Pub Date : 2023-01-01DOI: 10.12720/jait.14.5.1056-1062
Amir Namavar Jahromi, Ebrahim Pourjafari, Hadis Karimipour, Amit Satpathy, Lovell Hodge
{"title":"CRL+: A Novel Semi-Supervised Deep Active Contrastive Representation Learning-Based Text Classification Model for Insurance Data","authors":"Amir Namavar Jahromi, Ebrahim Pourjafari, Hadis Karimipour, Amit Satpathy, Lovell Hodge","doi":"10.12720/jait.14.5.1056-1062","DOIUrl":"https://doi.org/10.12720/jait.14.5.1056-1062","url":null,"abstract":"","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135052609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.12720/jait.14.5.1012-1018
Hongfei Jia, Yunpeng Qi, Chao Liu, Ruiyi Wu
—The dedicated Connected Autonomous Vehicle (CAV) lanes can avoid the interference of human-driven vehicles and create relatively safe operating conditions for CAVs. Besides, the dedicated CAV lanes can give full advantages of the connectivity and controllability to further improve the capacity of links. However, the consequent problem is unfairness among the traffic network users due to the higher priority right of CAVs in some links. This paper develops a bi-level programming model to design the CAV dedicated lanes deployment scheme considering the user fairness issue. In the lower-level model, we define the road resistance functions under various scenarios by investigating the effect of the dedicated lane on link capacity and construct the traffic assignment model which is solved by the diagonalized Frank-Wolfe method. The upper-level model aims to solve the multi-objective optimization problem that integrates user fairness and total system travel cost. The user fairness problem determines the fairness index using the Wilson entropy model, and the travel cost problem considers different users’ travel time value coefficients.
{"title":"A Model for Deployment of Dedicated Connected Autonomous Vehicle Lanes Considering User Fairness","authors":"Hongfei Jia, Yunpeng Qi, Chao Liu, Ruiyi Wu","doi":"10.12720/jait.14.5.1012-1018","DOIUrl":"https://doi.org/10.12720/jait.14.5.1012-1018","url":null,"abstract":"—The dedicated Connected Autonomous Vehicle (CAV) lanes can avoid the interference of human-driven vehicles and create relatively safe operating conditions for CAVs. Besides, the dedicated CAV lanes can give full advantages of the connectivity and controllability to further improve the capacity of links. However, the consequent problem is unfairness among the traffic network users due to the higher priority right of CAVs in some links. This paper develops a bi-level programming model to design the CAV dedicated lanes deployment scheme considering the user fairness issue. In the lower-level model, we define the road resistance functions under various scenarios by investigating the effect of the dedicated lane on link capacity and construct the traffic assignment model which is solved by the diagonalized Frank-Wolfe method. The upper-level model aims to solve the multi-objective optimization problem that integrates user fairness and total system travel cost. The user fairness problem determines the fairness index using the Wilson entropy model, and the travel cost problem considers different users’ travel time value coefficients.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136305462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.12720/jait.14.5.876-882
Yota Kurokawa, Masaru Fukushi
—This paper proposes a simple and effective evaluation method for fault-tolerant routing methods developed for Network-on-Chip (NoC)-based many-core processors. To cope with faults which significantly degrade the reliability of communication among cores, a variety of fault-tolerant routing methods have been studied. Those methods have been mainly evaluated in terms of communication performance such as latency and throughput by computer simulations of packet routing. However, such evaluations are not practical in that they cannot reveal the performance difference in executing parallel applications with the fault-tolerant routing methods. The proposed method obtains the information of the target parallel application such as task execution time, communication pattern, and communication amount and incorporates it in the conventional packet routing simulations. With the proposed evaluation method, computer simulations have been conducted to evaluate the performance of four famous fault-tolerant routing methods, i.e., Fcube4, Position Route, Passage-Y, and Passage-XY, using NAS Parallel Benchmarks and the performance difference is revealed in executing parallel programs named Integer Sort (IS) and Fast Fourier Transform (FFT). The results show that, Passage-XY outperforms other methods in both IS and FT, and for the case of IS, Passage-XY can reduce the program execution time by up to about 39%, 56%, and 26% compared with Fcube4, Position Route, and Passage-Y, respectively.
