Lightweight Cryptographic Algorithms for Guessing Attack Protection in Complex Internet of Things Applications

As the world keeps advancing, the need for automated interconnected devices has started to gain significance; to cater to the condition, a new concept Internet of Things (IoT) has been introduced that revolves around smart devicesʼ conception. These smart devices using IoT can communicate with each other through a network to attain particular objectives, i.e., automation and intelligent decision making. IoT has enabled the users to divide their household burden with machines as these complex machines look after the environment variables and control their behavior accordingly. As evident, these machines use sensors to collect vital information, which is then the complexity analyzed at a computational node that then smartly controls these devicesʼ operational behaviors. Deep learning-based guessing attack protection algorithms have been enhancing IoT security; however, it still has a critical challenge for the complex industries’ IoT networks. One of the crucial aspects of such systems is the need to have a significant training time for processing a large dataset from the networkʼs previous flow of data. Traditional deep learning approaches include decision trees, logistic regression, and support vector machines. However, it is essential to note that this convenience comes with a price that involves security vulnerabilities as IoT networks are prone to be interfered with by hackers who can access the sensor/communication data and later utilize it for malicious purposes. This paper presents the experimental study of cryptographic algorithms to classify the types of encryption algorithms into the asymmetric and asymmetric encryption algorithm. It presents a deep analysis of AES, DES, 3DES, RSA, and Blowfish based on timing complexity, size, encryption, and decryption performances. It has been assessed in terms of the guessing attack in real-time deep learning complex IoT applications. The assessment has been done using the simulation approach and it has been tested the speed of encryption and decryption of the selected encryption algorithms. For each encryption and decryption, the tests executed the same encryption using the same plaintext for five separate times, and the average time is compared. The key size used for each encryption algorithm is the maximum bytes the cipher can allow. To the comparison, the average time required to compute the algorithm by the three devices is used. For the experimental test, a set of plaintexts is used in the simulation—password-sized text and paragraph-sized text—that achieves target fair results compared to the existing algorithms in real-time deep learning networks for IoT applications.

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Real-time force monitoring of smart grippers for Internet of Things (IoT) applications

Journal of Industrial Information Integration ◽

10.1016/j.jii.2018.02.004 ◽

2018 ◽

Vol 11 ◽

pp. 19-28 ◽

Cited By ~ 17

Author(s):

Zhuming Bi ◽

Yanfei Liu ◽

Jeremiah Krider ◽

Joshua Buckland ◽

Andrew Whiteman ◽

...

Keyword(s):

Internet Of Things ◽

Real Time ◽

Iot Applications ◽

Force Monitoring

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Toward Polymorphic Internet of Things Receivers Through Real-Time Waveform-Level Deep Learning

GetMobile Mobile Computing and Communications ◽

10.1145/3511285.3511294 ◽

2022 ◽

Vol 25 (3) ◽

pp. 28-33

Author(s):

Francesco Restuccia ◽

Tommaso Melodia

Keyword(s):

Deep Learning ◽

Internet Of Things ◽

Real Time ◽

Software Defined Radio ◽

Wireless Systems ◽

Physical World ◽

Wireless Protocols ◽

Key Innovation ◽

Custom Made ◽

Time Waveform

Wireless systems such as the Internet of Things (IoT) are changing the way we interact with the cyber and the physical world. As IoT systems become more and more pervasive, it is imperative to design wireless protocols that can effectively and efficiently support IoT devices and operations. On the other hand, today's IoT wireless systems are based on inflexible designs, which makes them inefficient and prone to a variety of wireless attacks. In this paper, we introduce the new notion of a deep learning-based polymorphic IoT receiver, able to reconfigure its waveform demodulation strategy itself in real time, based on the inferred waveform parameters. Our key innovation is the introduction of a novel embedded deep learning architecture that enables the solution of waveform inference problems, which is then integrated into a generalized hardware/software architecture with radio components and signal processing. Our polymorphic wireless receiver is prototyped on a custom-made software-defined radio platform. We show through extensive over-the-air experiments that the system achieves throughput within 87% of a perfect-knowledge Oracle system, thus demonstrating for the first time that polymorphic receivers are feasible.

