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

2022 ◽  
Vol 25 (3) ◽  
pp. 28-33
Author(s):  
Francesco Restuccia ◽  
Tommaso Melodia

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.

2021 ◽  
Author(s):  
Mohamed Gamaleldin

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>


2021 ◽  
Author(s):  
Mohammed Y. Alzahrani ◽  
Alwi M Bamhdi

Abstract In recent years, the use of the internet of things (IoT) has increased dramatically, and cybersecurity concerns have grown in tandem. Cybersecurity has become a major challenge for institutions and companies of all sizes, with the spread of threats growing in number and developing at a rapid pace. Artificial intelligence (AI) in cybersecurity can to a large extent help face the challenge, since it provides a powerful framework and coordinates that allow organisations to stay one step ahead of sophisticated cyber threats. AI provides real-time feedback, helping rollover daily alerts to be investigated and analysed, effective decisions to be made and enabling quick responses. AI-based capabilities make attack detection, security and mitigation more accurate for intelligence gathering and analysis, and they enable proactive protective countermeasures to be taken to overwhelm attacks. In this study, we propose a robust system specifically to help detect botnet attacks of IoT devices. This was done by innovatively combining the model of a convolutional neural network with a long short-term memory algorithm mechanism to detect two common and serious IoT attacks (BASHLITE and Mirai) on four types of security camera. The data sets, which contained normal malicious network packets, were collected from real-time lab-connected camera devices in IoT environments. The results of the experiment showed that the proposed system achieved optimal performance, according to evaluation metrics. The proposed system gave the following weighted average results for detecting the botnet on the Provision PT-737E camera: camera precision: 88%, recall: 87% and F1 score: 83%. The results of system for classifying botnet attacks and normal packets on the Provision PT-838 camera were 89% for recall, 85% for F1 score and 94%, precision. The intelligent security system using the advanced deep learning model was successful for detecting botnet attacks that infected camera devices connected to IoT applications.


2021 ◽  
Author(s):  
Mohamed Gamaleldin

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>


Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Mohammad Kamrul Hasan ◽  
Muhammad Shafiq ◽  
Shayla Islam ◽  
Bishwajeet Pandey ◽  
Yousef A. Baker El-Ebiary ◽  
...  

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.


2020 ◽  
Vol 17 (1) ◽  
pp. 68-73
Author(s):  
M. Hemaanand ◽  
V. Sanjay Kumar ◽  
R. Karthika

With the evolution of technology ensuring people for their safety and security all around the time constantly is a big challenge. We propose an advanced technique based on deep learning and artificial intelligence platform that can monitor the people, their homes and their surroundings providing them a quantifiable increase in security. We have surveillance cameras in our homes for video capture as well as security purposes. Our proposed technique is to detect and classify as well as inform the user if there is any breach in security of the classified object using the cameras by implementing deep learning techniques and the technology of internet of things. It can serve as a perimeter monitoring and intruder alert system in smart surveillance environment. This paper provides a well-defined structure for live stream data analysis. It overcomes the challenge of static closed circuit cameras television as it serves as a motion based tracking system and monitors events in real time to ensure activities are limited to specific persons within authorized areas. It has the advantage of creating multiple bounding boxes to track down the objects which could be any living or non-living thing based on the trained modules. The trespasser or intruder can be efficiently detected using the CCTV camera surveillance which is being supported by the real-time object classifier algorithm at the intermediate module. The proposed method is mainly supported by the real time object detection and classification which is implemented using Mobile Net and Single shot detector.


Electronics ◽  
2021 ◽  
Vol 10 (20) ◽  
pp. 2497
Author(s):  
Mihai Andronie ◽  
George Lăzăroiu ◽  
Mariana Iatagan ◽  
Cristian Uță ◽  
Roxana Ștefănescu ◽  
...  

With growing evidence of deep learning-assisted smart process planning, there is an essential demand for comprehending whether cyber-physical production systems (CPPSs) are adequate in managing complexity and flexibility, configuring the smart factory. In this research, prior findings were cumulated indicating that the interoperability between Internet of Things-based real-time production logistics and cyber-physical process monitoring systems can decide upon the progression of operations advancing a system to the intended state in CPPSs. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout March and August 2021, with search terms including “cyber-physical production systems”, “cyber-physical manufacturing systems”, “smart process manufacturing”, “smart industrial manufacturing processes”, “networked manufacturing systems”, “industrial cyber-physical systems,” “smart industrial production processes”, and “sustainable Internet of Things-based manufacturing systems”. As we analyzed research published between 2017 and 2021, only 489 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 164, chiefly empirical, sources. Subsequent analyses should develop on real-time sensor networks, so as to configure the importance of artificial intelligence-driven big data analytics by use of cyber-physical production networks.


2021 ◽  
Vol 2 (4) ◽  
pp. 236-245
Author(s):  
Joy Iong Zong Chen ◽  
Kong-Long Lai

In the history of device computing, Internet of Things (IoT) is one of the fastest growing field that facing many security challenges. The effective efforts should have been made to address the security and privacy issues in IoT networks. The IoT devices are basically resource control device which provide routine attract impression for cyber attackers. The IoT participation nodes are increasing rapidly with more resource constrained that creating more challenging conditions in the real time. The existing methods provide an ineffective response to the tasks for effective IoT device. Also, it is an insufficient to involve the complete security and safety spectrum of the IoT networks. Because of the existing algorithms are not enriched to secure IoT bionetwork in the real time environment. The existing system is not enough to detect the proxy to the authorized person in the embedding devices. Also, those methods are believed in single model domain. Therefore, the effectiveness is dropping for further multimodal domain such as combination of behavioral and physiological features. The embedding intelligent technique will be securitizing for the IoT devices and networks by deep learning (DL) techniques. The DL method is addressing different security and safety problems arise in real time environment. This paper is highlighting hybrid DL techniques with Reinforcement Learning (RL) for the better performance during attack and compared with existing one. Also, here we discussed about DL combined with RL of several techniques and identify the higher accuracy algorithm for security solutions. Finally, we discuss the future direction of decision making of DL based IoT security system.


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