Real-time Security Research Articles

Emotion classification using EEG signal for women safety application based on deep learning

An emotion is a conscious logical response that varies for different situations in women’s life. These mental responses are caused by physiological, cognitive, and behavioral changes. Gender-based violence undermines the participation of women in decision-making, resulting in a decline in their quality of life. More accurate and automatic classification of women’s emotions can enhance human-computer interfaces and security in real time. There are some wearable technologies and mobile applications that claim to ensure the safety of women. However, they rely on limited social action and are ineffective at ensuring women’s safety when and where it is needed. In this work, a novel CDB-LSTM network has been proposed to accurately classify the emotions of women in seven different classes. The electroencephalogram (EEG) offers non-radioactive methods of identifying emotions. Initially, the EEG signals are preprocessed and they are converted into images via Time-Frequency Representation (TPR). A smoothed pseudo-Wigner-Ville distribution (SPWVD) is employed to convert the EEG time-domain signals into input images. Consequently, these converted images are given as input to the Convolutional Deep Belief Network (CDBN) for extracting the most relevant features. Finally, Bi-directional LSTM is used for classifying the emotions of women into seven classes namely: happy, relax, sad, fear, anxiety, anger, and stress. The proposed CDB-LSTM network preserves the high accuracy range of 97.27% in the validation phase. The proposed CDB-LSTM network improves the overall accuracy by 6.20% 32.98% 6.85% and 3.30% better than CNN-LSTM, Multi-domain feature fusion model, GCNN-LSTM and CNN with SVM and DT respectively.

Large-lateral-area SnO2 nanosheets with a loose structure for high-performance acetone sensor at the ppt level

Gas sensors with high sensitivity and high selectivity are required in practical applications to distinguish between target molecules in the detection of volatile organic compounds, real-time security alerts, and clinical diagnostics. Semiconducting tin oxide (SnO2) is highly regarded as a gas-sensing material due to its exceptional responsiveness to changes in gaseous environments and outstanding chemical stability. Herein, we successfully synthesized a large-lateral-area SnO2 nanosheet with a loose structure as a gas sensing material by a one-step facile aqueous solution process without a surfactant or template. The SnO2 sensor exhibited a remarkable sensitivity (Ra/Rg = 1.33) at 40 ppt for acetone, with a theoretical limit of detection of 1.37 ppt, which is the lowest among metal oxide semiconductor-based gas sensors. The anti-interference ability of acetone was higher than those of pristine SnO2 and commercial sensors. These sensors also demonstrated perfect reproducibility and long-term stability of 100 days. The ultrasensitive response of the SnO2 nanosheets toward acetone was attributed to the specific loose large lateral area structure, small grain size, and metastable (101) crystal facets. Considering these advantages, SnO2 nanosheets with larger lateral area sensors have great potential for the detection and monitoring of acetone.

Sample Reduction-Based Pairwise Linear Regression Classification for IoT Monitoring Systems

At present, the development of the Internet of Things (IoT) has become a significant symbol of the information age. As an important research branch of it, IoT-based video monitoring systems have achieved rapid developments in recent years. However, the mode of front-end data collection, back-end data storage and analysis adopted by traditional monitoring systems cannot meet the requirements of real-time security. The currently widely used edge computing-based monitoring system can effectively solve the above problems, but it has high requirements for the intelligent algorithms that will be deployed at the edge end (front-end). To meet the requirements, that is, to obtain a lightweight, fast and accurate video face-recognition method, this paper proposes a novel, set-based, video face-recognition framework, called sample reduction-based pairwise linear regression classification (SRbPLRC), which contains divide SRbPLRC (DSRbPLRC), anchor point SRbPLRC (APSRbPLRC), and attention anchor point SRbPLRC (AAPSRbPLRC) methods. Extensive experiments on some popular video face-recognition databases demonstrate that the performance of proposed algorithms is better than that of several state-of-the-art classifiers. Therefore, our proposed methods can effectively meet the real-time and security requirements of IoT monitoring systems.