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Deep Learning-Based Human Activity Real-Time Recognition for Pedestrian Navigation

Sensors ◽

10.3390/s20092574 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2574 ◽

Cited By ~ 1

Author(s):

Junhua Ye ◽

Xin Li ◽

Xiangdong Zhang ◽

Qin Zhang ◽

Wu Chen

Keyword(s):

Neural Network ◽

Deep Learning ◽

Real Time ◽

Learning Algorithms ◽

Support Vector ◽

Classification Methods ◽

Pedestrian Navigation ◽

Mode Recognition ◽

Posture Recognition ◽

Motion Mode

Several pedestrian navigation solutions have been proposed to date, and most of them are based on smartphones. Real-time recognition of pedestrian mode and smartphone posture is a key issue in navigation. Traditional ML (Machine Learning) classification methods have drawbacks, such as insufficient recognition accuracy and poor timing. This paper presents a real-time recognition scheme for comprehensive human activities, and this scheme combines deep learning algorithms and MEMS (Micro-Electro-Mechanical System) sensors’ measurements. In this study, we performed four main experiments, namely pedestrian motion mode recognition, smartphone posture recognition, real-time comprehensive pedestrian activity recognition, and pedestrian navigation. In the procedure of recognition, we designed and trained deep learning models using LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network) networks based on Tensorflow framework. The accuracy of traditional ML classification methods was also used for comparison. Test results show that the accuracy of motion mode recognition was improved from 89.9 % , which was the highest accuracy and obtained by SVM (Support Vector Machine), to 90.74 % (LSTM) and 91.92 % (CNN); the accuracy of smartphone posture recognition was improved from 81.60 % , which is the highest accuracy and obtained by NN (Neural Network), to 93.69 % (LSTM) and 95.55 % (CNN). We give a model transformation procedure based on the trained CNN network model, and then obtain the converted . t f l i t e model, which can be run in Android devices for real-time recognition. Real-time recognition experiments were performed in multiple scenes, a recognition model trained by the CNN network was deployed in a Huawei Mate20 smartphone, and the five most used pedestrian activities were designed and verified. The overall accuracy was up to 89.39 % . Overall, the improvement of recognition capability based on deep learning algorithms was significant. Therefore, the solution was helpful to recognize comprehensive pedestrian activities during navigation. On the basis of the trained model, a navigation test was performed; mean bias was reduced by more than 1.1 m. Accordingly, the positioning accuracy was improved obviously, which is meaningful to apply DL in the area of pedestrian navigation to make improvements.

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JLVEA: Lightweight Real-Time Video Stream Encryption Algorithm for Internet of Things

Sensors ◽

10.3390/s20133627 ◽

2020 ◽

Vol 20 (13) ◽

pp. 3627 ◽

Cited By ~ 1

Author(s):

Junhyeok Yun ◽

Mihui Kim

Keyword(s):

Internet Of Things ◽

Packet Loss ◽

Encryption Algorithm ◽

Memory Usage ◽

Video Encryption ◽

Data Loss ◽

Iot Security ◽

Encryption Algorithms ◽

Channel Separation ◽

Color Composition

Along with the recent growth of Internet of Things (IoT) security camera market, there have been a number of personal information leakage incidents from security attacks targeting such cameras. Therefore, a permutation-based video encryption algorithm was proposed to secure video streams in low-performance processors such as IoT security cameras. However, existing permutation-based video encryption algorithms are vulnerable to known-plaintext attacks since they use the same permutation list for every frame. Moreover, situation deduction based on the color composition is possible. In this paper, we propose a new permutation-based video encryption algorithm that updates the permutation list for every frame using a crypto secure pseudo-random number generator without significantly increasing memory usage. By doing so, the algorithm becomes robust to known-plaintext attacks, which has been a common problem with existing permutation-based video encryption algorithms. In addition, color channel separation can prevent attackers from deducing situations through color composition. Pre-compression encryption is applied to make the algorithm robust to data loss because of packet loss. We implement the proposed algorithm and conduct an experiment to show its performance in terms of probability of data loss because of packet loss, encryption speed, and memory usage.

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Internet of Things (IoT) Platform for Multi-Topic Messaging

Energies ◽

10.3390/en13133346 ◽

2020 ◽

Vol 13 (13) ◽

pp. 3346

Author(s):

Mahmoud Hussein ◽

Ahmed I. Galal ◽

Emad Abd-Elrahman ◽

Mohamed Zorkany

Keyword(s):