{"title":"A Simple and Effective Evaluation Method for Fault-Tolerant Routing Methods in Network-on-Chips","authors":"Yota Kurokawa, Masaru Fukushi","doi":"10.12720/jait.14.5.876-882","DOIUrl":"https://doi.org/10.12720/jait.14.5.876-882","url":null,"abstract":"—This paper proposes a simple and effective evaluation method for fault-tolerant routing methods developed for Network-on-Chip (NoC)-based many-core processors. To cope with faults which significantly degrade the reliability of communication among cores, a variety of fault-tolerant routing methods have been studied. Those methods have been mainly evaluated in terms of communication performance such as latency and throughput by computer simulations of packet routing. However, such evaluations are not practical in that they cannot reveal the performance difference in executing parallel applications with the fault-tolerant routing methods. The proposed method obtains the information of the target parallel application such as task execution time, communication pattern, and communication amount and incorporates it in the conventional packet routing simulations. With the proposed evaluation method, computer simulations have been conducted to evaluate the performance of four famous fault-tolerant routing methods, i.e., Fcube4, Position Route, Passage-Y, and Passage-XY, using NAS Parallel Benchmarks and the performance difference is revealed in executing parallel programs named Integer Sort (IS) and Fast Fourier Transform (FFT). The results show that, Passage-XY outperforms other methods in both IS and FT, and for the case of IS, Passage-XY can reduce the program execution time by up to about 39%, 56%, and 26% compared with Fcube4, Position Route, and Passage-Y, respectively.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135649055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.12720/jait.14.1.26-38
A. M. Syarif, A. Azhari, S. Suprapto, K. Hastuti
This study proposes a Gamelan melody generation system based on three characteristics, which are the melodic patterns recognition, composition meter rules that control the duration of notes, and the special notes (pitches) selection which represent ambiguous rules in determining the Gamelan musical mode system. Long-Short Term Memory (LSTM) networks were trained using the sequence prediction technique to generate symbolic based Gamelan melodies. The dataset collected from sheet music was converted into ABC notation format, added with codes representing the composition meter and special notes, and restructured into a character-based representation format. The LSTM network training showed good results in the melodic patterns recognition but the networks take less than 10 attempts for the LSTM network to successfully generate one melody. The evaluation was conducted using experts’ judgment. Three generated melodies were sent to experts to be read, hummed and judged. Overall, the evaluation results showed that the generated melodies can comply with the characteristics of the Gamelan melodic patterns, the composition meter and the special notes.
{"title":"Gamelan Melody Generation Using LSTM Networks Controlled by Composition Meter Rules and Special Notes","authors":"A. M. Syarif, A. Azhari, S. Suprapto, K. Hastuti","doi":"10.12720/jait.14.1.26-38","DOIUrl":"https://doi.org/10.12720/jait.14.1.26-38","url":null,"abstract":"This study proposes a Gamelan melody generation system based on three characteristics, which are the melodic patterns recognition, composition meter rules that control the duration of notes, and the special notes (pitches) selection which represent ambiguous rules in determining the Gamelan musical mode system. Long-Short Term Memory (LSTM) networks were trained using the sequence prediction technique to generate symbolic based Gamelan melodies. The dataset collected from sheet music was converted into ABC notation format, added with codes representing the composition meter and special notes, and restructured into a character-based representation format. The LSTM network training showed good results in the melodic patterns recognition but the networks take less than 10 attempts for the LSTM network to successfully generate one melody. The evaluation was conducted using experts’ judgment. Three generated melodies were sent to experts to be read, hummed and judged. Overall, the evaluation results showed that the generated melodies can comply with the characteristics of the Gamelan melodic patterns, the composition meter and the special notes.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66329271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.12720/jait.14.1.94-101
Afaf Tareef, Hayat Al-Dmour, Afnan Al-Sarayreh
Automated detection of human identity and gender offers several industrial applications in near future, such as monitoring, surveillance, commercial profiling and human computer interaction. In this paper, deep learning techniques have been used to investigate the problem of human identity and gender classification using hand images. First, pre-processing techniques have been applied to enhance the appearance of the hand images. The pre-processed image is passed through the convolution neural network to determine the gander. For identity detection, the network has been trained on the images for the determined gender for better recognition. To further enhance the result, the framework has been implemented using different optimizers and k fold cross-validation. Experimental results have shown that highly effective performance is achieved in both the human identification and gender classification objectives. High average accuracy of 97.75% using the dorsal hand side for human identification and 96.79% has been obtained for gender classification using the palm hand side. Conclusively, the proposed method has achieved more accuracy than the previous methods both for identification and gender classification.
