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Boulemtafes, Amine, Derhab, Abdelouahid, Ali Braham, Nassim Ait, Challal, Yacine.  2021.  PReDIHERO – Privacy-Preserving Remote Deep Learning Inference based on Homomorphic Encryption and Reversible Obfuscation for Enhanced Client-side Overhead in Pervasive Health Monitoring. 2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA). :1–8.
Homomorphic Encryption is one of the most promising techniques to deal with privacy concerns, which is raised by remote deep learning paradigm, and maintain high classification accuracy. However, homomorphic encryption-based solutions are characterized by high overhead in terms of both computation and communication, which limits their adoption in pervasive health monitoring applications with constrained client-side devices. In this paper, we propose PReDIHERO, an improved privacy-preserving solution for remote deep learning inferences based on homomorphic encryption. The proposed solution applies a reversible obfuscation technique that successfully protects sensitive information, and enhances the client-side overhead compared to the conventional homomorphic encryption approach. The solution tackles three main heavyweight client-side tasks, namely, encryption and transmission of private data, refreshing encrypted data, and outsourcing computation of activation functions. The efficiency of the client-side is evaluated on a healthcare dataset and compared to a conventional homomorphic encryption approach. The evaluation results show that PReDIHERO requires increasingly less time and storage in comparison to conventional solutions when inferences are requested. At two hundreds inferences, the improvement ratio could reach more than 30 times in terms of computation overhead, and more than 8 times in terms of communication overhead. The same behavior is observed in sequential data and batch inferences, as we record an improvement ratio of more than 100 times in terms of computation overhead, and more than 20 times in terms of communication overhead.
Khan, Wazir Zada, Khurram Khan, Muhammad, Arshad, Qurat-ul-Ain, Malik, Hafiz, Almuhtadi, Jalal.  2021.  Digital Labels: Influencing Consumers Trust and Raising Cybersecurity Awareness for Adopting Autonomous Vehicles. 2021 IEEE International Conference on Consumer Electronics (ICCE). :1–4.
Autonomous vehicles (AVs) offer a wide range of promising benefits by reducing traffic accidents, environmental pollution, traffic congestion and land usage etc. However, to reap the intended benefits of AVs, it is inevitable that this technology should be trusted and accepted by the public. The consumer's substantial trust upon AVs will lead to its widespread adoption in the real-life. It is well understood that the preservation of strong security and privacy features influence a consumer's trust on a product in a positive manner. In this paper, we introduce a novel concept of digital labels for AVs to increase consumers awareness and trust regarding the security level of their vehicle. We present an architecture called Cybersecurity Box (CSBox) that leverages digital labels to display and inform consumers and passengers about cybersecurity status of the AV in use. The introduction of cybersecurity digital labels on the dashboard of AVs would attempt to increase the trust level of consumers and passengers on this promising technology.
Du, Hao, Zhang, Yu, Qin, Bo, Xu, Weiduo.  2021.  Immersive Visualization VR System of 3D Time-varying Field. 2021 3rd International Academic Exchange Conference on Science and Technology Innovation (IAECST). :322–326.
To meet the application need of dynamic visualization VR display of 3D time-varying field, this paper designed an immersive visualization VR system of 3D time-varying field based on the Unity 3D framework. To reduce visual confusion caused by 3D time-varying field flow line drawing and improve the quality and efficiency of visualization rendering drawing, deep learning was used to extract features from the mesoscale vortex of the 3D time-varying field. Moreover, the 3D flow line dynamic visualization drawing was implemented through the Unity Visual Effect Graph particle system.
Hu, Yuan, Wan, Long.  2021.  Construction of immersive architectural wisdom guiding environment based on virtual reality. 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI). :1464–1467.
Construction of immersive architectural wisdom guiding environment based on virtual reality is studied in this paper. Emerging development of the computer smart systems have provided the engineers a novel solution for the platform construction. Network virtualization is currently the most unclear and controversial concept in the industry regarding the definition of virtualization subdivisions. To improve the current study, we use the VR system to implement the platform. The wisdom guiding environment is built through the virtual data modelling and the interactive connections. The platform is implemented through the software. The test on the data analysis accuracy and the interface optimization is conducted.