Operating System ◽

Internet Of Things ◽

Real Time ◽

Communication Protocol ◽

Real Time Systems ◽

Real Time System ◽

Client Server ◽

Iot Applications ◽

Iot Platforms ◽

Time Systems

IoT-based applications operate in a client–server architecture, which requires a specific communication protocol. This protocol is used to establish the client–server communication model, allowing all clients of the system to perform specific tasks through internet communications. Many data communication protocols for the Internet of Things are used by IoT platforms, including message queuing telemetry transport (MQTT), advanced message queuing protocol (AMQP), MQTT for sensor networks (MQTT-SN), data distribution service (DDS), constrained application protocol (CoAP), and simple object access protocol (SOAP). These protocols only support single-topic messaging. Thus, in this work, an IoT message protocol that supports multi-topic messaging is proposed. This protocol will add a simple “brain” for IoT platforms in order to realize an intelligent IoT architecture. Moreover, it will enhance the traffic throughput by reducing the overheads of messages and the delay of multi-topic messaging. Most current IoT applications depend on real-time systems. Therefore, an RTOS (real-time operating system) as a famous OS (operating system) is used for the embedded systems to provide the constraints of real-time features, as required by these real-time systems. Using RTOS for IoT applications adds important features to the system, including reliability. Many of the undertaken research works into IoT platforms have only focused on specific applications; they did not deal with the real-time constraints under a real-time system umbrella. In this work, the design of the multi-topic IoT protocol and platform is implemented for real-time systems and also for general-purpose applications; this platform depends on the proposed multi-topic communication protocol, which is implemented here to show its functionality and effectiveness over similar protocols.

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Development of a support vector machine learning and smart phone Internet of Things-based architecture for real-time sleep apnea diagnosis

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-020-01329-1 ◽

2020 ◽

Vol 20 (S14) ◽

Author(s):

Bin Ma ◽

Zhaolong Wu ◽

Shengyu Li ◽

Ryan Benton ◽

Dongqi Li ◽

...

Keyword(s):

Support Vector Machine ◽

Sleep Apnea ◽

Internet Of Things ◽

Real Time ◽

Early Warning ◽

Smart Phone ◽

Physiological Parameters ◽

Classification Model ◽

Support Vector ◽

Obstructive Sleep

Abstract Background The breathing disorder obstructive sleep apnea syndrome (OSAS) only occurs while asleep. While polysomnography (PSG) represents the premiere standard for diagnosing OSAS, it is quite costly, complicated to use, and carries a significant delay between testing and diagnosis. Methods This work describes a novel architecture and algorithm designed to efficiently diagnose OSAS via the use of smart phones. In our algorithm, features are extracted from the data, specifically blood oxygen saturation as represented by SpO2. These features are used by a support vector machine (SVM) based strategy to create a classification model. The resultant SVM classification model can then be employed to diagnose OSAS. To allow remote diagnosis, we have combined a simple monitoring system with our algorithm. The system allows physiological data to be obtained from a smart phone, the data to be uploaded to the cloud for processing, and finally population of a diagnostic report sent back to the smart phone in real-time. Results Our initial evaluation of this algorithm utilizing actual patient data finds its sensitivity, accuracy, and specificity to be 87.6%, 90.2%, and 94.1%, respectively. Discussion Our architecture can monitor human physiological readings in real time and give early warning of abnormal physiological parameters. Moreover, after our evaluation, we find 5G technology offers higher bandwidth with lower delays ensuring more effective monitoring. In addition, we evaluate our algorithm utilizing real-world data; the proposed approach has high accuracy, sensitivity, and specific, demonstrating that our approach is very promising. Conclusions Experimental results on the apnea data in University College Dublin (UCD) Database have proven the efficiency and effectiveness of our methodology. This work is a pilot project and still under development. There is no clinical validation and no support. In addition, the Internet of Things (IoT) architecture enables real-time monitoring of human physiological parameters, combined with diagnostic algorithms to provide early warning of abnormal data.

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AES WITH CHAOTIC USING CHEBYSHEV POLYNOMIAL

Iraqi Journal for Computers and Informatics ◽

10.25195/ijci.v44i2.55 ◽

2018 ◽

Vol 44 (2) ◽

pp. 35-40

Author(s):

Tanya jabor ◽

Hiba Taresh ◽

Alaa Raheema

Keyword(s):

Real Time ◽

Personal Information ◽

Chaotic Map ◽

Encryption Algorithm ◽

The Internet ◽

Key Generation ◽

Generation Algorithm ◽

Critical Data ◽

Encryption And Decryption ◽

Day By Day

All the important information is exchanged between facilities using the internet and networks, all these data should besecret and secured probably, the personal information of person in each of these institutions day by day need to organized secretlyand the need of the cryptography systems is raised which can easily encrypt the personal and critical data and it can be shared withother centers via internet without and concerns about privacy. Chaotic performance is added to different phases of AES but very few apply it on key generation and choosing ChebyshevPolynomial will provide a chaotic map which will led to random strong key. our system based on modified advanced encryptionstandard (AES) , with encryption and decryption in real time taking to consideration the criticality of data images that beenencrypted the main encryption algorithm is the same the modification is done by replacing the key generation algorithm byChebyshev Polynomial to generate key with the required key size.

