Search results for: gesture detection

Authors: Lakshmidevi Sreeramareddy, Komalpreet Kaur, Nane Pothier

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Graphical-based passwords have existed for decades. Their major advantage is that they are easier to remember than an alphanumeric password. However, their disadvantage (especially recognition-based passwords) is the smaller password space, making them more vulnerable to brute force attacks. Graphical passwords are also highly susceptible to the shoulder-surfing effect. The gesture-based password method that we developed is a grid-free, template-free method. In this study, we evaluated the gesture-based passwords for usability and vulnerability. The results of the study are significant. We developed a gesture-based password application for data collection. Two modes of data collection were used: Creation mode and Replication mode. In creation mode (Session 1), users were asked to create six different passwords and reenter each password five times. In replication mode, users saw a password image created by some other user for a fixed duration of time. Three different duration timers, such as 5 seconds (Session 2), 10 seconds (Session 3), and 15 seconds (Session 4), were used to mimic the shoulder-surfing attack. After the timer expired, the password image was removed, and users were asked to replicate the password. There were 74, 57, 50, and 44 users participated in Session 1, Session 2, Session 3, and Session 4 respectfully. In this study, the machine learning algorithms have been applied to determine whether the person is a genuine user or an imposter based on the password entered. Five different machine learning algorithms were deployed to compare the performance in user authentication: namely, Decision Trees, Linear Discriminant Analysis, Naive Bayes Classifier, Support Vector Machines (SVMs) with Gaussian Radial Basis Kernel function, and K-Nearest Neighbor. Gesture-based password features vary from one entry to the next. It is difficult to distinguish between a creator and an intruder for authentication. For each password entered by the user, four features were extracted: password score, password length, password speed, and password size. All four features were normalized before being fed to a classifier. Three different classifiers were trained using data from all four sessions. Classifiers A, B, and C were trained and tested using data from the password creation session and the password replication with a timer of 5 seconds, 10 seconds, and 15 seconds, respectively. The classification accuracies for Classifier A using five ML algorithms are 72.5%, 71.3%, 71.9%, 74.4%, and 72.9%, respectively. The classification accuracies for Classifier B using five ML algorithms are 69.7%, 67.9%, 70.2%, 73.8%, and 71.2%, respectively. The classification accuracies for Classifier C using five ML algorithms are 68.1%, 64.9%, 68.4%, 71.5%, and 69.8%, respectively. SVMs with Gaussian Radial Basis Kernel outperform other ML algorithms for gesture-based password authentication. Results confirm that the shorter the duration of the shoulder-surfing attack, the higher the authentication accuracy. In conclusion, behavioral features extracted from the gesture-based passwords lead to less vulnerable user authentication.

Keywords: authentication, gesture-based passwords, machine learning algorithms, shoulder-surfing attacks, usability

Procedia PDF Downloads 79

Authors: Rasha Ali Dheyab

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In today's increasingly interconnected and technologically-driven world, communication has evolved beyond traditional verbal exchanges. This paper delves into the fascinating realm of multimodal communication, a dynamic field at the intersection of linguistics, gesture studies, and technology. The study of how humans convey meaning through a combination of spoken language, gestures, facial expressions, and digital platforms has gained prominence as our modes of interaction continue to diversify. This exploration begins by examining the foundational theories in linguistics and gesture studies, tracing their historical development and mutual influences. It further investigates the role of nonverbal cues, such as gestures and facial expressions, in augmenting and sometimes even altering the meanings conveyed by spoken language. Additionally, the paper delves into the modern technological landscape, where emojis, GIFs, and other digital symbols have emerged as new linguistic tools, reshaping the ways in which we communicate and express emotions. The interaction between traditional and digital modes of communication is a central focus of this study. The paper investigates how technology has not only introduced new modes of expression but has also influenced the adaptation of existing linguistic and gestural patterns in online discourse. The emergence of virtual reality and augmented reality environments introduces yet another layer of complexity to multimodal communication, offering new avenues for studying how humans navigate and negotiate meaning in immersive digital spaces. Through a combination of literature review, case studies, and theoretical analysis, this paper seeks to shed light on the intricate interplay between language, gesture, and technology in the realm of multimodal communication. By understanding how these diverse modes of expression intersect and interact, we gain valuable insights into the ever-evolving nature of human communication and its implications for fields ranging from linguistics and psychology to human-computer interaction and digital anthropology.