Hyodo, Yasuhide, Sugai, Chihiro, Suzuki, Junya, Takahashi, Masafumi, Koizumi, Masahiko, Tomura, Asako, Mitsufuji, Yuki, Komoriya, Yota.  2021.  Psychophysiological Effect of Immersive Spatial Audio Experience Enhanced Using Sound Field Synthesis. 2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII). :1–8.
Recent advancements of spatial audio technologies to enhance human’s emotional and immersive experiences are gathering attention. Many studies are clarifying the neural mechanisms of acoustic spatial perception; however, they are limited to the evaluation of mechanisms using basic sound stimuli. Therefore, it remains challenging to evaluate the experience of actual music contents and to verify the effects of higher-order neurophysiological responses including a sense of immersive and realistic experience. To investigate the effects of spatial audio experience, we verified the psychophysiological responses of immersive spatial audio experience using sound field synthesis (SFS) technology. Specifically, we evaluated alpha power as the central nervous system activity, heart rate/heart rate variability and skin conductance as the autonomic nervous system activity during an acoustic experience of an actual music content by comparing stereo and SFS conditions. As a result, statistically significant differences (p \textbackslashtextless 0.05) were detected in the changes in alpha wave power, high frequency wave power of heart rate variability (HF), and skin conductance level (SCL) among the conditions. The results of the SFS condition showed enhanced the changes in alpha power in the frontal and parietal regions, suggesting enhancement of emotional experience. The results of the SFS condition also suggested that close objects are grouped and perceived on the basis of the spatial proximity of sounds in the presence of multiple sound sources. It is demonstrating that the potential use of SFS technology can enhance emotional and immersive experiences by spatial acoustic expression.
Wen, Kaiyuan, Gang, Su, Li, Zhifeng, Zou, Zhexiang.  2021.  Design of Remote Control Intelligent Vehicle System with Three-dimensional Immersion. 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE). :287–290.
The project uses 3D immersive technology to innovatively apply virtual reality technology to the monitoring field, and proposes the concept and technical route of remote 3D immersive intelligent control. A design scheme of a three-dimensional immersive remote somatosensory intelligent controller is proposed, which is applied to the remote three-dimensional immersive control of a crawler mobile robot, and the test and analysis of the principle prototype are completed.
Iglesias, Maria Insa, Jenkins, Mark, Morison, Gordon.  2021.  An Enhanced Photorealistic Immersive System using Augmented Situated Visualization within Virtual Reality. 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). :514–515.
This work presents a system which allows image data and extracted features from a real-world location to be captured and modelled in a Virtual Reality (VR) environment combined with Augmented Situated Visualizations (ASV) overlaid and registered in a virtual environment. Combining these technologies with techniques from Data Science and Artificial Intelligence (AI)(such as image analysis and 3D reconstruction) allows the creation of a setting where remote locations can be modelled and interacted with from anywhere in the world. This Enhanced Photorealistic Immersive (EPI) system is highly adaptable to a wide range of use cases and users as it can be utilized to model and interact with any environment which can be captured as image data (such as training for operation in hazardous environments, accessibility solutions for exploration of historical/tourism locations and collaborative learning environments). A use case example focused on a structural examination of railway tunnels along with a pilot study is presented, which can demonstrate the usefulness of the EPI system.
Zhang, Zuyao, Gao, Jing.  2021.  Design of Immersive Interactive Experience of Intangible Cultural Heritage based on Flow Theory. 2021 13th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). :146–149.
At present, the limitation of intangible cultural experience lies in the lack of long-term immersive cultural experience for users. In order to solve this problem, this study divides the process from the perspective of Freudian psychology and combines the theoretical research on intangible cultural heritage and flow experience to get the preliminary research direction. Then, based on the existing interactive experience cases of intangible cultural heritage, a set of method model of immersive interactive experience of intangible cultural heritage based on flow theory is summarized through user interviews in this research. Finally, through data verification, the model is proved to be correct. In addition, this study offers some important insights into differences between primary users and experienced users, and proposed specific guiding suggestions for immersive interactive experience design of intangible cultural heritage based on flow theory in the future.