{"title":"An Automated Deep Learning Framework for Human Identity and Gender Detection","authors":"Afaf Tareef, Hayat Al-Dmour, Afnan Al-Sarayreh","doi":"10.12720/jait.14.1.94-101","DOIUrl":"https://doi.org/10.12720/jait.14.1.94-101","url":null,"abstract":"Automated detection of human identity and gender offers several industrial applications in near future, such as monitoring, surveillance, commercial profiling and human computer interaction. In this paper, deep learning techniques have been used to investigate the problem of human identity and gender classification using hand images. First, pre-processing techniques have been applied to enhance the appearance of the hand images. The pre-processed image is passed through the convolution neural network to determine the gander. For identity detection, the network has been trained on the images for the determined gender for better recognition. To further enhance the result, the framework has been implemented using different optimizers and k fold cross-validation. Experimental results have shown that highly effective performance is achieved in both the human identification and gender classification objectives. High average accuracy of 97.75% using the dorsal hand side for human identification and 96.79% has been obtained for gender classification using the palm hand side. Conclusively, the proposed method has achieved more accuracy than the previous methods both for identification and gender classification.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66329821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.12720/jait.14.1.77-84
Tuan Nguyen Kim, Duy Ho Ngoc, Nin Ho Le Viet, N. Moldovyan
Many types of digital signature schemes have been researched and published in recent years. In this paper, we propose two new types of collective signature schemes, namely i) the collective signature for several signing groups and ii) the collective signature for several individual signings and several signing groups. And then we used two difficult problems factoring and discrete logarithm to construct these schemes. To create a combination of these two difficult problems we use the prime module p with a special structure: p = Nn + 1 with n = rq, N is an even number, r and q are prime numbers of at least 512 bit. Schnorr’s digital signature scheme and the RSA key generation algorithm are used to construct related basic schemes such as the single signature scheme, the collective signature scheme, and the group signature scheme. The proposed collective signature schemes are built from these basic schemes. The correctness, security level and performance of the proposed schemes have also been presented in this paper.
近年来，人们研究和发表了许多类型的数字签名方案。本文提出了两种新的集体签名方案，即i)多个签名组的集体签名方案和ii)多个个人签名和多个签名组的集体签名方案。然后我们用两个难题分解和离散对数来构造这些格式。为了创建这两个难题的组合，我们使用具有特殊结构的素数模块p: p = Nn + 1, n = rq, n是偶数，r和q是至少512位的素数。使用Schnorr的数字签名方案和RSA密钥生成算法构建了相关的基本方案，如单个签名方案、集体签名方案和组签名方案。提出的集体签名方案是在这些基本方案的基础上构建的。本文还介绍了所提方案的正确性、安全性和性能。
{"title":"The New Collective Signature Schemes Based on Two Hard Problems Using Schnorr's Signature Standard","authors":"Tuan Nguyen Kim, Duy Ho Ngoc, Nin Ho Le Viet, N. Moldovyan","doi":"10.12720/jait.14.1.77-84","DOIUrl":"https://doi.org/10.12720/jait.14.1.77-84","url":null,"abstract":"Many types of digital signature schemes have been researched and published in recent years. In this paper, we propose two new types of collective signature schemes, namely i) the collective signature for several signing groups and ii) the collective signature for several individual signings and several signing groups. And then we used two difficult problems factoring and discrete logarithm to construct these schemes. To create a combination of these two difficult problems we use the prime module p with a special structure: p = Nn + 1 with n = rq, N is an even number, r and q are prime numbers of at least 512 bit. Schnorr’s digital signature scheme and the RSA key generation algorithm are used to construct related basic schemes such as the single signature scheme, the collective signature scheme, and the group signature scheme. The proposed collective signature schemes are built from these basic schemes. The correctness, security level and performance of the proposed schemes have also been presented in this paper.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66330044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.12720/jait.14.2.178-184
Azeema Sadia, Fatima Bashir, Reema Qaiser Khan, Ammarah Khalid
—The Internet is used as a tool to offer people with endless knowledge. It is a global platform which is used for connectivity, communication, and sharing. At almost no cost, an individual can use the Internet to send email messages, update tweets, and Facebook messages to a vast number of people. These messages can also contain unsolicited advertisement which is identified as a spam. The company Twitter too is massively affected by spamming and it is an alarming issue for them. Twitter considers spam as actions that are unsolicited and repeated. These include tweet repetition, and the URLs that lead users to completely unrelated websites. The authors’ have worked with twitter’s dataset focusing on tweets about “iPhone”. It was collected by using an API which was further pre-processed. In this paper, content-based features have been selected that recognize the spamming tweet by using R. Multiple machine learning algorithms were applied to detect spamming tweets: Naive Bayes, Logistic Regression, KNN, Decision Tree, and Support Vector Machine. It was observed that the best performance was achieved by Naive Bayes Algorithm giving an accuracy of 89%.
