Log in

Relevant bibliographies by topics / Prediction of attacks in wild / Journal articles

To see the other types of publications on this topic, follow the link: Prediction of attacks in wild.

Journal articles on the topic 'Prediction of attacks in wild'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Prediction of attacks in wild.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Bilen, Abdulkadir, and Ahmet Bedri Özer. "Cyber-attack method and perpetrator prediction using machine learning algorithms." PeerJ Computer Science 7 (April 9, 2021): e475. http://dx.doi.org/10.7717/peerj-cs.475.

Full text

Abstract:

Cyber-attacks have become one of the biggest problems of the world. They cause serious financial damages to countries and people every day. The increase in cyber-attacks also brings along cyber-crime. The key factors in the fight against crime and criminals are identifying the perpetrators of cyber-crime and understanding the methods of attack. Detecting and avoiding cyber-attacks are difficult tasks. However, researchers have recently been solving these problems by developing security models and making predictions through artificial intelligence methods. A high number of methods of crime pred

APA, Harvard, Vancouver, ISO, and other styles

2

Siswanto, Joko, Irwan Sembiring, Adi Setiawan, and Iwan Setyawan. "Number of Cyber Attacks Predicted With Deep Learning Based LSTM Model." JUITA : Jurnal Informatika 12, no. 1 (2024): 39. http://dx.doi.org/10.30595/juita.v12i1.20210.

Full text

Abstract:

The increasing number of cyber attacks will result in various damages to the functioning of technological infrastructure. A prediction model for the number of cyber attacks based on the type of attack, handling actions and severity using time-series data has never been done. A deep learning-based LSTM prediction model is proposed to predict the number of cyberattacks in a time series on 3 evaluated data sets MSLE, MSE, MAE, RMSE, and MAPE, and displays the predicted relationships between prediction variables. Cyber attack dataset obtained from kaggle.com. The best prediction model is epoch 20,

APA, Harvard, Vancouver, ISO, and other styles

3

Zuzcák, Matej, and Petr Bujok. "Using honeynet data and a time series to predict the number of cyber attacks." Computer Science and Information Systems, no. 00 (2021): 40. http://dx.doi.org/10.2298/csis200715040z.

Full text

Abstract:

A large number of cyber attacks are commonly conducted against home computers, mobile devices, as well as servers providing various services. One such prominently attacked service, or a protocol in this case, is the Secure Shell (SSH) used to gain remote access to manage systems. Besides hu man attackers, botnets are a major source of attacks on SSH servers. Tools such as honeypots allow an effective means of recording and analysing such attacks. However, is it also possible to use them to effectively predict these attacks? The prediction of SSH attacks, specifically the prediction of activity

APA, Harvard, Vancouver, ISO, and other styles

4

Kadiri, Padmaja, and Seshadri Ravala. "Kernel-Based Machine Learning Models to Predict Mitigation Time During Cloud Security Attacks." International Journal of e-Collaboration 17, no. 4 (2021): 75–88. http://dx.doi.org/10.4018/ijec.2021100106.

Full text

Abstract:

Security threats are unforeseen attacks to the services provided by the cloud service provider. Depending on the type of attack, the cloud service and its associated features will be unavailable. The mitigation time is an integral part of attack recovery. This research paper explores the different parameters that will aid in predicting the mitigation time after an attack on cloud services. Further, the paper presents machine learning models that can predict the mitigation time. The paper presents the kernel-based machine learning models that can predict the average mitigation time during secur

APA, Harvard, Vancouver, ISO, and other styles

5

Li, Zhong, Xianke Wu, and Changjun Jiang. "Efficient poisoning attacks and defenses for unlabeled data in DDoS prediction of intelligent transportation systems." Security and Safety 1 (2022): 2022003. http://dx.doi.org/10.1051/sands/2022003.

Full text

Abstract:

Nowadays, large numbers of smart sensors (e.g., road-side cameras) which communicate with nearby base stations could launch distributed denial of services (DDoS) attack storms in intelligent transportation systems. DDoS attacks disable the services provided by base stations. Thus in this paper, considering the uneven communication traffic flows and privacy preserving, we give a hidden Markov model-based prediction model by utilizing the multi-step characteristic of DDoS with a federated learning framework to predict whether DDoS attacks will happen on base stations in the future. However, in t

APA, Harvard, Vancouver, ISO, and other styles

6

Ruda, Aleš, Jaromír Kolejka, and Thakur Silwal. "GIS-Assisted Prediction and Risk Zonation of Wildlife Attacks in the Chitwan National Park in Nepal." ISPRS International Journal of Geo-Information 7, no. 9 (2018): 369. http://dx.doi.org/10.3390/ijgi7090369.

Full text

Abstract:

Population growth forces the human community to expand into the natural habitats of wild animals. Their efforts to use natural sources often collide with wildlife attacks. These animals do not only protect their natural environment, but in the face of losing the potential food sources, they also penetrate in human settlements. The research was situated in the Chitwan National Park (CNP) in Nepal, and the aim of this study was to investigate possible geospatial connections between attacks of all kinds of animals on humans in the CNP and its surroundings between 2003 and 2013. The patterns of at

APA, Harvard, Vancouver, ISO, and other styles

7

Yuan, Guotao, Hong Huang, and Xin Li. "Self-supervised learning backdoor defense mixed with self-attention mechanism." Journal of Computing and Electronic Information Management 12, no. 2 (2024): 81–88. http://dx.doi.org/10.54097/7hx9afkw.