Chang, Xinyu, Wu, Bian.  2021.  Effects of Immersive Spherical Video-based Virtual Reality on Cognition and Affect Outcomes of Learning: A Meta-analysis. 2021 International Conference on Advanced Learning Technologies (ICALT). :389–391.
With the advancement of portable head-mounted displays, interest in educational application of immersive spherical video-based virtual reality (SVVR) has been emerging. However, it remains unclear regarding the effects of immersive SVVR on cognitive and affective outcomes. In this study, we retrieved 58 learning outcomes from 16 studies. A meta-analysis was performed using the random effects model to calculate the effect size. Several important moderators were also examined such as control group treatment, learning outcome type, interaction functionality, content instruction, learning domain, and learner's stage. The results show that immersive SVVR is more effective than other instructional conditions with a medium effect size. The key findings of the moderator analysis are that immersive SVVR has a greater impact on affective outcomes, as well as under the conditions that learning system provides interaction functionality or integrates with content instruction before virtual exploratory learning.
Abdul Manaf, Marlina Bt, Bt Sulaiman, Suziah, Bt Awang Rambli, Dayang Rohaya.  2021.  Immersive and Non-Immersive VR Display using Nature Theme as Therapy in Reducing Work Stress. 2021 International Conference on Computer Information Sciences (ICCOINS). :276–281.
Stress-related disorders are increasing because of work load, forces in teamwork, surroundings pressures and health related conditions. Thus, to avoid people living under heavy stress and develop more severe stress-related disorders, different internet and applications of stress management interventions are offered. Mobile applications with self-assessed health, burnout-scores and well-being are commonly used as outcome measures. Few studies have used sickleave to compare effects of stress interventions. A new approach is to use nature and garden in a multimodal stress management context. This study aimed to explore the effects of immersive and non-immersive games application by using nature theme virtual stress therapy in reducing stress level. Two weeks’ of experiments had involved 18 participants. Nine (9) of them were invited to join the first experiment which focused on immersive virtual reality (VR) experience. Their Blood Volume Pulse with Heart Rate (BVP+HR) and Skin Conductance (SC) were recorded using BioGraph Infiniti Biofeedback System that comes with three (3) sensors attached to the fingers. The second experiment were joined by another nine (9) participants. This experiment was testing on non-immersive desktop control experience. The same protocol measurements were taken which are BVP+HR and SC. Participants were given the experience to feel and get carried into the virtual nature as a therapy so that they will reduce stress. The result of this study points to whether immersive or non-immersive VR display using nature theme virtual therapy would reduce individuals stress level. After conducted series of experiments, results showed that both immersive and non-immersive VR display reduced stress level. However, participants were satisfied of using the immersive version as it provided a 360 degree of viewing, immersed experiences and feeling engaged. Thus, this showed and proved that applications developed with nature theme affect successfully reduce stress level no matter it is put in immersive or non-immersive display.
Guo, Siyao, Fu, Yi.  2021.  Construction of immersive scene roaming system of exhibition hall based on virtual reality technology. 2021 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS). :1029–1033.
On the basis of analyzing the development and application of virtual reality (VR) technology at home and abroad, and combining with the specific situation of the exhibition hall, this paper establishes an immersive scene roaming system of the exhibition hall. The system is completed by virtual scene modeling technology and virtual roaming interactive technology. The former uses modeling software to establish the basic model in the virtual scene, while the latter uses VR software to enable users to control their own roles to run smoothly in the roaming scene. In interactive roaming, this paper optimizes the A* pathfinding algorithm, uses binary heap to process data, and on this basis, further optimizes the pathfinding algorithm, so that when the pathfinding target is an obstacle, the pathfinder can reach the nearest place to the obstacle. Texture mapping technology, LOD technology and other related technologies are adopted in the modeling, thus finally realizing the immersive scene roaming system of the exhibition hall.