Keywords: multimodal communication, linguistics ., gesture studies., emojis., verbal communication., digital

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Authors: Menelaos N. Katsantonis, Athanasios Manikas, Alexandros Chatzis, Stavros Doropoulos, Anastasios Avramis, Ioannis Mavridis

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Museums enhance their potential by adopting new technologies and techniques to appeal to more visitors and engage them in creative and joyful activities. In this study, the Haptic Cycle is presented, a cycle of museum activities proposed for the development of museum learning approaches with optimized effectiveness and engagement. Haptic Cycle envisages the improvement of the museum’s services by offering a wide range of activities. Haptic Cycle activities make the museum’s exhibitions more approachable by bringing them closer to the visitors. Visitors can interact with the museum’s artifacts and explore them haptically and sonically. Haptic Cycle proposes constructivist learning activities in which visitors actively construct their knowledge by exploring the artifacts, experimenting with them and realizing their importance. Based on the Haptic Cycle, we developed the HapticSOUND system, an innovative virtual reality system that includes an advanced user interface that employs gesture-based technology. HapticSOUND’s interface utilizes the leap motion gesture recognition controller and a 3D-printed traditional Cretan lute, utilized by visitors to perform various activities such as exploring the lute and playing notes and songs.

Keywords: haptic cycle, HapticSOUND, museum learning, gesture-based, leap motion

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Authors: Jae-Jeong Kim, Ki-Ro Kim, Hyoung-Kyu Song

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In this paper, we propose an efficient signal detector that switches M parameter of QRD-M detection scheme is proposed for MIMO-OFDM system. The proposed detection scheme calculates the threshold by 1-norm condition number and then switches M parameter of QRD-M detection scheme according to channel information. If channel condition is bad, the parameter M is set to high value to increase the accuracy of detection. If channel condition is good, the parameter M is set to low value to reduce complexity of detection. Therefore, the proposed detection scheme has better trade off between BER performance and complexity than the conventional detection scheme. The simulation result shows that the complexity of proposed detection scheme is lower than QRD-M detection scheme with similar BER performance.

Keywords: MIMO-OFDM, QRD-M, channel condition, BER

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Authors: Jae-Hyun Ro, Yong-Jun Kim, Chang-Bin Ha, Hyoung-Kyu Song

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In multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems, many detection schemes have been developed to improve the error performance and to reduce the complexity. Maximum likelihood (ML) detection has optimal error performance but it has very high complexity. Thus, this paper proposes reduced complexity of ML detection combined with decision feedback equalizer (DFE). The error performance of the proposed detection scheme is higher than the conventional DFE. But the complexity of the proposed scheme is lower than the conventional ML detection.

Keywords: detection, DFE, MIMO-OFDM, ML

Procedia PDF Downloads 579

Authors: Daleesha M. Viswanathan, Sumam Mary Idicula

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Dynamic hand gestures are an intrinsic component in sign language communication. Extracting spatial temporal features of the hand gesture trajectory plays an important role in a dynamic gesture recognition system. Finding a discrete feature descriptor for the motion trajectory based on the orientation feature is the main concern of this paper. Kalman filter algorithm and Hidden Markov Models (HMM) models are incorporated with this recognition system for hand trajectory tracking and for spatial temporal classification, respectively.