Full text

Abstract:

Recent studies have shown that Deep Neural Networks (DNNs) are vulnerable to backdoor attacks, where attackers embed hidden backdoors into the DNN models by poisoning a small number of training samples. The attacked models perform normally on benign samples, but when the backdoor is activated, their prediction results will be maliciously altered. To address the issues of suboptimal backdoor defense effectiveness and limited generality, a hybrid self-attention mechanism-based self-supervised learning method for backdoor defense is proposed. This method defends against backdoor attacks by levera

APA, Harvard, Vancouver, ISO, and other styles

8

Hairab, Belal Ibrahim, Heba K. Aslan, Mahmoud Said Elsayed, Anca D. Jurcut, and Marianne A. Azer. "Anomaly Detection of Zero-Day Attacks Based on CNN and Regularization Techniques." Electronics 12, no. 3 (2023): 573. http://dx.doi.org/10.3390/electronics12030573.

Full text

Abstract:

The rapid development of cyberattacks in the field of the Internet of things (IoT) introduces new security challenges regarding zero-day attacks. Intrusion-detection systems (IDS) are usually trained on specific attacks to protect the IoT application, but the attacks that are yet unknown for IDS (i.e., zero-day attacks) still represent challenges and concerns regarding users’ data privacy and security in those applications. Anomaly-detection methods usually depend on machine learning (ML)-based methods. Under the ML umbrella are classical ML-based methods, which are known to have low predictio

APA, Harvard, Vancouver, ISO, and other styles

9

Tibble, Holly, Athanasios Tsanas, Elsie Horne, et al. "Predicting asthma attacks in primary care: protocol for developing a machine learning-based prediction model." BMJ Open 9, no. 7 (2019): e028375. http://dx.doi.org/10.1136/bmjopen-2018-028375.

Full text

Abstract:

IntroductionAsthma is a long-term condition with rapid onset worsening of symptoms (‘attacks’) which can be unpredictable and may prove fatal. Models predicting asthma attacks require high sensitivity to minimise mortality risk, and high specificity to avoid unnecessary prescribing of preventative medications that carry an associated risk of adverse events. We aim to create a risk score to predict asthma attacks in primary care using a statistical learning approach trained on routinely collected electronic health record data.Methods and analysisWe will employ machine-learning classifiers (naïv

APA, Harvard, Vancouver, ISO, and other styles

10

Gazzan, Mazen, and Frederick T. Sheldon. "Opportunities for Early Detection and Prediction of Ransomware Attacks against Industrial Control Systems." Future Internet 15, no. 4 (2023): 144. http://dx.doi.org/10.3390/fi15040144.

Full text

Abstract:

Industrial control systems (ICS) and supervisory control and data acquisition (SCADA) systems, which control critical infrastructure such as power plants and water treatment facilities, have unique characteristics that make them vulnerable to ransomware attacks. These systems are often outdated and run on proprietary software, making them difficult to protect with traditional cybersecurity measures. The limited visibility into these systems and the lack of effective threat intelligence pose significant challenges to the early detection and prediction of ransomware attacks. Ransomware attacks o

APA, Harvard, Vancouver, ISO, and other styles

11

He, Yuxiang, Baisong Yang, and Chiawei Chu. "GA-CatBoost-Weight Algorithm for Predicting Casualties in Terrorist Attacks: Addressing Data Imbalance and Enhancing Performance." Mathematics 12, no. 6 (2024): 818. http://dx.doi.org/10.3390/math12060818.

Full text

Abstract:

Terrorism poses a significant threat to international peace and stability. The ability to predict potential casualties resulting from terrorist attacks, based on specific attack characteristics, is vital for protecting the safety of innocent civilians. However, conventional data sampling methods struggle to effectively address the challenge of data imbalance in textual features. To tackle this issue, we introduce a novel algorithm, GA-CatBoost-Weight, designed for predicting whether terrorist attacks will lead to casualties among innocent civilians. Our approach begins with feature selection u

APA, Harvard, Vancouver, ISO, and other styles

12

Tasneem, Sumaiya, and Kazi Aminul Islam. "Improve Adversarial Robustness of AI Models in Remote Sensing via Data-Augmentation and Explainable-AI Methods." Remote Sensing 16, no. 17 (2024): 3210. http://dx.doi.org/10.3390/rs16173210.

Full text

Abstract:

Artificial intelligence (AI) has made remarkable progress in recent years in remote sensing applications, including environmental monitoring, crisis management, city planning, and agriculture. However, the critical challenge in utilizing AI models in real-world remote sensing applications is maintaining their robustness and reliability, particularly against adversarial attacks. In adversarial attacks, attackers manipulate benign data to create a perturbation to mislead AI models into predicting incorrect decisions, posing a catastrophic threat to the security of their applications, particularl

APA, Harvard, Vancouver, ISO, and other styles

13

Xiang, Yingxiao, Wenjia Niu, Endong Tong, et al. "Congestion Attack Detection in Intelligent Traffic Signal System: Combining Empirical and Analytical Methods." Security and Communication Networks 2021 (October 31, 2021): 1–17. http://dx.doi.org/10.1155/2021/1632825.

