scispace - formally typeset
Search or ask a question
Proceedings ArticleDOI

Performance Analysis of a Bottleneck Layer Network in the Estimation of Cyber-Attacks

29 Mar 2022-pp 181-187
TL;DR: The performance of the bottleneck layer on the ANN and DNN algorithm is verified in the proposed work with an openly available CIDDS-001 dataset dataset, which contains server traffic data on OpenStack and external severs.
Abstract: Cyber-attack is an attempt made from an individual or cybercriminals to hack a particular computer or network through internet. This leads to loss of information stored in the connected system and in certain cases it leads to denial of service. The traditional methods on addressing cyber-attacks are not efficient to the complex and high sophisticated attacks. Hence the deep learning based techniques are generated in recent years for estimating the attacks presence in a communication network. However, the deep learning networks are complex in nature as they are handled with a huge range of features during its operation. Therefore a bottleneck layer was developed to reduce the parameters count and feature formulations from a given data. The residual blocks are deeper than the traditional network architectures and it is achieved by enabling a 1x1 convolution block in the design flow. The performance of the bottleneck layer on the ANN and DNN algorithm is verified in the proposed work with an openly available CIDDS-001 dataset dataset. The CIDDS dataset is one of the recent dataset consists of server traffic data on OpenStack and external severs.
Citations
More filters
Proceedings ArticleDOI
25 May 2022
TL;DR: The scope of this project is defined by determining the patient health factors that influence the patient prior to having therapy and by providing a safe route for HBOT through a system that monitors the parameters to ensure that the treatment is appropriate for the patient's condition.
Abstract: A portable hyperbaric chamber may be designed for emergency therapy that can be administered to the patient immediately after the accident or incident, hence boosting the patient's chances of survival. Because the objective is to create an emergency device, the large oxygen cylinder used to give full oxygen to the patient within the chamber is omitted, hence preserving the hyperbaric environment inside. The scope of this project is defined by determining the patient health factors that influence the patient prior to having therapy and by providing a safe route for HBOT through a system that monitors the parameters to ensure that the treatment is appropriate for the patient's condition. The patient's ECG, heart rate, breathing rate, and temperature are recorded, and the patient's history is saved on a patient card that is read by a card reader.

1 citations

Proceedings ArticleDOI
S. P., S. R, M. M, L. P, D. R, Vinod Kumar D 
24 Nov 2022
TL;DR: In this paper , a new approach for reducing torque ripple has been created by using Proportional Integral digital control signals as DVR, which is a D-FACTS (Distribution Flexible AC Transmission System) device that might be extensively used to address the challenges of the distribution grid's nonstandard voltage, current, and frequency.
Abstract: Consumers and utility companies alike may be negatively impacted by poor power quality. The integration of renewable energy sources, smart grid systems, and extensive use of power electronics equipment has resulted in a slew of problems in modern electric power systems. It is possible that harmonics in current and voltage as well as voltage drop, and swell may cause harm to sensitive electronic equipment. Interference from other sections of the system may cause input voltage changes in these devices. For this reason, in today's technologically advanced world, the reliability and safety of the power system depend on power quality. DVR, or Dynamic Voltage Restorer, is a D-FACTS (Distribution Flexible AC Transmission System) device that might be extensively used to address the challenges of the distribution grid's non-standard voltage, current, and frequency. By using Proportional Integral digital control signals as DVR, a new approach for reducing torque ripple has been created in this article. After that, control signals are supplied to the voltage source converter input of the brushless direct current motor's voltage source converter. This control approach reduces torque ripples. This aim is continuously met, and therefore the suggested model outperforms a few established techniques, such as FCS-MPC, PWM-MPC, and RSC, with the proposed Pulsating Proportional Integral Control (PPIC) based DVR. 3.2Nm average torque, 10.64 % torque ripple, 8.56I of RMS DC link current, 961.52 W power, 2333.9 rpm speed are achieved using the suggested approach.
References
More filters
Journal ArticleDOI
01 Feb 2020
TL;DR: A survey of deep learning approaches for cyber security intrusion detection, the datasets used, and a comparative study to evaluate the efficiency of several methods are presented.
Abstract: In this paper, we present a survey of deep learning approaches for cybersecurity intrusion detection, the datasets used, and a comparative study. Specifically, we provide a review of intrusion detection systems based on deep learning approaches. The dataset plays an important role in intrusion detection, therefore we describe 35 well-known cyber datasets and provide a classification of these datasets into seven categories; namely, network traffic-based dataset, electrical network-based dataset, internet traffic-based dataset, virtual private network-based dataset, android apps-based dataset, IoT traffic-based dataset, and internet-connected devices-based dataset. We analyze seven deep learning models including recurrent neural networks, deep neural networks, restricted Boltzmann machines, deep belief networks, convolutional neural networks, deep Boltzmann machines, and deep autoencoders. For each model, we study the performance in two categories of classification (binary and multiclass) under two new real traffic datasets, namely, the CSE-CIC-IDS2018 dataset and the Bot-IoT dataset. In addition, we use the most important performance indicators, namely, accuracy, false alarm rate, and detection rate for evaluating the efficiency of several methods.