Keywords: orientation features, discrete feature vector, HMM., Indian sign language

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Authors: Marie Alaghband, Niloofar Yousefi, Ivan Garibay

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Facial expressions are important parts of both gesture and sign language recognition systems. Despite the recent advances in both fields, annotated facial expression datasets in the context of sign language are still scarce resources. In this manuscript, we introduce an annotated sequenced facial expression dataset in the context of sign language, comprising over 3000 facial images extracted from the daily news and weather forecast of the public tv-station PHOENIX. Unlike the majority of currently existing facial expression datasets, FePh provides sequenced semi-blurry facial images with different head poses, orientations, and movements. In addition, in the majority of images, identities are mouthing the words, which makes the data more challenging. To annotate this dataset we consider primary, secondary, and tertiary dyads of seven basic emotions of "sad", "surprise", "fear", "angry", "neutral", "disgust", and "happy". We also considered the "None" class if the image’s facial expression could not be described by any of the aforementioned emotions. Although we provide FePh as a facial expression dataset of signers in sign language, it has a wider application in gesture recognition and Human Computer Interaction (HCI) systems.

Keywords: annotated facial expression dataset, gesture recognition, sequenced facial expression dataset, sign language recognition

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Authors: Jozsef Katona, Attila Kovari, Tibor Ujbanyi, Gergely Sziladi

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By the development of IT systems, human-computer interaction is also developing even faster and newer communication methods become available in human-machine interaction. In this article, the application of a hand gesture controlled human-computer interface is being introduced through the example of a mobile robot. The control of the mobile robot is implemented in a realistic virtual environment that is advantageous regarding the aspect of different tests, parallel examinations, so the purchase of expensive equipment is unnecessary. The usability of the implemented hand gesture control has been evaluated by test subjects. According to the opinion of the testing subjects, the system can be well used, and its application would be recommended on other application fields too.

Keywords: human-machine interface (HCI), mobile robot, hand control, virtual environment

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Authors: Wei Li, Tuo Yang

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In order to satisfy the real-time and accurate requirements of cigarette smoke detection in complex scenes, a cigarette smoke detection technology based on the combination of deep learning and color features was proposed. Firstly, based on the color features of cigarette smoke, the suspicious cigarette smoke area in the image is extracted. Secondly, combined with the efficiency of cigarette smoke detection and the problem of network overfitting, a network model for cigarette smoke detection was designed according to YOLOV3 algorithm to reduce the false detection rate. The experimental results show that the method is feasible and effective, and the accuracy of cigarette smoke detection is up to 99.13%, which satisfies the requirements of real-time cigarette smoke detection in complex scenes.

Keywords: deep learning, computer vision, cigarette smoke detection, YOLOV3, color feature extraction

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Authors: Thomas Ousterhout

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In an effort to test the ability of a commercial grade EEG headset to effectively measure the N400 ERP, a replication study was conducted to see if similar results could be produced as that which used a medical grade EEG. Pictures of meaningful and meaningless hand postures were borrowed from the original author and subjects were required to perform a semantic discrimination task. The N400 was detected indicating semantic processing of the meaningfulness of the hand postures. The results corroborate those of the original author and support the use of some commercial grade EEG headsets for non-critical research applications.

Keywords: EEG, ERP, N400, semantics, congruency, gestures, emotiv

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Authors: Behzad Moeini Sam, Sara Mohammadi Avendi

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The beliefs of the ancient peoples about gods and kings and how to perform rituals played an active part in the ancient civilizations. One of them in the ancient Near Eastern civilizations, which were accomplished, was paying homage to the gods and kings. The reverence posture during the Achaemenid period consisted of raising one right hand with the palm and the extended fingers facing the mouth. It is worth paying attention to the fact that the ancient empires such as Akkadian, Assyrian, Babylonian, and Persian should be regarded as successive versions of the same multinational power structure, each resulting from an internal power struggle within this structure. This article tries to show the reverence gesture with those of the ancient Near East. The working method is to study Darius one in Persepolis and pay homage to him and his similarities to those of the ancient Near East. Thus, it is logical to assume that the Reverence gesture follows the Sumerian and Akkadian ones.