Full text

Abstract:

The intelligent traffic signal (I-SIG) system aims to perform automatic and optimal signal control based on traffic situation awareness by leveraging connected vehicle (CV) technology. However, the current signal control algorithm is highly vulnerable to CV data spoofing attacks. These vulnerabilities can be exploited to create congestion in an intersection and even trigger a cascade failure in the traffic network. To avoid this issue, timely and accurate congestion attack detection and identification are essential. This work proposes a congestion attack detection approach by combining empiric

APA, Harvard, Vancouver, ISO, and other styles

14

Phillips, Peter J., and Gabriela Pohl. "The Deferral of Attacks: SP/A Theory as a Model of Terrorist Choice when Losses Are Inevitable." Open Economics 1, no. 1 (2018): 71–85. http://dx.doi.org/10.1515/openec-2018-0001.

Full text

Abstract:

Abstract When a terrorist group’s aspirations far exceed the outcomes that can be expected to result from any of the available attack methods, an outcome below the terrorist group’s aspiration level is inevitable. A primary prediction of SP/A theory when applied to the study of terrorist behaviour is that when losses are inevitable the terrorist group will be risk averse and inclined to defer further action until expected outcomes improve, new attack method innovations are developed or the memory of the event that shaped aspirations has faded sufficiently that the aspiration level can be ‘rese

APA, Harvard, Vancouver, ISO, and other styles

15

Chan, Amy Hai Yan, Braden Te Ao, Christina Baggott, et al. "DIGIPREDICT: physiological, behavioural and environmental predictors of asthma attacks—a prospective observational study using digital markers and artificial intelligence—study protocol." BMJ Open Respiratory Research 11, no. 1 (2024): e002275. http://dx.doi.org/10.1136/bmjresp-2023-002275.

Full text

Abstract:

IntroductionAsthma attacks are a leading cause of morbidity and mortality but are preventable in most if detected and treated promptly. However, the changes that occur physiologically and behaviourally in the days and weeks preceding an attack are not always recognised, highlighting a potential role for technology. The aim of this study ‘DIGIPREDICT’ is to identify early digital markers of asthma attacks using sensors embedded in smart devices including watches and inhalers, and leverage health and environmental datasets and artificial intelligence, to develop a risk prediction model to provid

APA, Harvard, Vancouver, ISO, and other styles

16

Tudu, Debananda, and Bana Bihari Mishra. "Prediction of difficult cholecystectomy, a study of 100 cases." International Journal of Research in Medical Sciences 7, no. 1 (2018): 63. http://dx.doi.org/10.18203/2320-6012.ijrms20185364.

Full text

Abstract:

Background: Cholelithiasis is a common problem in day to day surgical practice, which has a prevalence of 10-15%. The prevalence is more here in this part of the country as this is a pocket of sickle cell disease region. Laparoscopic cholecystectomy is the gold standard procedure for gall stone diseases. Out of many complications one of the most important complications of laparoscopic cholecystectomy is bile duct injury particularly in difficult cases. Difficulties arise during creation of pneumoperitonium, releasing adhesion, identifying anatomy, anatomical variations and during extraction of

APA, Harvard, Vancouver, ISO, and other styles

17

Kosmacheva, I. M., N. V. Davidyuk, SV Belov, et al. "Predicting of cyber attacks on critical information infrastructure." Journal of Physics: Conference Series 2091, no. 1 (2021): 012062. http://dx.doi.org/10.1088/1742-6596/2091/1/012062.

Full text

Abstract:

Abstract According to modern statistics and analytical reviews, targeted computer attacks (cyber attacks) are becoming more and more numerous. Attackers began to use non-standard schemes for implementing attacks, using employees of organizations as intermediaries, which reduces the efficiency of detecting violations. At the same time, the targets of attackers are increasingly critical information infrastructure (CII) objects. The number of cyberattacks on the critical infrastructure of the Russian Federation increased by 150%. Successful attacks on CII are associated with a lack of software up

APA, Harvard, Vancouver, ISO, and other styles

18

Akinyelu, Andronicus A., and Aderemi O. Adewumi. "Classification of Phishing Email Using Random Forest Machine Learning Technique." Journal of Applied Mathematics 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/425731.

Full text

Abstract:

Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishing attacks, billions of dollars have been lost by many companies and individuals. In 2012, an online report put the loss due to phishing attack at about $1.5 billion. This global impact of phishing attacks will continue to be on the increase and thus requires more efficient phishing detection techniques to curb the menace. This paper investigates and reports the use of random forest machine learning algorithm in classification of phishing attacks, with the major objective of developing an improved p

APA, Harvard, Vancouver, ISO, and other styles

19

Kumar, Prashant, Chitra Kushwaha, Dimple Sethi, Debjani Ghosh, Punit Gupta, and Ankit Vidyarthi. "Investigating the performance of multivariate LSTM models to predict the occurrence of Distributed Denial of Service (DDoS) attack." PLOS ONE 20, no. 1 (2025): e0313930. https://doi.org/10.1371/journal.pone.0313930.