{"title":"Comparison of Machine Learning Algorithms for Spam Detection","authors":"Azeema Sadia, Fatima Bashir, Reema Qaiser Khan, Ammarah Khalid","doi":"10.12720/jait.14.2.178-184","DOIUrl":"https://doi.org/10.12720/jait.14.2.178-184","url":null,"abstract":"—The Internet is used as a tool to offer people with endless knowledge. It is a global platform which is used for connectivity, communication, and sharing. At almost no cost, an individual can use the Internet to send email messages, update tweets, and Facebook messages to a vast number of people. These messages can also contain unsolicited advertisement which is identified as a spam. The company Twitter too is massively affected by spamming and it is an alarming issue for them. Twitter considers spam as actions that are unsolicited and repeated. These include tweet repetition, and the URLs that lead users to completely unrelated websites. The authors’ have worked with twitter’s dataset focusing on tweets about “iPhone”. It was collected by using an API which was further pre-processed. In this paper, content-based features have been selected that recognize the spamming tweet by using R. Multiple machine learning algorithms were applied to detect spamming tweets: Naive Bayes, Logistic Regression, KNN, Decision Tree, and Support Vector Machine. It was observed that the best performance was achieved by Naive Bayes Algorithm giving an accuracy of 89%.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66330518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.12720/jait.14.3.463-471
T. S. Hlalele, Yanxia Sun, Zenghui Wang
— The incorporation of distributed energy resources in the distribution networks changes the fault current level and makes the fault detection be more complex. There are several challenges brought by these heterogenous energy systems including power quality, voltage stability, reliability and protection. In this paper, a fault detection based on reinforcement learning approach is proposed. The heart of this approach is a Q learning approach which uses a non-adaptive multi-agent reinforcement learning algorithm to detect and identify nonlinear system faults, and the algorithm learns the policy by telling an agent what actions to take under what circumstances. Moreover, the Discrete Wavelet Transform (DWT) is utilized to extract coefficient values from the captured one-fourth cycle of the three-phase current signal during fault which occurs during the transient stage. The simulations and signal analysis for different faults are used to validate the proposed fault detection method in MATLAB environment. The simulation results show that different types of faults such as CA, AB, ABC and ABCG can be detected and the best correlation coefficient achieved is 0.87851.
{"title":"Intelligent Fault Detection Based on Reinforcement Learning Technique on Distribution Networks","authors":"T. S. Hlalele, Yanxia Sun, Zenghui Wang","doi":"10.12720/jait.14.3.463-471","DOIUrl":"https://doi.org/10.12720/jait.14.3.463-471","url":null,"abstract":"— The incorporation of distributed energy resources in the distribution networks changes the fault current level and makes the fault detection be more complex. There are several challenges brought by these heterogenous energy systems including power quality, voltage stability, reliability and protection. In this paper, a fault detection based on reinforcement learning approach is proposed. The heart of this approach is a Q learning approach which uses a non-adaptive multi-agent reinforcement learning algorithm to detect and identify nonlinear system faults, and the algorithm learns the policy by telling an agent what actions to take under what circumstances. Moreover, the Discrete Wavelet Transform (DWT) is utilized to extract coefficient values from the captured one-fourth cycle of the three-phase current signal during fault which occurs during the transient stage. The simulations and signal analysis for different faults are used to validate the proposed fault detection method in MATLAB environment. The simulation results show that different types of faults such as CA, AB, ABC and ABCG can be detected and the best correlation coefficient achieved is 0.87851.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66331130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.12720/jait.14.3.444-453
Foram Suthar, Nimisha Patel
—The internet is an obvious target for a cyberattack nowadays. The population on the internet globally is increasing from 3 billion in 2014 to 4.5 billion in 2020, resulting into nearly 59% of the total world population. The attacker is always looking for loopholes and vulnerabilities of internet-connected devices. It has been noticed from the last decade, there are more Denial-of-Service Attack (DoS) or DoS attacks and their variant Distributed Denial-of-Service (DDoS) or DDoS attacks performed by the attacker. This creates a serious problem for the network administrator to secure the infrastructure. The attacker mainly targets reputed organization/ industries and try to violate the major parameter of cyber security— Availability. The most commonly performed attack by the attacker is a Transmission Control Protocol (TCP) Synonym (SYN) DDoS attack, caused due to the design issue of the TCP algorithm. The attacker floods the packets in the network causing the server to crash. Hence, it is important to understand the source of the DDoS attack. Therefore, a real-life and accurate TCP SYN detection mechanism is required. Numerous techniques have been used for preventing and detecting various DDoS flooding attacks, some of which are covered in the literature review. The paper highlights the strengths and weaknesses of the available defense mechanism. To understand the performance status of the system we have implemented a DoS by the hping3 tool. This gives us better clarity in shortlisting and analyzing the parameters for the detection of DDoS attacks. Also, we try to analyze the impact of TCP SYN attack on the network in DDoS attacks.