Keywords: Darius, Persepolis, Achaemenid, Proskynesis

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Authors: H. Benmoussa, A. A. El Kalam, A. Ait Ouahman

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The design and implementation of intrusion detection systems (IDS) remain an important area of research in the security of information systems. Despite the importance and reputation of the current intrusion detection systems, their efficiency and effectiveness remain limited as they should include active defense approach to allow anticipating and predicting intrusions before their occurrence. Consequently, they must be readapted. For this purpose we suggest a new generation of distributed intrusion detection system based on preventive detection approach and using intelligent and mobile agents. Our architecture benefits from mobile agent features and addresses some of the issues with centralized and hierarchical models. Also, it presents advantages in terms of increasing scalability and flexibility.

Keywords: Intrusion Detection System (IDS), preventive detection, mobile agents, distributed architecture

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Authors: Omair Ghori, Anton Stadler, Stefan Wilk, Wolfgang Effelsberg

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Fire-related incidents account for extensive loss of life and material damage. Quick and reliable detection of occurring fires has high real world implications. Whereas a major research focus lies on the detection of outdoor fires, indoor camera-based fire detection is still an open issue. Cameras in combination with computer vision helps to detect flames and smoke more quickly than conventional fire detectors. In this work, we present a computer vision-based smoke detection algorithm based on contrast changes and a multi-step classification. This work accelerates computer vision-based fire detection considerably in comparison with classical indoor-fire detection.

Keywords: contrast analysis, early fire detection, video smoke detection, video surveillance

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Authors: S. Aswath, Chinmaya Krishna Tilak, Amal Suresh, Ganesh Udupa

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There are technologies to control a humanoid robot in many ways. But the use of Electromyogram (EMG) electrodes has its own importance in setting up the control system. The EMG based control system helps to control robotic devices with more fidelity and precision. In this paper, development of an electromyogram based interface for human gesture recognition for the control of a humanoid robot is presented. To recognize control signs in the gestures, a single channel EMG sensor is positioned on the muscles of the human body. Instead of using a remote control unit, the humanoid robot is controlled by various gestures performed by the human. The EMG electrodes attached to the muscles generates an analog signal due to the effect of nerve impulses generated on moving muscles of the human being. The analog signals taken up from the muscles are supplied to a differential muscle sensor that processes the given signal to generate a signal suitable for the microcontroller to get the control over a humanoid robot. The signal from the differential muscle sensor is converted to a digital form using the ADC of the microcontroller and outputs its decision to the CM-530 humanoid robot controller through a Zigbee wireless interface. The output decision of the CM-530 processor is sent to a motor driver in order to control the servo motors in required direction for human like actions. This method for gaining control of a humanoid robot could be used for performing actions with more accuracy and ease. In addition, a study has been conducted to investigate the controllability and ease of use of the interface and the employed gestures.

Keywords: electromyogram, gesture, muscle sensor, humanoid robot, microcontroller, Zigbee

Procedia PDF Downloads 382

Authors: Diego Adrian Cardenas Jorge, Gerardo Mirando Guisado, Alfredo Barrientos Padilla

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A system based on Affective Computing can detect and interpret human information like voice, facial expressions and body movement to detect emotions and execute a corresponding response. This data is important due to the fact that a person can communicate more effectively with emotions than can be possible with words. This information can be processed through technological components like Facial Recognition, Gait Recognition or Gesture Recognition. As of now, solutions proposed using this technology only consider one component at a given moment. This research investigation proposes two solutions based on Affective Computing taking into account more than one component for emotion detection. The proposals reflect the levels of dependency between hardware devices and software, as well as the interaction process between the system and the user which implies the development of scenarios where both proposals will be put to the test in a live environment. Both solutions are to be developed in code by software engineers to prove the feasibility. To validate the impact on society and business interest, interviews with stakeholders are conducted with an investment mind set where each solution is labeled on a scale of 1 through 5, being one a minimum possible investment and 5 the maximum.