Safety Evaluation Modelling of Hydrogen Energy Industrial Chain for Transportation-energy Integration System in China

This paper divided the safety evaluation indexes of hydrogen energy industrial chain for transportation-energy integration in China into four target tiers, such as hydrogen production safety, storage & transmission safety, supply safety and hydrogen utilization safety. Then the influential factors of security risks under different target tiers were analyzed, and the evaluation index system of security risks were established. Furthermore, according to the characteristics of different target tiers of hydrogen energy industrial chain, the safety evaluation model was constructed by combining analytic hierarchy process with risk assessment matrix. This safety evaluation model can assess the safety status of hydrogen energy chain quantitatively and qualitatively, including calculating the weight of each index and analyzing the risk level of whole system. Moreover, such modeling method could provide a core assessment framework for enterprises and industries related to hydrogen energy to realize real-time security monitoring and alarm.

A Key-Based Multi-Mode Clock-Controlled Stream Cipher for Real-Time Secure Communications of IoT

With the rapid development of the Internet and wireless communications, as well as the popularization of personal communication systems, the security of real-time communications is demanded. The efficient technology of stream ciphers can satisfy this requirement of security. In this paper, to enhance the security strength of stream ciphers, we design a key-based multi-mode clock-controlled stream cipher for real-time secure communications of the Internet of things (IoT). The proposed stream cipher is equipped with a multi-mode depending on the key. The different working modes are shipped with different encrypting circuits depending on the user’s key. We analyze the period, the linear complexity, and use known attacks to verify the security strength of the proposed cipher. Compared with existing dual mode clock-controlled stream ciphers, the merits of our proposed cipher are its long period, high linear complexity, low hardware complex, low initialization clock, and simplicity in mode switching. Furthermore, the proposed cipher passes the FIPS PUB 140-1 and SP800-22 tests, obtaining at least 97.00%.

Real-time security margin control using deep reinforcement learning

This paper develops a real-time control method based on deep reinforcement learning aimed to determine the optimal control actions to maintain a sufficient secure operating limit. The secure operating limit refers to the limit to the most stressed pre-contingency operating point of an electric power system that can withstand a set of credible contingencies without violating stability criteria. The developed deep reinforcement learning method uses a hybrid control scheme that is capable of simultaneously adjusting both discrete and continuous action variables. The performance is evaluated on a modified version of the Nordic32 test system. The results show that the developed deep reinforcement learning method quickly learns an effective control policy to ensure a sufficient secure operating limit for a range of different system scenarios. The performance is also compared to a control based on a rule-based look-up table and a deep reinforcement learning control adapted for discrete action spaces. The hybrid deep reinforcement learning control managed to achieve significantly better on all of the defined test sets, indicating that the possibility of adjusting both discrete and continuous action variables resulted in a more flexible and efficient control policy.

A differential game approach for real-time security defense decision in scale-free networks

The current network security defense decision method is based on the principle of attack–defense confrontation and generally uses a random network model to project the real network. Most of the existing complex network defense decision methods consider a single defender while ignoring the confrontation and dependence between network attack and defense behaviors. In addition, the pertinence and dynamics of the defense effect are also insufficient. This article comprehensively considers the structure of a scale-free network and characteristics of network attack and defense behavior and combines a complex network with differential game theory to study the real-time defense decision problem. First, a propagation model based on a scale-free network is designed to realize the real-time fluctuation analysis of a network security state. Then, the attack and defense strategy are described, and the calculation method of revenue based on the confrontation analysis of the attack and defense behavior and a change in the security state of the network nodes based on the saddle point equilibrium strategy are introduced. Finally, based on the average connection degree of the real word Internet, a simulated network is constructed to conduct experiments. By comparison with random defense strategies and several typical complex network defense decision methods, the effectiveness and advancement of the proposed method are verified.