464 citations

Journal ArticleDOI
09 Apr 2021
TL;DR: An effective Chaotic based Biometric authentication scheme for user interaction layer of cloud is proposed and implemented and varies from conventional methods by utilizing a N-stage Arnold Transform to securely verify the claim of the so-called legitimate user.
Abstract: Cloud computing models have emerged to be a key player in the field of information processing in the recent decade. Almost all the services related to data processing and storage from firms work on a cloud platform providing the requested services to the consumers at any point of time and location. Security is an essential concern in cloud models as they primarily deal with data. Since multitude of user’s access cloud by way of storing confidential information in the virtual storage platform or accessing vital data from archives, security and privacy is of prime concern. This has been taken as the motivation of this research work. An effective Chaotic based Biometric authentication scheme for user interaction layer of cloud is proposed and implemented in this research paper. The proposed method uses fingerprint as the biometric trait and varies from conventional methods by utilizing a N-stage Arnold Transform to securely verify the claim of the so-called legitimate user. The experimentations have been compared with existing benchmark methods and superior performances observed in terms of detections, false detection accuracy etc.

84 citations

Journal ArticleDOI
TL;DR: In this article, an intrusion detection system (IDS) is proposed for the next-generation electrical grid environments that use Modbus/Transmission Control Protocol (TCP) and Distributed Network Protocol 3 (DNP3) protocols.
Abstract: The interconnected and heterogeneous nature of the next-generation Electrical Grid (EG), widely known as Smart Grid (SG), bring severe cybersecurity and privacy risks that can also raise domino effects against other Critical Infrastructures (CIs). In this paper, we present an Intrusion Detection System (IDS) specially designed for the SG environments that use Modbus/Transmission Control Protocol (TCP) and Distributed Network Protocol 3 (DNP3) protocols. The proposed IDS called MENSA (anoMaly dEtection aNd claSsificAtion) adopts a novel Autoencoder-Generative Adversarial Network (GAN) architecture for (a) detecting operational anomalies and (b) classifying Modbus/TCP and DNP3 cyberattacks. In particular, MENSA combines the aforementioned Deep Neural Networks (DNNs) in a common architecture, taking into account the adversarial loss and the reconstruction difference. The proposed IDS is validated in four real SG evaluation environments, namely (a) SG lab, (b) substation, (c) hydropower plant and (d) power plant, solving successfully an outlier detection (i.e., anomaly detection) problem as well as a challenging multiclass classification problem consisting of 14 classes (13 Modbus/TCP cyberattacks and normal instances). Furthermore, MENSA can discriminate five cyberattacks against DNP3. The evaluation results demonstrate the efficiency of MENSA compared to other Machine Learning (ML) and Deep Learning (DL) methods in terms of Accuracy, False Positive Rate (FPR), True Positive Rate (TPR) and the F1 score.

68 citations

Journal ArticleDOI
03 Sep 2021
TL;DR: This work proposes a searchable encryption algorithm that can be used for sharing blockchain- based medical image data that provides traceability, unforgettable and non-tampered image data using blockhain technology, overcoming the drawbacks of blockchain such as computing power and storage.
Abstract: Cloud applications that work on medical data using blockchain is used by managers and doctors in order to get the image data that is shared between various healthcare institutions. To ensure workability and privacy of the image data, it is important to verify the authenticity of the data, retrieve cypher data and encrypt plain image data. An effective methodology to encrypt the data is the use of a public key authenticated encryption methodology which ensures workability and privacy of the data. But, there are a number of such methodologies available that have been formulated previously. However, the drawback with those methodologies is their inadequacy in protecting the privacy of the data. In order to overcome these disadvantages, we propose a searchable encryption algorithm that can be used for sharing blockchain- based medical image data. This methodology provides traceability, unforgettable and non-tampered image data using blockhain technology, overcoming the drawbacks of blockchain such as computing power and storage. The proposed work will also sustain keyword guessing attacks apart from verification of authenticity and privacy protection of the image data. Taking these factors into consideration, it is determine that there is much work involved in providing stronger security and protecting privacy of data senders. The proposed methodology also meets the requirement of indistinguishability of trapdoor and ciphertext. The highlights of the proposed work are its capability in improving the performance of the system in terms of security and privacy protection.

56 citations

Journal ArticleDOI
TL;DR: This method models user behavior as sequences of user events including operation of home IoT devices and other monitored activities, and generates multiple event sequences by removing some events and learning the frequently observed sequences.
Abstract: As several home appliances, such as air conditioners, heaters, and refrigerators, were connecting to the Internet, they became targets of cyberattacks, which cause serious problems such as compromising safety and even harming users. We have proposed a method to detect such attacks based on user behavior. This method models user behavior as sequences of user events including operation of home IoT (Internet of Things) devices and other monitored activities. Considering users behave depending on the condition of the home such as time and temperature, our method learns event sequences for each condition. To mitigate the impact of events of other users in the home included in the monitored sequence, our method generates multiple event sequences by removing some events and learning the frequently observed sequences. For evaluation, we constructed an experimental network of home IoT devices and recorded time data for four users entering/leaving a room and operating devices. We obtained detection ratios exceeding 90% for anomalous operations with less than 10% of misdetections when our method observed event sequences related to the operation. In this article, we also discuss the effectiveness of our method by comparing with a method learning users’ behavior by Hidden Markov Models.

56 citations