Keywords: affective computing, emotions, emotion detection, face recognition, gait recognition

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Authors: Rashid Mahmood

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The network as a service (NaaS) usage has been well-known from the last few years in the many applications, like mission critical applications. In the NaaS, prevention method is not adequate as the security concerned, so the detection method should be added to the security issues in NaaS. The authentication and encryption are considered the first solution of the NaaS problem whereas now these are not sufficient as NaaS use is increasing. In this paper, we are going to present the concept of intrusion detection and then survey some of major intrusion detection techniques in NaaS and aim to compare in some important fields.

Keywords: IDS, cloud, naas, detection

Procedia PDF Downloads 284

Authors: Yousef Farhaoui

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An IDS is a tool which is used to improve the level of security. We present in this paper different architectures of IDS. We will also discuss measures that define the effectiveness of IDS and the very recent works of standardization and homogenization of IDS. At the end, we propose a new model of IDS called BiIDS (IDS Based on the two principles of detection) for securing web servers and applications by the Intrusion Detection System (IDS).

Keywords: intrusion detection, architectures, characteristic, tools, security, web server

Procedia PDF Downloads 388

Authors: Ji-Hoon Hong, Dong-Hee Kim, Nam-Uk Kim, Tai-Myoung Chung

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Smartphone users are increasing rapidly. Accordingly, many companies are running BYOD (Bring Your Own Device: Policies to bring private-smartphones to the company) policy to increase work efficiency. However, smartphones are always under the threat of malware, thus the company network that is connected smartphone is exposed to serious risks. Most smartphone malware detection techniques are to perform an independent detection (perform the detection of a single target application). In this paper, we analyzed a variety of intrusion detection techniques. Based on the results of analysis propose an agent using the network IDS.

Keywords: android malware detection, software-defined network, interaction environment, android malware detection, software-defined network, interaction environment

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Authors: Medjram Sofiane, Babahenini Mohamed Chaouki, Mohamed Benali Yamina

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Skin detection is an important task for computer vision systems. A good method for skin detection means a good and successful result of the system. The colour is a good descriptor that allows us to detect skin colour in the images, but because of lightings effects and objects that have a similar colour skin, skin detection becomes difficult. In this paper, we proposed a method using the YCbCr colour space for skin detection and lighting effects elimination, then we use the information of texture to eliminate the false regions detected by the YCbCr colour skin model.

Keywords: skin detection, YCbCr, GLCM, texture, human skin

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Authors: Zhang Xiaodong, Tang Zixin, Liu Xin

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In order to avoid self-collision of space manipulators during operation process, a real-time detection method is proposed in this paper. The manipulator is fitted into a cylinder enveloping surface, and then the detection algorithm of collision between cylinders is analyzed. The collision model of space manipulator self-links can be detected by using this algorithm in real-time detection during the operation process. To ensure security of the operation, a safety threshold is designed. The simulation and experiment results verify the effectiveness of the proposed algorithm for a 7-DOF space manipulator.

Keywords: space manipulator, collision detection, self-collision, the real-time collision detection

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Authors: Norzalina Othman, Nurul Na’imy Wan, Azliza Mohd Rusli, Wan Noor Syahirah Meor Idris

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Iris detection is a process where the position of the eyes is extracted from the face images. It is a current method used for many applications such as for security purpose and drowsiness detection. This paper proposes the use of eyes detection in controlling side mirror of motor vehicles. The eyes detection method aims to make driver easy to adjust the side mirrors automatically. The system will determine the midpoint coordinate of eyes detection on RGB (color) image and the input signal from y-coordinate will send it to controller in order to rotate the angle of side mirror on vehicle. The eye position was cropped and the coordinate of midpoint was successfully detected from the circle of iris detection using Viola Jones detection and circular Hough transform methods on RGB image. The coordinate of midpoint from the experiment are tested using controller to determine the angle of rotation on the side mirrors.