Full text

Abstract:

In the current cybersecurity landscape, Distributed Denial of Service (DDoS) attacks have become a prevalent form of cybercrime. These attacks are relatively easy to execute but can cause significant disruption and damage to targeted systems and networks. Generally, attackers perform it to make reprisal but sometimes this issue can be authentic also. In this paper basically conversed about some deep learning models that will hand over a descent accuracy in prediction of DDoS attacks. This study evaluates various models, including Vanilla LSTM, Stacked LSTM, Deep Neural Networks (DNN), and othe

APA, Harvard, Vancouver, ISO, and other styles

20

Qin, Shanshan, Chuyu Chen, and Ming Zhang. "Modeling of Concrete Deterioration under External Sulfate Attack and Drying–Wetting Cycles: A Review." Materials 17, no. 13 (2024): 3334. http://dx.doi.org/10.3390/ma17133334.

Full text

Abstract:

This paper comprehensively summarizes moisture transport, ion transport, and mechanical damage models applied to concrete under sulfate attack and drying–wetting cycles. It highlights the essential aspects and principles of each model, emphasizing their significance in understanding the movement of moisture and ions, as well as the resulting mechanical damage within the concrete during these degradation processes. The paper critically analyzes the assumptions made in each model, shedding light on their limitations and implications for prediction accuracy. Two primary challenges faced by curren

APA, Harvard, Vancouver, ISO, and other styles

21

Atchuta, Ramya, Praturi Akshara Sahithi Sruthi, Dharani Neelam, Pati Janardhan Babu, Putchakayala Sankeerth, and Pappala Vijay Kumar. "Creating Alert Messages Based on Wild Animal Activity Detection Using Hybrid Deep Neural Networks." International Journal of All Research Education and Scientific Methods 13, no. 02 (2025): 1573–82. https://doi.org/10.56025/ijaresm.2025.1302251573.

Full text

Abstract:

The provided study addresses rural communities and ranger service employees who face the serious concern of animal attacks using a successful observation approach. The Hybrid model proposed is “Visual Geometry Group (VGG) – 19 combined with Bidirectional Long Short Term Memory (Bi-LSTM)” and is designed to identify animal species, monitor their movements, and send alerts for health warnings in forested areas where human beings are at risk. The model using VGG-19 for feature extraction and Bi-LSTM for sentence level classification achieves animal and their movement pattern identification with a

APA, Harvard, Vancouver, ISO, and other styles

22

Bonde, Lossan, and Severin Dembele. "High Accuracy Location Information Extraction From Social Network Texts Using Natural Language Processing." International Journal on Natural Language Computing 12, no. 4 (2023): 01–12. http://dx.doi.org/10.5121/ijnlc.2023.12401.

Full text

Abstract:

Terrorism has become a worldwide plague with severe consequences for the development of nations. Besides killing innocent people daily and preventing educational activities from taking place, terrorism is also hindering economic growth. Machine Learning (ML) and Natural Language Processing (NLP) can contribute to fighting terrorism by predicting in real-time future terrorist attacks if accurate data is available. This paper is part of a research project that uses text from social networks to extract necessary information to build an adequate dataset for terrorist attack prediction. We collecte

APA, Harvard, Vancouver, ISO, and other styles

23

Lossan, Bonde, and Dembele Severin. "High Accuracy Location Information Extraction From Social Network Texts Using Natural Language Processing." International Journal on Natural Language Computing (IJNLC) 12, no. 4 (2023): 12. https://doi.org/10.5281/zenodo.8327988.

Full text

Abstract:

Terrorism has become a worldwide plague with severe consequences for the development of nations. Besides killing innocent people daily and preventing educational activities from taking place, terrorism is also hindering economic growth. Machine Learning (ML) and Natural Language Processing (NLP) can contribute to fighting terrorism by predicting in real-time future terrorist attacks if accurate data is available. This paper is part of a research project that uses text from social networks to extract necessary information to build an adequate dataset for terrorist attack prediction. We collecte

APA, Harvard, Vancouver, ISO, and other styles

24

Parikshit N. Mahalle,Gitanjali Rahul Shinde,Nilesh P. Sable, Sayali Renuse,. "A Hybrid Perspective on Threat Analysis and Activity-Based Attack Modeling for Strengthening Access Control in IoT." Journal of Electrical Systems 20, no. 1s (2024): 366–78. http://dx.doi.org/10.52783/jes.777.

Full text

Abstract:

The rapid expansion of Internet of Things (IoT) devices has resulted in an unparalleled surge in the production of data and interconnectivity. Nevertheless, as IoT ecosystems become increasingly intricate, security concerns become of utmost importance, particularly in access control systems. The objective of this research is to improve the security of IoT access control by utilizing a hybrid model for analyzing threats and modeling attacks based on activities. This study has two primary objectives: a) A hybrid classification model is used to predict labels (attack or not) in binary classificat

APA, Harvard, Vancouver, ISO, and other styles

25

Altheyabi, Jassir Adel. "Cyber Attacks Visualization and Prediction in Complex Multi-Stage Network." Academic Journal of Research and Scientific Publishing 3, no. 33 (2022): 59–85. http://dx.doi.org/10.52132/ajrsp.e.2022.33.3.