The Privacy Flag Observatory: A Crowdsourcing Tool for Real Time Privacy Threats Evaluation

Instilling good privacy practices to developers and users appears to be a difficult and daunting task. The World Wide Web encompasses a panspermia of different technologies, commercial and open source apis, evolving security standards and protocols that can be deployed towards the implementation of complex, powerful, web applications. At the same time, the proliferation of applications and services on all types of devices has also increased the attack surface for privacy threats. In this paper, we present the Privacy Flag Observatory, a platform which is one of the main tools produced by the Privacy Flag eu funded research project. The goal of this initiative is to raise awareness among European citizens of the potential privacy threats that beset the software and services they trust and use every day, including websites and smartphone applications. The Privacy Flag Observatory is one of the components that contributed to a large extent, to the success of the project’s goals. It is a real-time security and privacy threat monitoring platform whose aim is to collect, archive, analyze and present security and privacy-related information to the broader public as well as experts. Although the platform relies on crowdsourcing information gathering strategies and interacts with several other components installed on users’ devices or remote servers and databases, in this paper, we focus on the observatory platform referring only cursorily to other components such as the mobile phone add-on.

A Comprehensive Survey on the Cooperation of Fog Computing Paradigm-Based IoT Applications: Layered Architecture, Real-Time Security Issues, and Solutions

A Comprehensive Survey on the Cooperation of Fog Computing Paradigm-Based IoT Applications: Layered Architecture, Real-Time Security Issues, and Solutions

Physics-Constrained Robustness Evaluation of Intelligent Security Assessment for Power Systems

Machine learning (ML) algorithms have been widely developed to enable real-time security assessment for large-scale electricity grids. However, it also has been extensively recognized that the ML models are vulnerable to adversarial examples, which are slightly perturbed instances that can mislead the classifications but cannot be distinguished by human eyes. Considering the adversarial examples against the ML-based security assessment, they must not only cause misclassification but also follow the power balance and transfer limits and bypass the bad data detection. To this end, this paper proposes a novel concept named physics-constrained robustness that aims to computing a lower-bound of adversarial perturbations, evaluating the vulnerability of the ML-based intelligent security assessment (ISA) for power systems. A general optimization problem is formulated to compute the physics-constrained robustness of ISA with the mislabeling, power balance, power limitation, and invisible constraints. The analytical results and comparing relationship of ISA’s robustness with different constraints are provided. By using the static security assessment as an example, we provide explicit formulations to evaluate the physics-constrained robustness of. Finally, extensive experiments are conducted to evaluate the physics-constrained robustness of ISA in static and dynamic cases with the real-world load profiles from New York State and provide suggestions to select the ML models and parameters.

Elliptic-Curve based Crypto-Compression Scheme For Medical Videos in Connected Ambulance

Communication and collaboration in transport systems are overdeveloped through Vehicular Ad hoc Networks (VANET). In VANETs, various messages are sent to activate warnings about road accidents, sharp turns, traffic jams, etc. Data security in VANETs is then essential to prevent the intrusion of hackers. Although security and safety in VANETs have been the subject of several research works, some intelligent vehicles contain extremely sensitive data that require specific high-level and real-time security solutions. This is the case for connected medical ambulances in which surgery can be performed with the help of an outside doctor. In such cases, video is the most complicated type of data to handle due to its size, content, and structure. In this paper, an elliptical approach to video representation is introduced, and a video crypto-compression scheme is proposed. The scheme described is built on Elliptic Curve Cryptography, which demonstrates good performance in computer security in recent years.