Keywords: iris detection, midpoint coordinates, RGB images, side mirror

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Authors: Leping Chen, Daoxiang An, Xiaotao Huang

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Automatic vehicle detection using synthetic aperture radar (SAR) image has been widely researched, as well as using optical remote sensing images. However, most researches treat the detection as an independent problem, failing to make full use of SAR data information. In circular SAR (CSAR), the two long borders of vehicle will shrink if the imaging surface is set higher than the reference one. Based on above variance, an automatic vehicle detection using CSAR image is proposed to enhance detection ability under complex environment, such as vehicles’ closely packing, which confuses the detector. The detection method uses the multiple images generated by different height plane to obtain an energy-concentrated image for detecting and then uses the maximally stable extremal regions method (MSER) to detect vehicles. A result of vehicles’ detection is given to verify the effectiveness and correctness of proposed method.

Keywords: circular SAR, vehicle detection, automatic, imaging

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Authors: Bouchemha Amel, Chachoui Takieddine, H. Maalem

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Automatic detection of targets in a modern communication system RADAR is based primarily on the concept of adaptive CFAR detector. To have an effective detection, we must minimize the influence of disturbances due to the clutter. The detection algorithm adapts the CFAR detection threshold which is proportional to the average power of the clutter, maintaining a constant probability of false alarm. In this article, we analyze the performance of two variants of adaptive algorithms CA-CFAR and OS-CFAR and we compare the thresholds of these detectors in the marine environment (no-Gaussian) with a Weibull distribution.

Keywords: CFAR, threshold, clutter, distribution, Weibull, detection

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Authors: Siti Sara binti Hj Ahmad, Adil Elshiekh Abdalla

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The Arabic and Malay languages belong to different language’s families; while the Arabic language descends from the Semitic language, Malay belongs to the Austronesian (Malayo-Polynesian) family. Hence, the grammatical systems of the two languages differ from each other. Arabic, being a language found in the heart of the dessert, and Malay is the language found in the heart of thick equatorial forests, is another source of vital cultural differences. Consequently, it is expected that this situation will create differences in the ways of how speakers of the two languages perceive the world around them, convey and understand their messages. On the other hand, as the majority of the speakers of Malay language are Muslims, Arabic language found its way in this region; currently, Arabic is widely taught in school, some terms of it found their way in the Malay language. Accordingly, the Arabic language and culture have widely penetrated into the Malay language. This study is proposed with the aim to find out the differences and similarities between the two languages, in the term of the nonverbal communication. The result of this study will be of high significance, as it will help in enhancing the mutual understanding between the speakers of these languages. The comparative analysis approach will be utilized in this study.

Keywords: gesture, Arabic language, Malay language, comparative analysis

Procedia PDF Downloads 536

Authors: Rashid Mahmood, Muhammad Junaid Sarwar

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Mobile ad hoc networks (MANETs) use has been well-known from the last few years in the many applications, like mission critical applications. In the (MANETS) prevention method is not adequate as the security concerned, so the detection method should be added to the security issues in (MANETs). The authentication and encryption is considered the first solution of the MANETs problem where as now these are not sufficient as MANET use is increasing. In this paper we are going to present the concept of intrusion detection and then survey some of major intrusion detection techniques in MANET and aim to comparing in some important fields.

Keywords: MANET, IDS, intrusions, signature, detection, prevention

Procedia PDF Downloads 347

Authors: Sulaiman Al amro, Ali Alkhalifah

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The growing number of computer viruses and the detection of zero day malware have been the concern for security researchers for a large period of time. Existing antivirus products (AVs) rely on detecting virus signatures which do not provide a full solution to the problems associated with these viruses. The use of logic formulae to model the behaviour of viruses is one of the most encouraging recent developments in virus research, which provides alternatives to classic virus detection methods. In this paper, we proposed a comparative study about different virus detection techniques. This paper provides the advantages and drawbacks of different detection techniques. Different techniques will be used in this paper to provide a discussion about what technique is more effective to detect computer viruses.