Full text

Abstract:

In network security, various protocols exist, but these cannot be said to be secure. Moreover, is not easy to train the end-users, and this process is time-consuming as well. It can be said this way, that it takes much time for an individual to become a good cybersecurity professional. Many hackers and illegal agents try to take advantage of the vulnerabilities through various incremental penetrations that can compromise the critical systems. The conventional tools available for this purpose are not enough to handle things as desired. Risks are always present, and with dynamically evolving net

APA, Harvard, Vancouver, ISO, and other styles

26

Dr., B. Premamayudu, Muralikrishna K., and Pramodh K. "Diabetes Prediction Using Machine Learning KNN -Algorithm Technique." International Journal of Innovative Science and Research Technology 7, no. 5 (2022): 941–44. https://doi.org/10.5281/zenodo.6642320.

Full text

Abstract:

Diabetes is a chronic disease caused due to high amount of glucose present in the human body. If this diabetes is ignored, this may lead to severe health problems such as kidney failure, heart attacks, blood pressure, eye damage, weight loss, frequent urination, etc. Basically, human body contains Insulin which is produced by pancreas. This insulin helps to enter glucose in to blood cells in order to generate energy to the body. There are types in diabetes Type1 and Type 2 other form is gestational diabetes which is caused during pregnancy. This can be controlled in the earlier stages of the a

APA, Harvard, Vancouver, ISO, and other styles

27

Beri, Shivani. "Automated Detection of Heart Attacks: A CNN-Based Approach." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (2023): 1567–70. http://dx.doi.org/10.22214/ijraset.2023.57684.

Full text

Abstract:

Abstract: Seventeen million people die because of the world’s cardiovascular diseases annually that make up to 32% of the global population. One fourth (25%) of global deaths and 17 million each year, representing almost a third (32%), are attributable to cardiovascular disease or the world heart attack syndrome. In addition, cardiovascular disease kills an amazing 32% of overall deaths on the globe per quarter of percentage to be exact. In the short term, heart disease’s mortality rate could decline by predicting it. It has demonstrated that convolutional neural network is the best approach w

APA, Harvard, Vancouver, ISO, and other styles

28

Yi, Zhuo, Shi Jun Yao, and Liang Wang. "Researches on Brittle Seam Mining Based Situation Assessment and Prediction Mechanism of DDoS Attacks in Cloud Computing Platform." Applied Mechanics and Materials 519-520 (February 2014): 262–70. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.262.

Full text

Abstract:

According to the problem of Situation Assessment and Prediction of DDoS Attacks in Cloud Computing Platform, the concepts of Brittle Point and Brittle seam were introduced to describe the situation of an eco-system according to ranked node availability, and a Situation Assessment and Prediction Mechanism based on the Brittle Seam Mining Algorithm was proposed. In the Brittle Seam Mining Algorithm, biological features of DDoS attacks and cloud computing platform were analyzed from the point of bio-dynamics, and the analysis results indicate that bandwidth loads of the normal user access from di

APA, Harvard, Vancouver, ISO, and other styles

29

Arivunambi, Amirthasaravanan, and Arjun Paramarthalingam. "A Study on Two-Phase Monitoring Server for Ransomware Evaluation and Detection in IoT Environment." Journal of Trends in Computer Science and Smart Technology 4, no. 2 (2022): 72–82. http://dx.doi.org/10.36548/jtcsst.2022.2.003.

Full text

Abstract:

Current trending- Internet of things (IoT) is internetworking of an assortment of hardware devices to offer a collection of applications and services. In the present-day world, ransomware cyber-attack has become one of the major attacks in IoT systems. Ransomware is a hazardous malware that targets the user’s computer inaccessible or inoperative, and then requesting the computer victim user to transfer a huge ransom to relapse the damage. At instance, the evolution rate outcomes illustrate that the level of attacks such as Locky and Cryptowall ransomware are conspicuously growing then other ra

APA, Harvard, Vancouver, ISO, and other styles

30

Lipsa, Swati, Ranjan Kumar Dash, and Korhan Cengiz. "Mitigating Security Threats for Digital Twin Platform: A Systematic Review with Future Scope and Research Challenges." International Journal of Electronics and Communications Systems 4, no. 1 (2024): 1. http://dx.doi.org/10.24042/ijecs.v4i1.22279.

Full text

Abstract:

In Industry 4.0, the digital twin (DT) enables users to simulate future states and configurations for prediction, optimization, and estimation. Although the potential of digital twin technology has been demonstrated by its proliferation in traditional industrial sectors, including construction, manufacturing, transportation, supply chain, healthcare, and agriculture, the risks involved with their integration have frequently been overlooked. Moreover, as a digital approach, it is intuitive to believe it is susceptible to adversarial attacks. This issue necessitates research into the multitude o

APA, Harvard, Vancouver, ISO, and other styles

31

Nasrabadi, Abbas, and Javad Haddadnia. "Predicting Heart Attacks in Patients Using Artificial Intelligence Methods." Modern Applied Science 10, no. 3 (2016): 66. http://dx.doi.org/10.5539/mas.v10n3p66.