A Real-Time Deep Learning-based Smart Surveillance Using Fog Computing: A Complete Architecture

Fog computing offers low-latency and real-time big-data processing capabilities closer to the network edge. This particular benefit addresses the main bottleneck in a centralized cloud framework, which is, it cannot process latency-sensitive large video frames generated from the Internet of Things-based video surveillance cameras in real-time. Besides, the recent advancements in the computer vision field offer many state-of-the-art image processing capabilities that can be utilized for real-time surveillance data processing. Deploying those processing powers at several fog computing layers can bring novel solutions for computer vision-based real-time security solutions. This paper proposes a deep learning-based framework for smart video surveillance that can process the real-time frames on two consecutive fog layers, one for action recognition and the other for criminal threat-based response generation. The proposed architecture consists of three major modules. The first module is responsible for capturing surveillance videos by deploying RaspberryPi cameras in a distributed network. The second module is responsible for action recognition using a deep learning-based model installed inside NVIDIA Jetson Nano-devices placed on two fog layers. Finally, the security response is generated and broadcast to the law-enforcement agency. To evaluate the proposed model, experiments on semantic segmentation-based scene object recognition were run. The experimental results came up with a suitable recognition model that can be deployed in the fog layers of our proposed framework.

A Data Driven Security Correction Method for Power Systems with UPFC

The access of unified power flow controllers (UPFC) has changed the structure and operation mode of power grids all across the world, and it has brought severe challenges to the traditional real-time calculation of security correction based on traditional models. Considering the limitation of computational efficiency regarding complex, physical models, a data-driven power system security correction method with UPFC is, in this paper, proposed. Based on the complex mapping relationship between the operation state data and the security correction strategy, a two-stage deep neural network (DNN) learning framework is proposed, which divides the offline training task of security correction into two stages: in the first stage, the stacked auto-encoder (SAE) classification model is established, and the node correction state (0/1) output based on the fault information; in the second stage, the DNN learning model is established, and the correction amount of each action node is obtained based on the action nodes output in the previous stage. In this paper, the UPFC demonstration project of Nanjing West Ring Network is taken as a case study to validate the proposed method. The results show that the proposed method can fully meet the real-time security correction time requirements of power grids, and avoid the inherent defects of the traditional model method without an iterative solution and can also provide reasonable security correction strategies for N-1 and N-2 faults.

Discover and safe: an automated security management system for educational institutions

In this paper, we proposed discover and safe (DaS) automatic security system by the help of face recognition. The focus of this automated system is to provide high-level security to manage the entry of people with face detection. This paper proposes haar cascade algorithm with dip libraries to create a camera-based real-time security management system through face detection and recognition. Haar cascade algorithm is an object detection algorithm which is mainly used in identifying face of any image or a real-time video (by webcam or building camera). The DaS framework worked on two phases: 1) to locate whether the VM is an unintended security; 2) to secure mission critical applications. The DaS access system implemented the face encoding scheme to detect the face and eye which works effectively on light and illumination changes. The results show that DaS framework can Armor the VM from obscured security problems and steal hidden doors against the attackers.

Design and implementation of Pedestrian detection based on Zynq APSoc

Pedestrian recognition is an important content of intelligent security surveillance video processing system. In order to meet the application requirements of miniaturization and real-time security system, a moving target recognition system based on APSoc platform is designed. The system uses ov5640 camera as the video collector, zynq7020 as the development platform, and uses the soft and hard co-design to realize each function module. Finally, the pedestrian image acquisition and real-time display system is successfully completed. The test results show that the system can effectively and stably track pedestrians and moving objects within a certain range of distance, and can be displayed in real time, miniaturized and low power consumption, which can be further applied in the field of security.

A Mobile Based Activity Recog-nition Model

In the last decade, activity recognition (AR) of humans via smart phones became important and attractive subject for scholars and developers in many areas from health care to real-time security systems. In this research, we worked on AR that based on data collected from Android-based smart phone's accelerometers held at waist region while performing different activities (i.e. walking, jogging, climbing stairs, downing stairs, sitting, and standing). To achieve this goal, six classification algorithms were performed: Naïve Bayes (NB), Multi Layer Perceptron (MLP), Bayes Network (BN), Sequential Minimal Optimization (SMO), Kstar, and Decision Tree (DT). Experimental results of the six models were illustrated and analyzed. Comparison results declare that MLP algorithm outperforms other algorithms.

