Pub Date : 2023-01-10DOI: 10.33897/fujeas.v3i2.690
Adeel Ahmed
Authentication of smartphone devices has been never so important nowadays. Machine learning techniques are not far behind to touch the new milestones of the latest and ever updating world. However, totally depending on machine learning will give you the scenarios of false user being accepted as true one and a true user being rejected as the false one, which can be devastating in some cases. Fifth factor of authentication “Something You Process” eradicates most of the cases of the false acceptance and false rejection, if used with the mentioned techniques. The novel approach applied here is the fifth factor combined with machine learning system and Behavioral authentication. The fifth factor is anti-shoulder surfing since the arithmetic operation is hidden by hand placed on the screen. After placing hand on the screen in such a way that it hides the code from others, the system shows the arithmetic operation and the processed calculation is performed in user’s mind. The pattern which is shown to the user is public, but machine learns the touch dynamics of the user along with his different postures including lying posture. The focus has been on the aspect of something that can be another layer or line of defense which can save the user’s authentication process. It results in decrement of false acceptance or false rejection upon unlocking of a smartphone device. This study deals with the postures of standing, sitting, and lying. The data is collected and the features are extracted in all of these positions.
{"title":"Behavioral Authentication for Smartphones backed by Something you Process","authors":"Adeel Ahmed","doi":"10.33897/fujeas.v3i2.690","DOIUrl":"https://doi.org/10.33897/fujeas.v3i2.690","url":null,"abstract":"Authentication of smartphone devices has been never so important nowadays. Machine learning techniques are not far behind to touch the new milestones of the latest and ever updating world. However, totally depending on machine learning will give you the scenarios of false user being accepted as true one and a true user being rejected as the false one, which can be devastating in some cases. Fifth factor of authentication “Something You Process” eradicates most of the cases of the false acceptance and false rejection, if used with the mentioned techniques. The novel approach applied here is the fifth factor combined with machine learning system and Behavioral authentication. The fifth factor is anti-shoulder surfing since the arithmetic operation is hidden by hand placed on the screen. After placing hand on the screen in such a way that it hides the code from others, the system shows the arithmetic operation and the processed calculation is performed in user’s mind. The pattern which is shown to the user is public, but machine learns the touch dynamics of the user along with his different postures including lying posture. The focus has been on the aspect of something that can be another layer or line of defense which can save the user’s authentication process. It results in decrement of false acceptance or false rejection upon unlocking of a smartphone device. This study deals with the postures of standing, sitting, and lying. The data is collected and the features are extracted in all of these positions.","PeriodicalId":36255,"journal":{"name":"Iranian Journal of Botany","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85070995","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 : 2023-01-10DOI: 10.33897/fujeas.v3i2.565
Ijaz Hussain, Wajiha Safat
The existing data for clinical diagnosis are often enlarged, but available tools are not efficient enough for decision making. Data mining techniques provide a user-oriented approach for clinical diagnosis and reduce risk factors. To improve clinical diagnosis, particularly for heart diseases, nine different data mining techniques have been applied for classification and clustering. We compare all these techniques for better prediction. Despite all recent research efforts, the literature lacks the application of multiple techniques on multiple data sets for heart disease prediction; which helps in decision making. In particular, this study is the augmentation of techniques for multiple data analysis by comparing four datasets with 14 attributes and a different number of instances. Another challenge is how to increase the accuracy of the decision-making process. Our research findings predict the better accuracy by using SMO and classification via regression for all data sets which shows the significant difference. Consequently, this research further helps to integrate the clinical decision support, thereby reducing medical errors, enhance patient safety, decrease unwanted practice variation, and improve patient recovery.