Full text

Abstract:

<p class="zhengwen">Today the heart disease is one of the most important causes of death in the world. So its early prediction and diagnosis is important in medical field, which could help in on time treatment, decreasing health costs and decreasing death caused by it. In fact the main goal of using data mining algorithms in medicine by using patients’ data is better utilizing the database and discovering tacit knowledge to help doctors in better decision making.</p><p class="zhengwen">Therefore using data mining and discovering knowledge in cardiovascular centers could creat

APA, Harvard, Vancouver, ISO, and other styles

32

Tsai, Min-Jen, Ping-Yi Lin, and Ming-En Lee. "Adversarial Attacks on Medical Image Classification." Cancers 15, no. 17 (2023): 4228. http://dx.doi.org/10.3390/cancers15174228.

Full text

Abstract:

Due to the growing number of medical images being produced by diverse radiological imaging techniques, radiography examinations with computer-aided diagnoses could greatly assist clinical applications. However, an imaging facility with just a one-pixel inaccuracy will lead to the inaccurate prediction of medical images. Misclassification may lead to the wrong clinical decision. This scenario is similar to the adversarial attacks on deep learning models. Therefore, one-pixel and multi-pixel level attacks on a Deep Neural Network (DNN) model trained on various medical image datasets are investig

APA, Harvard, Vancouver, ISO, and other styles

33

Krieg, Steven J., Christian W. Smith, Rusha Chatterjee, and Nitesh V. Chawla. "Predicting terrorist attacks in the United States using localized news data." PLOS ONE 17, no. 6 (2022): e0270681. http://dx.doi.org/10.1371/journal.pone.0270681.

Full text

Abstract:

Terrorism is a major problem worldwide, causing thousands of fatalities and billions of dollars in damage every year. To address this threat, we propose a novel feature representation method and evaluate machine learning models that learn from localized news data in order to predict whether a terrorist attack will occur on a given calendar date and in a given state. The best model (a Random Forest aided by a novel variable-length moving average method) achieved area under the receiver operating characteristic (AUROC) of ≥ 0.667 (statistically significant w.r.t. random guessing with p ≤ .0001)

APA, Harvard, Vancouver, ISO, and other styles

34

Priyadi, Iman, Julius Santony, and Jufriadif Na'am. "Data Mining Predictive Modeling for Prediction of Gold Prices Based on Dollar Exchange Rates, Bi Rates and World Crude Oil Prices." Indonesian Journal of Artificial Intelligence and Data Mining 2, no. 2 (2019): 93. http://dx.doi.org/10.24014/ijaidm.v2i2.6864.

Full text

Abstract:

Gold is an investment instrument that is quite safe from inflationary attacks, and gold is one aspect of initiating investment. Can by buying gold in physical form and then selling when the price has risen high or by digitally investing gold. One of them is by trading gold online. To maximize the benefits of gold trading, a gold price prediction (XAUUSD) is needed for traders. This study aims to (1) Analyze various factors that influence the price of gold (2) Provide recommendations about the prediction of gold prices. Materials that will be used as objects of research to produce gold price pr

APA, Harvard, Vancouver, ISO, and other styles

35

Prajwal, S., M. Siddhartha, S. Charan, and L. Girish. "DDoS Detection and Mitigation SDN using Support Vector Machine." International Journal of Advanced Scientific Innovation 1, no. 2 (2021): 26–31. https://doi.org/10.5281/zenodo.4782280.

Full text

Abstract:

In recent years, the rise of software-defined networks (SDN) has made network control more flexible, easier to set up and manage, and has provided a stronger ability to adapt to the changing demands of application development and network conditions. The network becomes easier to maintain but also achieves improved security as a result of SDN. The architecture of SDN is designed for Control Plane and Forwarding Plane separation and uses open APIs to realize programmable control. SDN allows for the importing of third-party applications to improve network service or even provide a new network ser

APA, Harvard, Vancouver, ISO, and other styles

36

P, Arul, and Shanmugapriya N. "Intrusion Detection Using IDMAL Algorithm for IOT Devices." Indian Journal of Science and Technology 16, no. 17 (2023): 1268–75. https://doi.org/10.17485/IJST/v16i17.2030.

Full text

Abstract:

Abstract <strong>Objective:</strong> To develop a system based on fog computing to maintain the security of the user data and privacy in the IoT environment. The proposed work is intended to predict the assaults, automatically recognize known attacks, and select the appropriate defense mechanism to safeguard the private data in an IoT environment. <strong>Methods:</strong> The proposed approach is used to recognize a variety of intrusion methods, including DDOS, DOS, and multistage attacks developed by hackers with the explicit goal of seizing control of the entire IoT network a

APA, Harvard, Vancouver, ISO, and other styles

37

Yogesh V. Gandge. "A Python Framework for Pest Management and Crop Yield prediction." Communications on Applied Nonlinear Analysis 32, no. 8s (2025): 48–53. https://doi.org/10.52783/cana.v32.3540.

Full text

Abstract:

In recent years, agriculture has become increasingly important as the world's population continues to grow. The demand for food grains is constantly on the rise; this exerts more pressure on farmers to maximize their crop yields. One of the biggest challenges the farmers face is managing pests and maintaining soil fertility. Farmers apply pesticides only after witnessing the pest on the crop. By this time the pest has already made an impact on the crop leading to crop loss. Traditional methods of pest management and preserving soil fertility are time-consuming and less effective.In this paper,

APA, Harvard, Vancouver, ISO, and other styles

38

Zhai, Kun, Qiang Ren, Junli Wang, and Chungang Yan. "Byzantine-robust federated learning via credibility assessment on non-IID data." Mathematical Biosciences and Engineering 19, no. 2 (2021): 1659–76. http://dx.doi.org/10.3934/mbe.2022078.