Suzuki, Toshiki, Ochiai, Takuro, Hoshino, Junichi.  2021.  Scenario-Based Customer Service VR Training System Using Second Language. 2021 Nicograph International (NicoInt). :94–97.
Since a training system using VR can reproduce an actual training environment, training systems have been studied in commercial fields such as medical care and construction. This immersive experience in a virtual space can have a great effect on learning a second language. In this paper, we propose an immersive learning system that learns phrases used in the customer service industry in the customer service experience. We asked the subjects to experience the system, measured the effects of learning, and evaluated the system. Evaluating the learning effect of phrases used in customer service English on 8 students, all student achieved good learning results. Besides, to evaluate the usability of the system, the VR system was evaluated by performing SSQ to measure VR sickness shows this system doesn't cause virtual sickness, SUS to measure usability shows this system evaluation is higher than average system, and IPQ to measure presence in an immersive space shows this system gives average virtual reality experience.
Beck, Dennis, Morgado, Leonel, Lee, Mark, Gütl, Christian, Dengel, Andreas, Wang, Minjuan, Warren, Scott, Richter, Jonathon.  2021.  Towards an Immersive Learning Knowledge Tree - a Conceptual Framework for Mapping Knowledge and Tools in the Field. 2021 7th International Conference of the Immersive Learning Research Network (iLRN). :1–8.
The interdisciplinary field of immersive learning research is scattered. Combining efforts for better exploration of this field from the different disciplines requires researchers to communicate and coordinate effectively. We call upon the community of immersive learning researchers for planting the Knowledge Tree of Immersive Learning Research, a proposal for a systematization effort for this field, combining both scholarly and practical knowledge, cultivating a robust and ever-growing knowledge base and methodological toolbox for immersive learning. This endeavor aims at promoting evidence-informed practice and guiding future research in the field. This paper contributes with the rationale for three objectives: 1) Developing common scientific terminology amidst the community of researchers; 2) Cultivating a common understanding of methodology, and 3) Advancing common use of theoretical approaches, frameworks, and models.
Kjamilji, Artrim, Levi, Albert, Savas, Erkay, Güney, Osman Berke.  2021.  Secure Matrix Operations for Machine Learning Classifications Over Encrypted Data in Post Quantum Industrial IoT. 2021 International Symposium on Networks, Computers and Communications (ISNCC). :1–8.
We tackle the problem where a server owns a trained Machine Learning (ML) model and a client/user has an unclassified query that he wishes to classify in secure and private fashion using the server’s model. During the process the server learns nothing, while the user learns only his final classification and nothing else. Since several ML classification algorithms, such as deep neural networks, support vector machines-SVM (and hyperplane decisions in general), Logistic Regression, Naïve Bayes, etc., can be expressed in terms of matrix operations, initially we propose novel secure matrix operations as our building blocks. On top of them we build our secure and private ML classification algorithms under strict security and privacy requirements. As our underlying cryptographic primitives are shown to be resilient to quantum computer attacks, our algorithms are also suitable for the post-quantum world. Our theoretical analysis and extensive experimental evaluations show that our secure matrix operations, hence our secure ML algorithms build on top of them as well, outperform the state of the art schemes in terms of computation and communication costs. This makes our algorithms suitable for devices with limited resources that are often found in Industrial IoT (Internet of Things)
Zahra, Ayima, Asif, Muhammad, Nagra, Arfan Ali, Azeem, Muhammad, Gilani, Syed A..  2021.  Vulnerabilities and Security Threats for IoT in Transportation and Fleet Management. 2021 4th International Conference on Computing Information Sciences (ICCIS). :1–5.
The fields of transportation and fleet management have been evolving at a rapid pace and most of these changes are due to numerous incremental developments in the area. However, a comprehensive study that critically compares and contrasts all the existing techniques and methodologies in the area is still missing. This paper presents a comparative analysis of the vulnerabilities and security threats for IoT and their mitigation strategies in the context of transportation and fleet management. Moreover, we attempt to classify the existing strategies based on their underlying principles.