{"title":"Heart Diseases Prediction and Diagnosis using Supervised Learning","authors":"Ijaz Hussain, Wajiha Safat","doi":"10.33897/fujeas.v3i2.565","DOIUrl":"https://doi.org/10.33897/fujeas.v3i2.565","url":null,"abstract":"The existing data for clinical diagnosis are often enlarged, but available tools are not efficient enough for decision making. Data mining techniques provide a user-oriented approach for clinical diagnosis and reduce risk factors. To improve clinical diagnosis, particularly for heart diseases, nine different data mining techniques have been applied for classification and clustering. We compare all these techniques for better prediction. Despite all recent research efforts, the literature lacks the application of multiple techniques on multiple data sets for heart disease prediction; which helps in decision making. In particular, this study is the augmentation of techniques for multiple data analysis by comparing four datasets with 14 attributes and a different number of instances. Another challenge is how to increase the accuracy of the decision-making process. Our research findings predict the better accuracy by using SMO and classification via regression for all data sets which shows the significant difference. Consequently, this research further helps to integrate the clinical decision support, thereby reducing medical errors, enhance patient safety, decrease unwanted practice variation, and improve patient recovery.","PeriodicalId":36255,"journal":{"name":"Iranian Journal of Botany","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90628050","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 : 2023-01-10DOI: 10.33897/fujeas.v3i2.691
Saqib Iqbal, G. F. Siddiqui, Lal Hussain
computational Modelling is emerging field to model the cognitive as well as social interactions between individual and society. Aggression is social evil which is instance response and its impact last for long time. Different societies have different norms and values based on ecological, environmental and cultural attributes so aggression level also varies among individuals and societies. Current study is based on psychological and temporal aggressive behaviour different individuals and societies in same habitat. In this paper we have proposed a frame work to model human social and psychological behaviors. Results are based on simulation which are according to our assumptions.
{"title":"Country level Social Aggression using Computational Modelling","authors":"Saqib Iqbal, G. F. Siddiqui, Lal Hussain","doi":"10.33897/fujeas.v3i2.691","DOIUrl":"https://doi.org/10.33897/fujeas.v3i2.691","url":null,"abstract":"computational Modelling is emerging field to model the cognitive as well as social interactions between individual and society. Aggression is social evil which is instance response and its impact last for long time. Different societies have different norms and values based on ecological, environmental and cultural attributes so aggression level also varies among individuals and societies. Current study is based on psychological and temporal aggressive behaviour different individuals and societies in same habitat. In this paper we have proposed a frame work to model human social and psychological behaviors. Results are based on simulation which are according to our assumptions.","PeriodicalId":36255,"journal":{"name":"Iranian Journal of Botany","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85897346","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 : 2023-01-10DOI: 10.33897/fujeas.v3i2.688
S. Rehman
Food quality is a major issue for society since it is a crucial guarantee not only for human health but also for society's progress and stability. The planting, harvesting, and storage through preparation and consumption, all aspects of food processing should be considered. One of the most important methods for managing fruit and vegetable quality is by using AI food quality evaluation techniques. Emerging technologies such as computer vision and artificial intelligence (AI) are thought to profit from the availability of massive data for active training and the generation of intelligent and operational equipment in real-time and predictably. The review helps provide an overview of leading-edge artificial intelligence and computer vision technologies that can help farmers in agriculture and food processing. In addition, the review presents some implications for the challenges and recommendations regarding the inclusion of technologies in real-time agriculture, policies, and substantial global investments. In addition, the fourth industrial revolution technologies of profound learning and computer vision robotics which are key to sustainability for food production is also addressed in it.
{"title":"A Comparative Analysis of Fruits and Vegetables Quality Using AI-Assisted Technologies: A review","authors":"S. Rehman","doi":"10.33897/fujeas.v3i2.688","DOIUrl":"https://doi.org/10.33897/fujeas.v3i2.688","url":null,"abstract":" \u0000Food quality is a major issue for society since it is a crucial guarantee not only for human health but also for society's progress and stability. The planting, harvesting, and storage through preparation and consumption, all aspects of food processing should be considered. One of the most important methods for managing fruit and vegetable quality is by using AI food quality evaluation techniques. Emerging technologies such as computer vision and artificial intelligence (AI) are thought to profit from the availability of massive data for active training and the generation of intelligent and operational equipment in real-time and predictably. The review helps provide an overview of leading-edge artificial intelligence and computer vision technologies that can help farmers in agriculture and food processing. In addition, the review presents some implications for the challenges and recommendations regarding the inclusion of technologies in real-time agriculture, policies, and substantial global investments. In addition, the fourth industrial revolution technologies of profound learning and computer vision robotics which are key to sustainability for food production is also addressed in it.","PeriodicalId":36255,"journal":{"name":"Iranian Journal of Botany","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83798193","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 : 2023-01-10DOI: 10.33897/fujeas.v3i2.689
Rashid Amin, Adeel Ahmed, Syed Shabih Ul Hasan, Habib Akbar
Human eyes are vulnerable to several abnormalities because of trauma, aging and disease like diabetes. The main factors of blindness around the world are glaucoma, cataract, macular degeneration and diabetic retinopathy etc. These eye diseases need to be detected and diagnosed timely with appropriate treatment for the solution of this problem. Multiple eye disease detection by analyzing various medical images can provide a timely diagnosis of eye diseases. The steps that are involved in multiple eye disease detection using deep learning are the acquisition of images, region of interest extraction, extraction of features and classification or detection of a particular disease. In this paper, diseases like uveitis, glaucoma, crossed eyes, bulging eyes and cataracts have been detected using deep learning models like Resnet and vgg16 model. We have obtained 92% accuracy using Resnet50 and 79% accuracy using the vgg16 model.
