Ransomware Detection techniques in the Dawn of Artificial Intelligence: A Survey

The sovereignty of Artificial Intelligence over Human Ethics and Heedfulness

Journal of University of Shanghai for Science and Technology ◽

10.51201/jusst/21/08444 ◽

2021 ◽

Vol 23 (08) ◽

pp. 657-665

Author(s):

Sunil Varma Mudundi ◽

◽

Tejaswi Pasumathy ◽

Dr. Raul Villamarin Roudriguez ◽

◽

...

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Computer Vision ◽

Critical Thinking ◽

Cognitive Abilities ◽

Machine Learning Algorithms ◽

Good Decision ◽

Deep Dive ◽

Detection Techniques ◽

Ethics And Technology

Artificial Intelligence in present days is in extreme growth. We see AI in almost every field in work today. Artificial Intelligence is being introduced in crucial roles like recruiting, Law enforcement and in the Military. To be involved in such crucial roles, it needs lots of trusts and scientific evaluation. With the evolution of artificial intelligence, automatic machines are in a speed run in this decade. Developing a machine/robot with a set of tools/programs will technically sort of some of the challenges. But the problem arises when we completely depend on robots/machines. Artificial intelligence this fast-growing technology will be very helpful when we take help from it for just primary needs like face detection, sensor-controllers, bill counters…etc. But we face real challenges when we involve with decision making, critical thinking…etc. In mere future, automated machines are going to replace many positions of humans. Many firms from small to big are opting for Autonomous means just to make their work simpler and efficient. Using a machine gives more accurate results and outputs in simulated time. As technology is developing fast, they should be developed as per societal rules and conditions. Scientists and analysts predict that singularity in AI can be achieved by 2047. Ray Kurzweil, Director of Technology at Google predicted that AI may achieve singularity in 2047. We all saw the DRDO invention on autonomous fighting drones. They operate without any human assistance. They evaluate target type, its features and eliminate them based on edge detection techniques using computer vision. AI is also into recruiting people for companies. Some companies started using AI Recruiter to evaluate the big pool of applications and select efficient ones into the industry. This is possible through computer vision and machine learning algorithms. In recent times AI is being used as a suggestion tool for judgement too. Apart from all these advancements, some malicious scenarios may affect humankind. When AI is used in the wrong way many lives will fall in danger. Collecting all good and evil from past experiences is it possible to feed a machine to work autonomously. As many philosophers and educated people kept some set of guidelines in society is it practically possible to follow when AI achieves singularity and when we talk about the neural networking of human. They have good decision-making skills, critical thinking…etc. We will briefly discuss the ethics and AI robots / Machines that involve consciousness and cognitive abilities. In this upgrading technological world, AI is ruling a maximum number of operations. So, we will discuss how can ethics be followed. How can we balance ethics and technology in both phases.We will deep dive into some of these interesting areas in this article.

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Secure Real-Time Artificial Intelligence System against Malicious QR Code Links

Security and Communication Networks ◽

10.1155/2021/5540670 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Mohammed S. Al-Zahrani ◽

Heider A. M. Wahsheh ◽

Fawaz W. Alsaade

Keyword(s):

Artificial Intelligence ◽

Private Information ◽

Code Generation ◽

Qr Code ◽

Suitable Model ◽

Decision Tree Classifier ◽

Data Types ◽

Qr Codes ◽

Detection Techniques ◽

Tree Classifier

Recently, hackers intend to reproduce malicious links utilizing several ways to mislead users. They try to control victims’ machines or get their data remotely by gaining access to private information they use via cyberspace. QR codes are two-dimensional barcodes with the capacity to encode various data types and can be viewed by digital devices, such as smartphones. However, there is no approved protocol in QR code generation; therefore, QR codes might be exposed to several questionable attacks. QR code attacks might be perpetrated using barcodes, and there are some security countermeasures. Some of these solutions are restricted to malicious link detection techniques with knowledge of cryptographic methods. Therefore, this study aims to detect malicious links embedded in 1D (linear) and 2D (QR) codes. A cybercrime attack was proposed based on barcode counterfeiting that can be used to perform online attacks. A dataset of 100000 malicious and benign URLs was created via several resources, and their lexical features were obtained. Analyses were conducted to illustrate how different features and users deal with online barcode content. Several artificial intelligence models were implemented. A decision tree classifier was identified as the most suitable model for identifying malicious URLs. Our outcomes suggested that a secure artificial intelligence barcode scanner (BarAI) is recommended to detect malicious barcode links with an accuracy of 90.243%.

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Artificial Intelligence–Based Intrusion Detection Techniques

The State of the Art in Intrusion Prevention and Detection ◽

10.1201/b16390-17 ◽

2014 ◽

pp. 303-328

Keyword(s):

Artificial Intelligence ◽

Intrusion Detection ◽

Detection Techniques

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A Comparative Study between Machine Learning Algorithm and Artificial Intelligence Neural Network in Detecting Minor Bearing Fault of Induction Motors

Energies ◽

10.3390/en12112105 ◽

2019 ◽

Vol 12 (11) ◽

pp. 2105 ◽

Cited By ~ 7

Author(s):

Shrinathan Esakimuthu Pandarakone ◽

Yukio Mizuno ◽

Hisahide Nakamura

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Fault Detection ◽

Induction Motors ◽

Learning Algorithm ◽

Detection Techniques ◽

Bearing Fault ◽

Fast Fourier Transform Analysis ◽

The Difference

Most of the mechanical systems in industries are made to run through induction motors (IM). To maintain the performance of the IM, earlier detection of minor fault and continuous monitoring (CM) are required. Among IM faults, bearing faults are considered as indispensable because of its high probability incidence nature. CM mainly depends upon signal processing and fault detection techniques. In recent decades, various methods have been involved in detecting the bearing fault using machine learning (ML) algorithms. Additionally, the role of artificial intelligence (AI), a growing technology, has also been used in fault diagnosis of IM. Taking the necessity of minor fault detection and the detailed study about the role of ML and AI to detect the bearing fault, the present study is performed. A comprehensive study is conducted by considering various diagnosis methods from ML and AI for detecting a minor bearing fault (hole and scratch). This study helps in understanding the difference between the diagnosis approach and their effectiveness in detecting an IM bearing fault. It is accomplished through FFT (fast Fourier transform) analysis of the load current and the extracted features are used to train the algorithm. The application is extended by comparing the result of ML and AI, and then explaining the specific purpose of use.