Hasan, Raiful, Hasan, Ragib.  2021.  Towards a Threat Model and Security Analysis of Video Conferencing Systems. 2021 IEEE 18th Annual Consumer Communications Networking Conference (CCNC). :1–4.
Video Conferencing has emerged as a new paradigm of communication in the age of COVID-19 pandemic. This technology is allowing us to have real-time interaction during the social distancing era. Even before the current crisis, it was increasingly commonplace for organizations to adopt a video conferencing tool. As people adopt video conferencing tools and access data with potentially less secure equipment and connections, meetings are becoming a target to cyber attackers. Enforcing appropriate security and privacy settings prevents attackers from exploiting the system. To design the video conferencing system's security and privacy model, an exhaustive threat model must be adopted. Threat modeling is a process of optimizing security by identifying objectives, vulnerabilities, and defining the plan to mitigate or prevent potential threats to the system. In this paper, we use the widely accepted STRIDE threat modeling technique to identify all possible risks to video conferencing tools and suggest mitigation strategies for creating a safe and secure system.
Sion, Laurens, Van Landuyt, Dimitri, Yskout, Koen, Verreydt, Stef, Joosen, Wouter.  2021.  Automated Threat Analysis and Management in a Continuous Integration Pipeline. 2021 IEEE Secure Development Conference (SecDev). :30–37.
Security and privacy threat modeling is commonly applied to systematically identify and address design-level security and privacy concerns in the early stages of architecture and design. Identifying and resolving these threats should remain a continuous concern during the development lifecycle. Especially with contemporary agile development practices, a single-shot upfront analysis becomes quickly outdated. Despite it being explicitly recommended by experts, existing threat modeling approaches focus largely on early development phases and provide limited support during later implementation phases.In this paper, we present an integrated threat analysis toolchain to support automated, continuous threat elicitation, assessment, and mitigation as part of a continuous integration pipeline in the GitLab DevOps platform. This type of automation allows for continuous attention to security and privacy threats during development at the level of individual commits, supports monitoring and managing the progress in addressing security and privacy threats over time, and enables more advanced and fine-grained analyses such as assessing the impact of proposed changes in different code branches or merge/pull requests by analyzing the changes to the threat model.
Chen, Xiarun, Li, Qien, Yang, Zhou, Liu, Yongzhi, Shi, Shaosen, Xie, Chenglin, Wen, Weiping.  2021.  VulChecker: Achieving More Effective Taint Analysis by Identifying Sanitizers Automatically. 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :774–782.
The automatic detection of vulnerabilities in Web applications using taint analysis is a hot topic. However, existing taint analysis methods for sanitizers identification are too simple to find available taint transmission chains effectively. These methods generally use pre-constructed dictionaries or simple keywords to identify, which usually suffer from large false positives and false negatives. No doubt, it will have a greater impact on the final result of the taint analysis. To solve that, we summarise and classify the commonly used sanitizers in Web applications and propose an identification method based on semantic analysis. Our method can accurately and completely identify the sanitizers in the target Web applications through static analysis. Specifically, we analyse the natural semantics and program semantics of existing sanitizers, use semantic analysis to find more in Web applications. Besides, we implemented the method prototype in PHP and achieved a vulnerability detection tool called VulChecker. Then, we experimented with some popular open-source CMS frameworks. The results show that Vulchecker can accurately identify more sanitizers. In terms of vulnerability detection, VulChecker also has a lower false positive rate and a higher detection rate than existing methods. Finally, we used VulChecker to analyse the latest PHP applications. We identified several new suspicious taint data propagation chains. Before the paper was completed, we have identified four unreported vulnerabilities. In general, these results show that our approach is highly effective in improving vulnerability detection based on taint analysis.
Zhang, Xueling, Wang, Xiaoyin, Slavin, Rocky, Niu, Jianwei.  2021.  ConDySTA: Context-Aware Dynamic Supplement to Static Taint Analysis. 2021 IEEE Symposium on Security and Privacy (SP). :796–812.