{"title":"Multiple eye disease detection using deep learning","authors":"Rashid Amin, Adeel Ahmed, Syed Shabih Ul Hasan, Habib Akbar","doi":"10.33897/fujeas.v3i2.689","DOIUrl":"https://doi.org/10.33897/fujeas.v3i2.689","url":null,"abstract":"Human eyes are vulnerable to several abnormalities because of trauma, aging and disease like diabetes. The main factors of blindness around the world are glaucoma, cataract, macular degeneration and diabetic retinopathy etc. These eye diseases need to be detected and diagnosed timely with appropriate treatment for the solution of this problem. Multiple eye disease detection by analyzing various medical images can provide a timely diagnosis of eye diseases. The steps that are involved in multiple eye disease detection using deep learning are the acquisition of images, region of interest extraction, extraction of features and classification or detection of a particular disease. In this paper, diseases like uveitis, glaucoma, crossed eyes, bulging eyes and cataracts have been detected using deep learning models like Resnet and vgg16 model. We have obtained 92% accuracy using Resnet50 and 79% accuracy using the vgg16 model.","PeriodicalId":36255,"journal":{"name":"Iranian Journal of Botany","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84844291","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 : 2022-06-16DOI: 10.33897/fujeas.v3i1.571
Ijaz Hussain
The emergence of the Internet has caused an increasing generation of data. A high amount of the data is of textual form, which is highly unstructured. Almost every field i.e, business, engineering, medicine, and science can benefit from the textual data when knowledge is extracted. The knowledge extraction requires the extraction and recording of metadata on the unstructured text documents that constitute the textual data. This phenomenon is regarded as topic modeling. The resulting topics can ease searching, statistical characterization, and classification. Some well-known algorithms for topic modeling include Latent Dirichlet Allocation (LDA), Nonnegative Matrix Factorization (NMF), and Probabilistic Latent Semantic Analysis (PLSA). Different parameters can affect the performance of topic modeling. An interesting parameter could be the time required to perform topic modeling. The fact that time is affected by many factors applicable to topic modeling as well; however, measuring the time concerning some constraints can be beneficial to provide insight. In this paper, we alter some preprocessing steps and topics to study their impact on the time taken by the LDA and NMF topic models. In preprocessing, we limit our study by altering only the sampling and feature subset selection whereas in the second step we have changed the number of topics. The results show a significant improvement in time.