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Application of a Lightweight Encryption Algorithm to a Quantized Speech Image for Secure IoT

10.20944/preprints201802.0096.v2 ◽

2018 ◽

Author(s):

Mourad Talbi ◽

Med Salim Bouhalel

Keyword(s):

Internet Of Things ◽

Data Security ◽

Speech Signal ◽

Block Cipher ◽

Encryption Algorithm ◽

Encryption Algorithms ◽

Computationally Expensive ◽

Complex Algorithm ◽

Lightweight Encryption ◽

Image Simulations

The IoT Internet of Things being a promising technology of the future. It is expected to connect billions of devices. The increased communication number is expected to generate data mountain and the data security can be a threat. The devices in the architecture are fundamentally smaller in size and low powered. In general, classical encryption algorithms are computationally expensive and this due to their complexity and needs numerous rounds for encrypting, basically wasting the constrained energy of the gadgets. Less complex algorithm, though, may compromise the desired integrity. In this paper we apply a lightweight encryption algorithm named as Secure IoT (SIT) to a quantized speech image for Secure IoT. It is a 64-bit block cipher and requires 64-bit key to encrypt the data. This quantized speech image is constructed by first quantizing a speech signal and then splitting the quantized signal into frames. Then each of these frames is transposed for obtaining the different columns of this quantized speech image. Simulations result shows the algorithm provides substantial security in just five encryption rounds.

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Improving Fire Safety Systems Based On Internet Of Things And Deep Learning

10.32920/17315693.v1 ◽

2021 ◽

Author(s):

Mohamed Gamaleldin

Keyword(s):

Deep Learning ◽

Internet Of Things ◽

Real Time ◽

Fire Safety ◽

Ground Truth ◽

Current Status ◽

Dynamic Nature ◽

Actual Fire ◽

People Movement ◽

Safety Systems

Structure fires are one of the main concerns for fire safety systems. The actual fire safety of a building depends on not only how it is designed and constructed, but also on how it is operated. Computational fluid dynamics software is the current solution to reduce the casualties in the fire circumstances. However, it consumes hours to provide the results in some cases that makes it hard to run in real-time. It also does not accept any changes after starting the simulation, which makes it unsuitable for running in the dynamic nature of the fire. On the other hand, the current evacuation signs are fixed, which might guide occupants and firefighter to dangerous zones.<div><br><div>In this research, we present a smoke emulator that runs in real-time to reflect what is happening on the ground-truth. This system is achieved using a light-weight smoke emulator engine, deep learning, and internet of things. The IoT sensors are sending the measurements to correct the emulator from any deviation and reflect events such as fire starting, people movement, and the door’s status. This emulator helps the firefighter by providing them with a map that shows the smoke development in the building. They can take a snapshot from the current status of the building and try different virtual evacuation and firefighting plans to pick the best and safest for them to proceed. The system will also control the exit signs to have adaptive exit routes that guide occupants away from fire and smoke to minimize the exposure time to the toxic gases<br></div></div>

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A Secure and Efficient Lightweight Symmetric Encryption Scheme for Transfer of Text Files between Embedded IoT Devices

Symmetry ◽

10.3390/sym11020293 ◽

2019 ◽

Vol 11 (2) ◽

pp. 293 ◽

Cited By ~ 30

Author(s):

Sreeja Rajesh ◽

Varghese Paul ◽

Varun Menon ◽

Mohammad Khosravi

Keyword(s):

Encryption Algorithm ◽

Symmetric Encryption ◽

Embedded Devices ◽

Lower Efficiency ◽

Avalanche Effect ◽

Encryption And Decryption ◽

Encryption Algorithms ◽

Iot Devices ◽

Area Of Concern ◽

The Internet Of Things

Recent advancements in wireless technology have created an exponential rise in the number of connected devices leading to the internet of things (IoT) revolution. Large amounts of data are captured, processed and transmitted through the network by these embedded devices. Security of the transmitted data is a major area of concern in IoT networks. Numerous encryption algorithms have been proposed in these years to ensure security of transmitted data through the IoT network. Tiny encryption algorithm (TEA) is the most attractive among all, with its lower memory utilization and ease of implementation on both hardware and software scales. But one of the major issues of TEA and its numerous developed versions is the usage of the same key through all rounds of encryption, which yields a reduced security evident from the avalanche effect of the algorithm. Also, the encryption and decryption time for text is high, leading to lower efficiency in IoT networks with embedded devices. This paper proposes a novel tiny symmetric encryption algorithm (NTSA) which provides enhanced security for the transfer of text files through the IoT network by introducing additional key confusions dynamically for each round of encryption. Experiments are carried out to analyze the avalanche effect, encryption and decryption time of NTSA in an IoT network including embedded devices. The results show that the proposed NTSA algorithm is much more secure and efficient compared to state-of-the-art existing encryption algorithms.

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