Static taint analyses are widely-applied techniques to detect taint flows in software systems. Although they are theoretically conservative and de-signed to detect all possible taint flows, static taint analyses almost always exhibit false negatives due to a variety of implementation limitations. Dynamic programming language features, inaccessible code, and the usage of multiple programming languages in a software project are some of the major causes. To alleviate this problem, we developed a novel approach, DySTA, which uses dynamic taint analysis results as additional sources for static taint analysis. However, naïvely adding sources causes static analysis to lose context sensitivity and thus produce false positives. Thus, we developed a hybrid context matching algorithm and corresponding tool, ConDySTA, to preserve context sensitivity in DySTA. We applied REPRODROID [1], a comprehensive benchmarking framework for Android analysis tools, to evaluate ConDySTA. The results show that across 28 apps (1) ConDySTA was able to detect 12 out of 28 taint flows which were not detected by any of the six state-of-the-art static taint analyses considered in ReproDroid, and (2) ConDySTA reported no false positives, whereas nine were reported by DySTA alone. We further applied ConDySTA and FlowDroid to 100 top Android apps from Google Play, and ConDySTA was able to detect 39 additional taint flows (besides 281 taint flows found by FlowDroid) while preserving the context sensitivity of FlowDroid.
Gylling, Andreas, Ekstedt, Mathias, Afzal, Zeeshan, Eliasson, Per.  2021.  Mapping Cyber Threat Intelligence to Probabilistic Attack Graphs. 2021 IEEE International Conference on Cyber Security and Resilience (CSR). :304–311.
As cyber threats continue to grow and expertise resources are limited, organisations need to find ways to evaluate their resilience efficiently and take proactive measures against an attack from a specific adversary before it occurs. Threat modelling is an excellent method of assessing the resilience of ICT systems, forming Attack (Defense) Graphs (ADGs) that illustrate an adversary’s attack vectors. Cyber Threat Intelligence (CTI) is information that helps understand the current cyber threats, but has little integration with ADGs. This paper contributes with an approach that resolves this problem by using CTI feeds of known threat actors to enrich ADGs under multiple reuse. This enables security analysts to take proactive measures and strengthen their ICT systems against current methods used by any threat actor that is believed to pose a threat to them.
Baniya, Babu Kaji.  2021.  Intrusion Representation and Classification using Learning Algorithm. 2021 23rd International Conference on Advanced Communication Technology (ICACT). :279–284.
At present, machine learning (ML) algorithms are essential components in designing the sophisticated intrusion detection system (IDS). They are building-blocks to enhance cyber threat detection and help in classification at host-level and network-level in a short period. The increasing global connectivity and advancements of network technologies have added unprecedented challenges and opportunities to network security. Malicious attacks impose a huge security threat and warrant scalable solutions to thwart large-scale attacks. These activities encourage researchers to address these imminent threats by analyzing a large volume of the dataset to tackle all possible ranges of attack. In this proposed method, we calculated the fitness value of each feature from the population by using a genetic algorithm (GA) and selected them according to the fitness value. The fitness values are presented in hierarchical order to show the effectiveness of problem decomposition. We implemented Support Vector Machine (SVM) to verify the consistency of the system outcome. The well-known NSL-knowledge discovery in databases (KDD) was used to measure the performance of the system. From the experiments, we achieved a notable classification accuracies using a SVM of the current state of the art intrusion detection.
Ndichu, Samuel, Ban, Tao, Takahashi, Takeshi, Inoue, Daisuke.  2021.  A Machine Learning Approach to Detection of Critical Alerts from Imbalanced Multi-Appliance Threat Alert Logs. 2021 IEEE International Conference on Big Data (Big Data). :2119–2127.