互联网是当今网络攻击的明显目标。全球互联网人口将从2014年的30亿增加到2020年的45亿，占世界总人口的近59%。攻击者总是在寻找联网设备的漏洞和漏洞。从过去的十年中已经注意到，有更多的拒绝服务攻击(DoS)或DoS攻击及其变体分布式拒绝服务(DDoS)或DDoS攻击由攻击者执行。这给网络管理员保护基础设施带来了严重的问题。攻击者主要针对知名组织/行业，并试图破坏网络安全的主要参数-可用性。攻击者最常见的攻击是TCP (Transmission Control Protocol) SYN (Transmission Control Protocol Synonym) DDoS攻击，这是由于TCP算法的设计问题造成的。攻击者使报文在网络中泛滥，导致服务器崩溃。因此，了解DDoS攻击的来源非常重要。因此，需要一种真实、准确的TCP SYN检测机制。许多技术已被用于预防和检测各种DDoS洪水攻击，其中一些在文献综述中有介绍。本文重点分析了现有防御机制的优缺点。为了了解系统的性能状况，我们通过hping3工具实现了一个DoS。这使我们更清楚地列出和分析检测DDoS攻击的参数。同时，我们尝试分析TCP SYN攻击在DDoS攻击中对网络的影响。
{"title":"A Survey on DDoS Detection and Prevention Mechanism","authors":"Foram Suthar, Nimisha Patel","doi":"10.12720/jait.14.3.444-453","DOIUrl":"https://doi.org/10.12720/jait.14.3.444-453","url":null,"abstract":"—The internet is an obvious target for a cyberattack nowadays. The population on the internet globally is increasing from 3 billion in 2014 to 4.5 billion in 2020, resulting into nearly 59% of the total world population. The attacker is always looking for loopholes and vulnerabilities of internet-connected devices. It has been noticed from the last decade, there are more Denial-of-Service Attack (DoS) or DoS attacks and their variant Distributed Denial-of-Service (DDoS) or DDoS attacks performed by the attacker. This creates a serious problem for the network administrator to secure the infrastructure. The attacker mainly targets reputed organization/ industries and try to violate the major parameter of cyber security— Availability. The most commonly performed attack by the attacker is a Transmission Control Protocol (TCP) Synonym (SYN) DDoS attack, caused due to the design issue of the TCP algorithm. The attacker floods the packets in the network causing the server to crash. Hence, it is important to understand the source of the DDoS attack. Therefore, a real-life and accurate TCP SYN detection mechanism is required. Numerous techniques have been used for preventing and detecting various DDoS flooding attacks, some of which are covered in the literature review. The paper highlights the strengths and weaknesses of the available defense mechanism. To understand the performance status of the system we have implemented a DoS by the hping3 tool. This gives us better clarity in shortlisting and analyzing the parameters for the detection of DDoS attacks. Also, we try to analyze the impact of TCP SYN attack on the network in DDoS attacks.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66331376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.12720/jait.14.3.559-570
Samin Ahsan Tausif, Aysha Gazi Mouri, Ishfaq Rahman, Nilufar Hossain, H. M. Z. Haque
—The expanded use of smartphones and the Internet of Things have enabled the usage of mobile crowdsensing technologies to improve public health care in clinical sciences. Mobile crowdsensing enlightens a new sensing pattern that can reliably differentiate individuals based on their cognitive fitness. In previous studies on this domain, the visual correlation has not been illustrated between physiological functions and the mental fitness of human beings. Therefore, there exists potential gaps in providing mathematical evidence of correlation between physical activities & cognitive health. Moreover, empirical analysis of autonomous smartphone sensing to assess mental health is yet to be researched on a large scale, showing the correspondence between ubiquitous mobile sensors data and Patient Health Questionnaire-9 (PHQ-9) depression scales. This research systematically collects mobile sensors’ data along with standard PHQ-9 questionnaire data and utilizes traditional machine learning techniques (Supervised and Unsupervised) for performing necessary analysis. Moreover, we have conducted statistical t-tests to find similarities or to differentiate between people of distinct cognitive fitness levels. This research has successfully demonstrated the numerical evidence of correlations between physiological activities and the cognitive fitness of human beings. The Fine-tuned regression models built for the purpose of predicting users’ cognitive fitness score, perform accurately to a certain extent. In this analysis, crowdsensing is perceived to differentiate several people’s cognitive fitness levels comprehensively. Furthermore, our study has addressed a significant insights to assessing people’s mental fitness by relying upon their smartphone usage.
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