Journal of Computer Virology and Hacking Techniques最新文献

{"title":"Machine learning methods for speech emotion recognition on telecommunication systems","authors":"Alexey Osipov, Ekaterina Pleshakova, Yang Liu, Sergey Gataullin","doi":"10.1007/s11416-023-00500-2","DOIUrl":"https://doi.org/10.1007/s11416-023-00500-2","url":null,"abstract":"The manuscript is devoted to the study of human behavior in stressful situations using machine learning methods, which depends on the psychotype, socialization and a host of other factors. Global mobile subscribers lost approximately $53 billion in 2022 due to phone fraud and unwanted calls, with almost half (43%) of subscribers having spam blocking or caller ID apps installed. Phone scammers build their conversation focusing on the behavior of a certain category of people. Previously, a person is introduced into a state of acute stress, in which his further behavior to one degree or another can be manipulated. We were allowed to single out the target audience by research by Juniper Research. These are men under the age of 44 who have the highest risk of being deceived by scammers. This significantly narrows the scope of research and allows us to limit the behavioral features of this particular category of subscribers. In addition, this category of people uses modern gadgets, which allows researchers not to consider outdated models; has stable health indicators, which allows not to conduct additional studies of people with diseases of the heart system, because. Their percentage in this sample is minimal; and also most often undergoes a polygraph interview, for example, when applying for a job, and this allows us to get a sample sufficient for training the neural network. To teach the method, polygrams were used, marked by a polygraph examiner and a psychologist of healthy young people who underwent a scheduled polygraph test for company loyalty. For testing, the readings of the PPG sensor built into the smart bracelet were taken and analyzed within a month from young people who underwent a polygraph test. We have developed a modification of the wavelets capsular neural network—2D-CapsNet, allowing to identify the state of panic stupor by classification quality indicators: Accuracy—86.0%, Precision—84.0%, Recall = 87.5% and F-score—85.7%, according to the photoplethysmogram graph (PPG), which does not allow him to make logically sound decisions. When synchronizing a smart bracelet with a smartphone, the method allows real-time tracking of such states, which makes it possible to respond to a call from a telephone scammer during a conversation with a subscriber. The proposed method can be widely used in cyber-physical systems in order to detect illegal actions.","PeriodicalId":15545,"journal":{"name":"Journal of Computer Virology and Hacking Techniques","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135306401","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}

{"title":"Editorial special issue: Russian research in cybersecurity","authors":"Vladimir Fomichev, Alisa Koreneva","doi":"10.1007/s11416-023-00494-x","DOIUrl":"https://doi.org/10.1007/s11416-023-00494-x","url":null,"abstract":"This special issue covers works of Russian researchers on cybersecurity, fundamental and applied information security problems, tackling computer network security as well as development and analysis of hardware and software security tools. Here we provide 12 selected papers on different topics within the above-described scope. We would like to make special mention of the invited paper “Undocumented × 86 instructions to control the CPU at the microarchitecture level in modern Intel processors”, which introduces two undocumented × 86 architecture instructions which are intended to read and write Intel processors microcode data.","PeriodicalId":15545,"journal":{"name":"Journal of Computer Virology and Hacking Techniques","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135015127","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}

{"title":"DHCP DoS and starvation attacks on SDN controllers and their mitigation","authors":"Hafiz Usama Ishtiaq, Areeb Ahmed Bhutta, Adnan Noor Mian","doi":"10.1007/s11416-023-00483-0","DOIUrl":"https://doi.org/10.1007/s11416-023-00483-0","url":null,"abstract":"Software Defined Networking (SDN) technology offers possibilities to improve network administration through a separate central controller for network switching devices. However, security in SDN is a critical issue and SDN faces new challenges due to shared protocols, inherits flaws from traditional networks and control flexibility. Dynamic Host Configuration Protocol (DHCP) is a crucial protocol for SDN, but DHCP itself poses a security risk to SDN. In our study we performed security analysis for DHCP attacks on RYU, OpenDaylight and Floodlight, three popular SDN controllers. Our research demonstrates that they are vulnerable to starvation attacks and denial of service attacks by flooding DHCP discovery messages, slowing down networks and overloading controllers. In order to address these problems, we looked at state-of-the-art DHCP security approaches and evaluated their performance on these SDN controllers. We proposed and implemented a DHCP security algorithm on the RYU controller based on our analysis. Our solution utilize flexibility of SDN controller to identify discovery flood packets and verify authentic hosts to mitigate effects of DHCP attacks. Furthermore, the proposed solution transfers the authentic flows to switch for reduction in controller load. We demonstrate that without significant computational load the suggested method successfully rejects malicious DHCP packets, restores the IP address pool, and mitigates the harmful network consequences of DHCP-related attacks. The proposed solution improves the throughput by 3.6 times, transferred data by 66.8%, CPU usage by 93.9% and packet loss by 95% compared to the conventional RYU controller.","PeriodicalId":15545,"journal":{"name":"Journal of Computer Virology and Hacking Techniques","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135478385","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}

{"title":"Retraction Note: Artificial intelligence enabled fuzzy multimode decision support system for cyber threat security defense automation","authors":"Feilu Hang, Linjiang Xie, Zhenhong Zhang, Wei Guo, Hanruo Li","doi":"10.1007/s11416-023-00485-y","DOIUrl":"https://doi.org/10.1007/s11416-023-00485-y","url":null,"abstract":"","PeriodicalId":15545,"journal":{"name":"Journal of Computer Virology and Hacking Techniques","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135741686","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}

{"title":"Generative adversarial networks and image-based malware classification","authors":"Huy Nguyen, Fabio Di Troia, Genya Ishigaki, Mark Stamp","doi":"10.1007/s11416-023-00465-2","DOIUrl":"https://doi.org/10.1007/s11416-023-00465-2","url":null,"abstract":"For efficient malware removal, determination of malware threat levels, and damage estimation, malware family classification plays a critical role. In this paper, we extract features from malware executable files and represent them as images using various approaches. We then focus on generative adversarial networks (GAN) for multiclass classification and compare our GAN results to other popular machine learning techniques, including support vector machine (SVM), XGBoost, and restricted Boltzmann machines (RBM). We find that the AC-GAN discriminator is generally competitive with other machine learning techniques. We also evaluate the utility of the GAN generative model for adversarial attacks on image-based malware detection. While AC-GAN generated images are visually impressive, we find that they are easily distinguished from real malware images using any of several learning techniques. This result indicates that our GAN generated images are of surprisingly little value in adversarial attacks.","PeriodicalId":15545,"journal":{"name":"Journal of Computer Virology and Hacking Techniques","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135837732","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}