulate various attack scenarios involving a vehicular platoon communicating via a VLC network and assess the degree of exposure of such networks to three types of attacks – Sybil attacks, delay attacks, and denial-of-service (DoS) attacks. We recommend the use of a light-to-frequency (LTF) converter comprising of a receiver to collect and decode transmitted symbols with regard to the frequency of transmission. Once there is a drop in the intensity of the light transmitted in the platoon, CSK is implemented to alter the intensity of the red, green, and blue (RGB) spectrum coupled with radiofrequency to ensure the security of the communication. CSK will use coded symbols to transmit the control information from the leader using a microcontroller.","PeriodicalId":37820,"journal":{"name":"Journal of Cyber Security and Mobility","volume":"86 4 1","pages":"497-530"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89359545","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}

引用次数: 1

Analysis of Video Forensics System for Detection of Gun, Mask and Anomaly Using Soft Computing Techniques 基于软计算技术的枪支、掩码和异常检测视频取证系统分析

Journal of Cyber Security and Mobility Pub Date : 2022-11-07 DOI: 10.13052/jcsm2245-1439.1143

S. K. Nanda, D. Ghai, P. Ingole

{"title":"Analysis of Video Forensics System for Detection of Gun, Mask and Anomaly Using Soft Computing Techniques","authors":"S. K. Nanda, D. Ghai, P. Ingole","doi":"10.13052/jcsm2245-1439.1143","DOIUrl":"https://doi.org/10.13052/jcsm2245-1439.1143","url":null,"abstract":"The video forensics world is a developing network of experts associated with the computerized video forensics industry. With quickly developing innovation, the video turned out to be the most significant weapon in the battle against individuals who violate the law by catching them in the act. Proof caught on video is viewed as more dependable, more exact, and more persuading than observer declaration alone. But, proof can be effortlessly tempered by utilizing programming. Video forensics examination, tells us about the accuracy of the input video. It has become a challenge for law enforcement agencies to deal with the increasing violence rate which involves the use of masks and weapons. The identification of a person becomes difficult with the use of face masks. The proposed method uses an efficient technique that is YOLO to detect guns, masks and suspicious persons from a video by extracting frames and features. It further compares the obtained frame with the available images in the dataset and generates output with bounding boxes detecting guns, masks and suspicious persons. This paper also examined the domain of video forensics and its outcomes. Experimental results show that the proposed method outperforms the existing techniques tested on different datasets. The precision for YOLO design for guns and masks is 100% and 75% respectively. The precision for customized CNN engineering for guns and face masks is 61.54% and 61.5% respectively. Execution measurements for both models have shown that the YOLO design outperformed the customized CNN with its presentation.","PeriodicalId":37820,"journal":{"name":"Journal of Cyber Security and Mobility","volume":"16 1","pages":"549-574"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90271696","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}

引用次数: 0

An Efficient Solution to User Authorization Query Problem in RBAC Systems Using Hierarchical Clustering 基于层次聚类的RBAC系统中用户授权查询问题的有效解决

Journal of Cyber Security and Mobility Pub Date : 2022-11-07 DOI: 10.13052/jcsm2245-1439.1142

K. R. Rao, Aditya Kolpe, Tribikram Pradhan, B. B. Zarpelão

引用次数: 0

Two-Dimensional Projection Based Wireless Intrusion Classification Using Lightweight EfficientNet 基于轻量级高效网络的二维投影无线入侵分类

Journal of Cyber Security and Mobility Pub Date : 2022-11-07 DOI: 10.32604/cmc.2022.026749

H. Tekleselassie

{"title":"Two-Dimensional Projection Based Wireless Intrusion Classification Using Lightweight EfficientNet","authors":"H. Tekleselassie","doi":"10.32604/cmc.2022.026749","DOIUrl":"https://doi.org/10.32604/cmc.2022.026749","url":null,"abstract":"Internet of Things (IoT) networks leverage wireless communication protocol, which adversaries can exploit. Impersonation attacks, injection attacks, and flooding are several examples of different attacks existing in Wi-Fi networks. Intrusion Detection System (IDS) became one solution to distinguish those attacks from benign traffic. Deep learning techniques have been intensively utilized to classify the attacks. However, the main issue of utilizing deep learning models is projecting the data, notably tabular data, into image-based data. This study proposes a novel projection from wireless network attacks data into grid-like data for feeding one of the Convolutional Neural Network (CNN) models, EfficientNet. We define the particular sequence of placing the attribute values in a matrix that would be captured as an image. By combining the most important subset of attributes and EfficientNet, we aim for an accurate and lightweight IDS module deployed in IoT networks. We examine the proposed model using the Wi-Fi attacks dataset, called AWID dataset. We achieve the best performance by a 99.91% F1 score and 0.11% false positive rate. In addition, our proposed model achieved comparable results with other statistical machine learning models, which shows that our proposed model successfully exploited the spatial information of tabular data to maintain detection accuracy. We also successfully maintain the false positive rate of about 0.11%. We also compared the proposed model with other machine learning models, and it is shown that our proposed model achieved comparable results with the other three models. We believe the spatial information must be considered by projecting the tabular data into grid-like data.","PeriodicalId":37820,"journal":{"name":"Journal of Cyber Security and Mobility","volume":"4 1","pages":"601-620"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81683448","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}

引用次数: 2

AI-enhanced Defense Against Ransomware Within the Organization's Architecture 在组织架构内增强人工智能防御勒索软件

Journal of Cyber Security and Mobility Pub Date : 2022-11-07 DOI: 10.13052/jcsm2245-1439.1146

B. Chaithanya, S. Brahmananda

{"title":"AI-enhanced Defense Against Ransomware Within the Organization's Architecture","authors":"B. Chaithanya, S. Brahmananda","doi":"10.13052/jcsm2245-1439.1146","DOIUrl":"https://doi.org/10.13052/jcsm2245-1439.1146","url":null,"abstract":"Ransomware is a type of revenue-generating tactic that cybercriminals utilize to improve their income. Businesses have spent billions of dollars recovering control of their resources, which may include confidential data, operational applications and models, financial transactions, and other information, as a result of malicious software. Ransomware can infiltrate a resource or device and restrict the owner from accessing or utilizing it. There are various obstacles that a business must overcome in order to avoid ransomware attacks. Traditional ransomware detection systems employ a static detection method in which a finite dataset is provided into the system and a logical check is performed to prevent ransomware attacks against the system. This was effective in the early stages of the internet, but the scenario of recent times is far more advanced, and as more and more cyber world contrivances have been analyzed, multiple gaps have been identified, to the benefit of ransomware attackers, who use these gaps to generate astronomically large sums of money. As a result, the suggested methodology aims to efficiently detect diverse patterns associated with various file formats by starting with their sources, data collecting, probabilistic identification of target devices, and deep learning classifier with intelligent detection. An organization can use the recommended approach to safeguard its data and prepare for future ransomware attacks by using it as a roadmap to lead them through their security efforts.","PeriodicalId":37820,"journal":{"name":"Journal of Cyber Security and Mobility","volume":"7 1","pages":"621-654"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85821054","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}

引用次数: 2