Keywords: computer viruses, virus detection, signature-based, behaviour-based, heuristic-based

Procedia PDF Downloads 445

Authors: Kaewmart Pongakkasira

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This study investigated how different levels of pixelation affect face detection in natural scenes. Eye movements and reaction times, while observers searched for faces in natural scenes rendered in different ranges of pixels, were recorded. Detection performance for coarse visual detail at lower pixel size (3 x 3) was better than with very blurred detail carried by higher pixel size (9 x 9). The result is consistent with the notion that face detection relies on gross detail information of face-shape template, containing crude shape structure and features. In contrast, detection was impaired when face shape and features are obscured. However, it was considered that the degradation of scenic information might also contribute to the effect. In the next experiment, a more direct measurement of the effect of pixelation on face detection, only the embedded face photographs, but not the scene background, will be filtered.

Keywords: eye movements, face detection, face-shape information, pixelation

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Authors: M. Ranjeeth, S. Anuradha

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Spectrum sensing is the main feature of cognitive radio technology. Spectrum sensing gives an idea of detecting the presence of the primary users in a licensed spectrum. In this paper we compare the theoretical results of detection probability of different fading environments like Rayleigh, Rician, Nakagami-m fading channels with the simulation results using energy detection based spectrum sensing. The numerical results are plotted as P_f Vs P_d for different SNR values, fading parameters. It is observed that Nakagami fading channel performance is better than other fading channels by using energy detection in spectrum sensing. A MATLAB simulation test bench has been implemented to know the performance of energy detection in different fading channel environment.

Keywords: spectrum sensing, energy detection, fading channels, probability of detection, probability of false alarm

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Authors: P. Revathi, T. Mrunalini, K. Padma Priya, P. Ramya, R. Saranya

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During the tough times of covid, those people who were hospitalized found it difficult to always convey what they wanted to or needed to the attendee. Sometimes the attendees might also not be there. In that case, the patients can use simple hand gestures to control electrical appliances (like its set it for a zero watts bulb)and three other gestures for voice note intimation. In this AI-based hand recognition project, NodeMCU is used for the control action of the relay, and it is connected to the firebase for storing the value in the cloud and is interfaced with the python code via raspberry pi. For three hand gestures, a voice clip is added for intimation to the attendee. This is done with the help of Google’s text to speech and the inbuilt audio file option in the raspberry pi 4. All the 5 gestures will be detected when shown with their hands via a webcam which is placed for gesture detection. A personal computer is used for displaying the gestures and for running the code in the raspberry pi imager.

Keywords: onion harvesting, automatic pluging, camera, raspberry pi

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Authors: John Onyima, Ikechukwu Ezepue

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Virtualization and cloud computing are among the fast-growing computing innovations in recent times. Organisations all over the world are moving their computing services towards the cloud this is because of its rapid transformation of the organization’s infrastructure and improvement of efficient resource utilization and cost reduction. However, this technology brings new security threats and challenges about safety, reliability and data confidentiality. Evidently, no single security technique can guarantee security or protection against malicious attacks on a cloud computing network hence an integrated model of intrusion detection and prevention system has been proposed. Anomaly-based and signature-based detection techniques will be integrated to enable the network and its host defend themselves with some level of intelligence. The anomaly-base detection was implemented using the local deviation factor graph-based (LDFGB) algorithm while the signature-based detection was implemented using the snort algorithm. Results from this collaborative intrusion detection and prevention techniques show robust and efficient security architecture for cloud computing networks.

Keywords: anomaly-based detection, cloud computing, intrusion detection, intrusion prevention, signature-based detection

Procedia PDF Downloads 273