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Myocardial infarction detection using artificial intelligence and nonlinear features based on synthesis of the standard 12-lead and Frank XYZ leads

10.21203/rs.3.rs-56003/v1 ◽

2020 ◽

Author(s):

Wei Zeng ◽

Zixiang Lin ◽

Chengzhi Yuan

Keyword(s):

Artificial Intelligence ◽

Myocardial Infarction ◽

Phase Space ◽

System Dynamics ◽

Automatic Detection ◽

Discrete Wavelet ◽

Potential Candidate ◽

Detection Techniques ◽

Cardiac System ◽

Cardiac Vector

Abstract Nowadays cardiovascular diseases ( CVD ) is one of the prime causes of human mortality, which has received tremendous and elaborative research interests regarding the prevention of CVD . Myocardial ischemia is a kind of CVD which will lead to myocardial infarction (MI). The diagnostic criterion of MI is supplemented with clinical judgment and several electrocardiographic (ECG) or vectorcardiographic ( VCG ) programs. However the visual inspection of ECG or VCG signals by cardiologists is tedious, laborious and subjective. To overcome such disadvantages, numerous MI detection techniques including signal processing and artificial intelligence tools have been developed. In this study we propose a novel technique for automatic detection of MI based on disparity of cardiac system dynamics and synthesis of the standard 12-lead and Frank XYZ leads. First, 12-lead ECG signals are reduced to 3-dimensional VCG signals, which are synthesized with Frank XYZ leads to build a hybrid 4-dimensional cardiac vector. This vector is decomposed into a series of proper rotation components ( PRCs ) by using the intrinsic time-scale decomposition ( ITD ) method. Second, four levels discrete wavelet transform ( DWT ) is employed to decompose the predominant PRCs into different frequency bands, in which third-order Daubechies ( db3 ) wavelet function is selected as reference variable for analysis. Third, phase space of the reference variable is reconstructed based on db3 , in which the properties associated with the nonlinear cardiac system dynamics are preserved. Three-dimensional ( 3D ) phase space reconstruction ( PSR ) together with Euclidean distance (ED) has been utilized to derive features. Fourth, neural networks are then used to model, identify and classify cardiac system dynamics between normal (healthy) and MI cardiac vector signals. Finally, experiments are carried out on the PhysioNet PTB database to assess the effectiveness of the proposed method, in which conventional 12-lead and Frank XYZ leads ECG signal fragments from 148 patients with MI and 52 healthy controls were extracted. By using the 10-fold cross-validation style, the achieved average classification accuracy is reported to be 98.20 % . The result verifies the effectiveness of the proposed method which can serve as a potential candidate for the automatic detection of MI in the clinical application.

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Study protocol for a two-site clinical trial to validate a smartphone-based artificial intelligence classifier identifying cervical precancer and cancer in HPV-positive women in Cameroon

PLoS ONE ◽

10.1371/journal.pone.0260776 ◽

2021 ◽

Vol 16 (12) ◽

pp. e0260776

Author(s):

Inès Baleydier ◽

Pierre Vassilakos ◽

Roser Viñals ◽

Ania Wisniak ◽

Bruno Kenfack ◽

...

Keyword(s):

Artificial Intelligence ◽

Cervical Cancer ◽

Acetic Acid ◽

Cancer Screening ◽

Cervical Cancer Screening ◽

Study Protocol ◽

Screening Program ◽

Histopathological Examination ◽

Current Standard ◽

Detection Techniques

Introduction Cervical cancer remains a major public health challenge in low- and middle-income countries (LMICs) due to financial and logistical issues. WHO recommendation for cervical cancer screening in LMICs includes HPV testing as primary screening followed by visual inspection with acetic acid (VIA) and treatment. However, VIA is a subjective procedure dependent on the healthcare provider’s experience. Its accuracy can be improved by computer-aided detection techniques. Our aim is to assess the performance of a smartphone-based Automated VIA Classifier (AVC) relying on Artificial Intelligence to discriminate precancerous and cancerous lesions from normal cervical tissue. Methods The AVC study will be nested in an ongoing cervical cancer screening program called “3T-study” (for Test, Triage and Treat), including HPV self-sampling followed by VIA triage and treatment if needed. After application of acetic acid on the cervix, precancerous and cancerous cells whiten more rapidly than non-cancerous ones and their whiteness persists stronger overtime. The AVC relies on this key feature to determine whether the cervix is suspect for precancer or cancer. In order to train and validate the AVC, 6000 women aged 30 to 49 years meeting the inclusion criteria will be recruited on a voluntary basis, with an estimated 100 CIN2+, calculated using a confidence level of 95% and an estimated sensitivity of 90% +/-7% precision on either side. Diagnostic test performance of AVC test and two current standard tests (VIA and cytology) used routinely for triage will be evaluated and compared. Histopathological examination will serve as reference standard. Participants’ and providers’ acceptability of the technology will also be assessed. The study protocol was registered under ClinicalTrials.gov (number NCT04859530). Expected results The study will determine whether AVC test can be an effective method for cervical cancer screening in LMICs.

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