The extraordinary number of alerts generated by network intrusion detection systems (NIDS) can desensitize security analysts tasked with incident response. Security information and event management systems (SIEMs) perform some rudimentary automation but cannot replicate the decision-making process of a skilled analyst. Machine learning and artificial intelligence (AI) can detect patterns in data with appropriate training. In practice, the majority of the alert data comprises false alerts, and true alerts form only a small proportion. Consequently, a naive engine that classifies all security alerts into the majority class can yield a superficial high accuracy close to 100%. Without any correction for the class imbalance, the false alerts will dominate algorithmic predictions resulting in poor generalization performance. We propose a machine-learning approach to address the class imbalance problem in multi-appliance security alert data and automate the security alert analysis process performed in security operations centers (SOCs). We first used the neighborhood cleaning rule (NCR) to identify and remove ambiguous, noisy, and redundant false alerts. Then, we applied the support vector machine synthetic minority oversampling technique (SVMSMOTE) to generate synthetic training true alerts. Finally, we fit and evaluated the decision tree and random forest classifiers. In the experiments, using alert data from eight security appliances, we demonstrated that the proposed method can significantly reduce the need for manual auditing, decreasing the number of uninspected alerts and achieving a performance of 99.524% in recall.
Aljubory, Nawaf, Khammas, Ban Mohammed.  2021.  Hybrid Evolutionary Approach in Feature Vector for Ransomware Detection. 2021 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE). :1–6.
Ransomware is one of the most serious threats which constitute a significant challenge in the cybersecurity field. The cybercriminals use this attack to encrypts the victim's files or infect the victim's devices to demand ransom in exchange to restore access to these files and devices. The escalating threat of Ransomware to thousands of individuals and companies requires an urgent need for creating a system capable of proactively detecting and preventing ransomware. In this research, a new approach is proposed to detect and classify ransomware based on three machine learning algorithms (Random Forest, Support Vector Machines , and Näive Bayes). The features set was extracted directly from raw byte using static analysis technique of samples to improve the detection speed. To offer the best detection accuracy, CF-NCF (Class Frequency - Non-Class Frequency) has been utilized for generate features vectors. The proposed approach can differentiate between ransomware and goodware files with a detection accuracy of up to 98.33 percent.
J, Goutham Kumar, S, Gowri, Rajendran, Surendran, Vimali, J.S., Jabez, J., Srininvasulu, Senduru.  2021.  Identification of Cyber Threats and Parsing of Data. 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI). :556–564.
One of the significant difficulties in network safety is the arrangement of a mechanized and viable digital danger's location strategy. This paper presents an AI procedure for digital dangers recognition, in light of fake neural organizations. The proposed procedure changes large number of gathered security occasions over to singular occasion profiles and utilize a profound learning-based discovery strategy for upgraded digital danger identification. This research work develops an AI-SIEM framework dependent on a blend of occasion profiling for information preprocessing and distinctive counterfeit neural organization techniques by including FCNN, CNN, and LSTM. The framework centers around separating between obvious positive and bogus positive cautions, consequently causing security examiners to quickly react to digital dangers. All trials in this investigation are performed by creators utilizing two benchmark datasets (NSLKDD and CICIDS2017) and two datasets gathered in reality. To assess the presentation correlation with existing techniques, tests are carried out by utilizing the five ordinary AI strategies (SVM, k-NN, RF, NB, and DT). Therefore, the exploratory aftereffects of this examination guarantee that our proposed techniques are fit for being utilized as learning-based models for network interruption discovery and show that despite the fact that it is utilized in reality, the exhibition beats the traditional AI strategies.
Perrone, Paola, Flammini, Francesco, Setola, Roberto.  2021.  Machine Learning for Threat Recognition in Critical Cyber-Physical Systems. 2021 IEEE International Conference on Cyber Security and Resilience (CSR). :298–303.
Cybersecurity has become an emerging challenge for business information management and critical infrastructure protection in recent years. Artificial Intelligence (AI) has been widely used in different fields, but it is still relatively new in the area of Cyber-Physical Systems (CPS) security. In this paper, we provide an approach based on Machine Learning (ML) to intelligent threat recognition to enable run-time risk assessment for superior situation awareness in CPS security monitoring. With the aim of classifying malicious activity, several machine learning methods, such as k-nearest neighbours (kNN), Naïve Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF), have been applied and compared using two different publicly available real-world testbeds. The results show that RF allowed for the best classification performance. When used in reference industrial applications, the approach allows security control room operators to get notified of threats only when classification confidence will be above a threshold, hence reducing the stress of security managers and effectively supporting their decisions.