Split-based sequential sampling for realtime security assessment

Machine learning (ML) for real-time security assessment requires a diverse training database to be accurate for scenarios beyond historical records. Generating diverse operating conditions is highly relevant for the uncertain future of emerging power systems that are completely different to historical power systems. In response, for the first time, this work proposes a novel split-based sequential sampling approach based on optimisation that generates more diverse operation scenarios for training ML models than state-of-the-art approaches. This work also proposes a volume-based coverage metric, the convex hull volume (CHV), to quantify the quality of samplers based on the coverage of generated data. This metric accounts for the distribution of samples across multidimensional space to measure coverage within the physical network limits. Studies on IEEE test cases with 6, 68 and 118 buses demonstrate the efficiency of the approach. Samples generated using the proposed split-based sampling cover 37.5% more volume than random sampling in the IEEE 68-bus system. The proposed CHV metric can assess the quality of generated samples (standard deviation of 0.74) better than a distance-based coverage metric which outputs the same value (standard deviation of <0.001) for very different data distributions in the IEEE 68-bus system. As we demonstrate, the proposed split-based sampling is relevant as a pre-step for training ML models for critical tasks such as security assessment.

LFSS-KF: lightweight fast real-time security standards with key fusion for surveillance videos

ABSTRACT Uses of video surveillance (VS) have exponentially increased using the internet as a platform. Security concerns related to the transmission of videos must be addressed. Even though the majority of current encryption standards are effective, it is computationally demanding and unsuitable for real-time VS applications. Lightweight crypto-encryption standards are necessary to meet real-time needs. This paper provides a thorough analysis of various encryption standards to protect video data and evaluates vulnerability under various attacks. A Lightweight Fast Security Standard (LFSS) is proposed to meet real-time requirements by adopting dual key fusion (KF) approach. Entropy and crypto weights are quantitatively compared with proposed and chosen lightweight ciphers for surveillance videos based on NCPR, UACI and attacks into account. The proposed LFSS method offers a good NCPR of 99.5806 and offers only 40% and 3% average execution time over Secure Internet of things (SIT) and Secure Force (SF) security standards, respectively.

Spray: Streaming Log Parser for Real-Time Analysis

Logs is an important source of data in the field of security analysis. Log messages characterized by unstructured text, however, pose extreme challenges to security analysis. To this end, the first issue to be addressed is how to efficiently parse logs into structured data in real-time. The existing log parsers mostly parse raw log files by batch processing and are not applicable to real-time security analysis. It is also difficult to parse large historical log sets with such parsers. Some streaming log parsers also have some demerits in accuracy and parsing performance. To realize automatic, accurate, and efficient real-time log parsing, we propose Spray, a streaming log parser for real-time analysis. Spray can automatically identify the template of a real-time incoming log and accurately match the log and its template for parsing based on the law of contrapositive. We also improve Spray’s parsing performance based on key partitioning and search tree strategies. We conducted extensive experiments from such aspects as accuracy and performance. Experimental results show that Spray is much more accurate in parsing a variety of public log sets and has higher performance for parsing large log sets.

Kriper: A blockchain network with permissioned storage

Blockchain has been a revolution in the past few years. Beyond the new currencies that were created around different incarnations of the blockchain concept, there are many other contributions that provide interesting services as a data linked structure using a decentralized network that provide a high level of security. Companies have developed many projects to incorporate blockchain into their business logic pursuing to incorporate other related services as persistence of large volumes of data, privacy or anonymity of transactions, distributed data processing, security (confidentiality, integrity, and availability), document management or micro messages in real time. Nevertheless, as it will be discussed in this article, current blockchains do not meet the needs of companies in many aspects, leading to a scarce or superficial adoption. This article introduces Kriper, a blockchain that aims at meeting corporate world needs by responding with a community-based, open blockchain that may also be segregated and private for certain uses whereas it provides a permissioned distributed storage and micro message lightweight services.