Full text

Abstract:

<abstract> <p>Federated learning is a novel framework that enables resource-constrained edge devices to jointly learn a model, which solves the problem of data protection and data islands. However, standard federated learning is vulnerable to Byzantine attacks, which will cause the global model to be manipulated by the attacker or fail to converge. On non-iid data, the current methods are not effective in defensing against Byzantine attacks. In this paper, we propose a Byzantine-robust framework for federated learning via credibility assessment on non-iid data (BRCA). Credibility a

APA, Harvard, Vancouver, ISO, and other styles

39

Yuan, Jiaqing, and Munindar P. Singh. "Conversation Modeling to Predict Derailment." Proceedings of the International AAAI Conference on Web and Social Media 17 (June 2, 2023): 926–35. http://dx.doi.org/10.1609/icwsm.v17i1.22200.

Full text

Abstract:

Conversations among online users sometimes derail, i.e., break down into personal attacks. Derailment interferes with the healthy growth of communities in cyberspace. The ability to predict whether an ongoing conversation will derail could provide valuable advance, even real-time, insight to both interlocutors and moderators. Prior approaches predict conversation derailment retrospectively without the ability to forestall the derailment proactively. Some existing works attempt to make dynamic predictions as the conversation develops, but fail to incorporate multisource information, such as con

APA, Harvard, Vancouver, ISO, and other styles

40

Hao, Qiang, Dongdong Xu, Yusen Qin, et al. "A Hardware Security Protection Method for Conditional Branches of Embedded Systems." Micromachines 15, no. 6 (2024): 760. http://dx.doi.org/10.3390/mi15060760.

Full text

Abstract:

The branch prediction units (BPUs) generally have security vulnerabilities, which can be used by attackers to tamper with the branches, and the existing protection methods cannot defend against these attacks. Therefore, this article proposes a hardware security protection method for conditional branches of embedded systems. This method calculates the number of branch target buffer (BTB) updates every 80 clock cycles. If the number exceeds the set threshold, the BTB will be locked and prevent any process from tampering with the BTB entries, thereby resisting branch prediction analysis (BPA) att

APA, Harvard, Vancouver, ISO, and other styles

41

Alabdulatif, Abdulatif, and Sajjad Hussain Rizvi. "Network intrusion detection system using an optimized machine learning algorithm." Mehran University Research Journal of Engineering and Technology 42, no. 1 (2023): 153. http://dx.doi.org/10.22581/muet1982.2301.14.

Full text

Abstract:

The rapid growth of the data-communications network for real-world commercial applications requires security and robustness. Network intrusion is one of the most prominent network attacks. Moreover, the variants of network intrusion have also been extensively reported in the literature. Network Intrusion Detection Systems (NIDS) have already been devised and proposed in the literature to handle this issue. In the recent literature, Kitsune, NIDS, and its dataset have received approx. 500 citations so far in 2019. But, still, the comprehensive parametric evaluation of this dataset using a machi

APA, Harvard, Vancouver, ISO, and other styles

42

Zhou, Meng, Xin Li, and Lejian Liao. "On Preventing Location Attacks for Urban Vehicular Networks." Mobile Information Systems 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/5850670.

Full text

Abstract:

The prevalence of global positioning system (GPS) equipped in vehicular networks exposes users’ location information to the location-based services. We argue that such data contains rich informative cues on drivers’ private behaviors and preferences, which will lead to the location privacy attacks. In this paper, we proposed a sophisticated prediction model to predict driver’s next location by using ak-order Markov chain-based third-rank tensor representing the partially observed transfer information of vehicles. Then Bayesian Personalized Ranking (BPR) is used to learn the unobserved transiti

APA, Harvard, Vancouver, ISO, and other styles

43

Hosseinpoor, Mohammadjavad. "Presenting an Intelligent Algorithm to Predict Heart Attack." Journal of Health and Biomedical Informatics 10, no. 4 (2024): 346–56. http://dx.doi.org/10.34172/jhbmi.2024.03.

Full text

Abstract:

Introduction: Today, heart attack is one of the most common causes of death in adults all over the world. According to the announcement of the Ministry of Health, Treatment, and Medical Education, 11 to 15 percent of deaths in Iran are caused by heart attacks, and in the world, Iran has the highest number of deaths due to heart disease in 2022. It has been estimated that deaths from these diseases will increase to 20 million people. Therefore, predicting this disease is one of the most challenging topics in the medical field, and today most prediction systems are created using artificial intel

APA, Harvard, Vancouver, ISO, and other styles

44

Asra Sarwath, Dr. Raafiya Gulmeher, and Zeenath Sultana. "Neural Network Model Using An Enhanced Whale Optimization Method For Cyber Threat Detection." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 02 (2025): 164–71. https://doi.org/10.47392/irjaeh.2025.0022.

Full text

Abstract:

In our modern, highly-connected society, cybersecurity is of the utmost importance. With the rapid advancement and increasing integration of technology into our daily lives, the need of cyber security cannot be emphasized enough. Cybersecurity is vital for individual’s protection. Credential stuffing is a sort of cyber assault whereby attackers use previously obtained usernames, keywords and passwords to unlawfully invoke user accounts across many websites. This is plausible as many individuals utilize identical passwords and usernames across several websites. The proposed Enhanced Whale Optim

APA, Harvard, Vancouver, ISO, and other styles

45

Shaikh, Rumana M. "Cardiovascular Diseases Prediction Using Machine Learning Algorithms." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (2021): 1083–88. http://dx.doi.org/10.17762/turcomat.v12i6.2426.

Full text

Abstract:

A broad variety of health conditions are involved in heart disease. Several illnesses and disorders come under the heart disease umbrella. Heart disease forms include: In arrhythmia, abnormality of the heart rhythm. Arteriosclerosis, Hardening of the arteries is atherosclerosis. Via cardiomyopathy, this disorder causes muscles in the heart to harden or grow weak. Defects of the congenital heart, heart abnormalities that are present at birth are congenital heart defects. Disease of the coronary arteries (CAD), the accumulation of plaque in the heart's arteries triggers CAD. It's called ischemic

APA, Harvard, Vancouver, ISO, and other styles

46

Khalaf, Meaad Ali, and Amani Steiti. "Artificial Intelligence Predictions in Cyber Security: Analysis and Early Detection of Cyber Attacks." Babylonian Journal of Machine Learning 2024 (May 9, 2024): 63–68. http://dx.doi.org/10.58496/bjml/2024/006.

Full text

Abstract:

The landscape of cyber-attacks has changed due, to the upward push of digitalization and interconnected structures. This necessitates the need for revolutionary techniques to emerge as aware of and mitigate these threats at a degree. This studies delves into the correlation amongst cyber security and artificial intelligence (AI) with a focus on how AI can decorate detection of cyber-attacks via assessment, prediction and different strategies. By harnessing machine mastering, neural networks and records analytics predictive models driven with the useful resource of AI have emerged as an approac

APA, Harvard, Vancouver, ISO, and other styles

47

Ma, Zhuo, Xinglong Wang, Ruijie Ma, Zhuzhu Wang, and Jianfeng Ma. "Integrating Gaze Tracking and Head-Motion Prediction for Mobile Device Authentication: A Proof of Concept." Sensors 18, no. 9 (2018): 2894. http://dx.doi.org/10.3390/s18092894.

Full text

Abstract:

We introduce a two-stream model to use reflexive eye movements for smart mobile device authentication. Our model is based on two pre-trained neural networks, iTracker and PredNet, targeting two independent tasks: (i) gaze tracking and (ii) future frame prediction. We design a procedure to randomly generate the visual stimulus on the screen of mobile device, and the frontal camera will simultaneously capture head motions of the user as one watches it. Then, iTracker calculates the gaze-coordinates error which is treated as a static feature. To solve the imprecise gaze-coordinates caused by the

APA, Harvard, Vancouver, ISO, and other styles

48

Klisarić, Sanja. "Cyber terrorism as a trigger for the growing need for security of the system from the dangers that come from the internet." Megatrend revija 18, no. 2 (2021): 247–56. http://dx.doi.org/10.5937/megrev2102247k.

Full text

Abstract:

In a world of increasingly intense cyber content that relies on new technologies, the security of all systems is at risk. This article will present the danger and security options in order to protect not only data but also the entire system connected to the new technologies. Also, the importance of persons dealing with online security issues will be emphasized. The analysis of the content reveals the possibility for prediction that is presented in the paper, and connected with the conclusion that technology will develop more and more, and thus the danger of cyber attacks will be more and more

APA, Harvard, Vancouver, ISO, and other styles

49

M. Gopila. "Predicting Unusual Activity Using CNN and Time Series for Enhanced Cyber Protection." Communications on Applied Nonlinear Analysis 32, no. 5s (2024): 78–90. https://doi.org/10.52783/cana.v32.2969.

Full text

Abstract:

A great deal of systems for intrusion prevention and detection in use presently do not constantly capitalize on time series modeling's strength. System administrators will be able to better organize resource allocation and system readiness to fend off fraudulent activity by having access to time series models. In an attempt to plug the knowledge gap, we're investigating the potential of integrating a quantitatively derived time series modeling to the incumbent cyber safety system in a way that is simple to implement. This investigation conforms the GARMA (1, 1; 1, ±) hypothesis to data sets co

APA, Harvard, Vancouver, ISO, and other styles

50

Ramadhan, Rafiq Fajar, and Wahid Miftahul Ashari. "Performance Comparison of Random Forest and Decision Tree Algorithms for Anomaly Detection in Networks." Journal of Applied Informatics and Computing 8, no. 2 (2024): 367–75. https://doi.org/10.30871/jaic.v8i2.8492.

Full text

Abstract:

The increase in cyber attacks has made network security a very important focus in this digital era. This research compares the performance of two machine learning algorithms, that is Random Forest and Decision Tree for detecting anomalies in networks using the UNSW-NB15 datasets, which include various types of attacks such as DoS, Backdoor, Exploits and others which will be used to train and test both models. The data collection method, pre-processing, data splitting and modelling using SMOTE method to handle data imbalanced were applied in both algorithms and then evaluated using accuracy, pr

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!