{"title":"Effect of Preprocessing and No of Topics on Automated Topic Classification Performance","authors":"Ijaz Hussain","doi":"10.33897/fujeas.v3i1.571","DOIUrl":"https://doi.org/10.33897/fujeas.v3i1.571","url":null,"abstract":"The emergence of the Internet has caused an increasing generation of data. A high amount of the data is of textual form, which is highly unstructured. Almost every field i.e, business, engineering, medicine, and science can benefit from the textual data when knowledge is extracted. The knowledge extraction requires the extraction and recording of metadata on the unstructured text documents that constitute the textual data. This phenomenon is regarded as topic modeling. The resulting topics can ease searching, statistical characterization, and classification. Some well-known algorithms for topic modeling include Latent Dirichlet Allocation (LDA), Nonnegative Matrix Factorization (NMF), and Probabilistic Latent Semantic Analysis (PLSA). Different parameters can affect the performance of topic modeling. An interesting parameter could be the time required to perform topic modeling. The fact that time is affected by many factors applicable to topic modeling as well; however, measuring the time concerning some constraints can be beneficial to provide insight. In this paper, we alter some preprocessing steps and topics to study their impact on the time taken by the LDA and NMF topic models. In preprocessing, we limit our study by altering only the sampling and feature subset selection whereas in the second step we have changed the number of topics. The results show a significant improvement in time.","PeriodicalId":36255,"journal":{"name":"Iranian Journal of Botany","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78756018","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 : 2022-06-16DOI: 10.33897/fujeas.v3i1.506
Muhammad Imran, Anmol Haider
Appearance and facial features play an important role in gender recognition through images. For gender classification, multiple techniques were presented to acquire better results in which preprocessing part is one of the major and very important for gender classification as it removes noise, enhances, images, and eliminates any unnatural colors from an image. Another major aspect is the efficient feature extraction method. If features extracted accurately then the result of classification will improve. Over the past few years, gender classification techniques work perfectly for a controlled environment. However, challenges occurred for real-time applications due to low resolution, off-angle poses, faces with occlusion, and various expressions. The main focus of this study is to overcome existing challenges and propose a method that can be implemented in real-time applications. This research work proposed a novel method in which CNN has been used for classification of gender for real-time application. To assess the performance of proposed method experiments were conducted on static images and video data sets. The proposed research work achieved 98% of accuracy during the experiments.
{"title":"Facial Based Gender Classification for Real Time Applications","authors":"Muhammad Imran, Anmol Haider","doi":"10.33897/fujeas.v3i1.506","DOIUrl":"https://doi.org/10.33897/fujeas.v3i1.506","url":null,"abstract":"Appearance and facial features play an important role in gender recognition through images. For gender classification, multiple techniques were presented to acquire better results in which preprocessing part is one of the major and very important for gender classification as it removes noise, enhances, images, and eliminates any unnatural colors from an image. \u0000Another major aspect is the efficient feature extraction method. If features extracted accurately then the result of classification will improve. Over the past few years, gender classification techniques work perfectly for a controlled environment. However, challenges occurred for real-time applications due to low resolution, off-angle poses, faces with occlusion, and various expressions. The main focus of this study is to overcome existing challenges and propose a method that can be implemented in real-time applications. This research work proposed a novel method in which CNN has been used for classification of gender for real-time application. To assess the performance of proposed method experiments were conducted on static images and video data sets. The proposed research work achieved 98% of accuracy during the experiments.","PeriodicalId":36255,"journal":{"name":"Iranian Journal of Botany","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90428909","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 : 2022-06-16DOI: 10.33897/fujeas.v3i1.381
Dr Atif Ali
Nano-robotics is a scientific discipline that is becoming more and more popular given the perspectives it opens up through many applications. The fields of application of the nano-robot are immense: materials technology, space, ecology, IT, electronics, communications, etc. But the discipline which is being revolutionized by these new applications of nano-robotics is military weapons and applications. This is why in this article, after an overview of the theory of the nanoworld, the rest of the document has focused on applications in the military. The latest remarkable advances in the application of nano-robots in the military have been compiled. Their benefits, radically revolutionary, military nanotechnologies have been discussed as more destructive weapons than nuclear weapons for the whole world and their future use in all military regions.
{"title":"Nano-Robotics: Next Level of Military Technologies","authors":"Dr Atif Ali","doi":"10.33897/fujeas.v3i1.381","DOIUrl":"https://doi.org/10.33897/fujeas.v3i1.381","url":null,"abstract":"Nano-robotics is a scientific discipline that is becoming more and more popular given the perspectives it opens up through many applications. The fields of application of the nano-robot are immense: materials technology, space, ecology, IT, electronics, communications, etc. But the discipline which is being revolutionized by these new applications of nano-robotics is military weapons and applications. This is why in this article, after an overview of the theory of the nanoworld, the rest of the document has focused on applications in the military. The latest remarkable advances in the application of nano-robots in the military have been compiled. Their benefits, radically revolutionary, military nanotechnologies have been discussed as more destructive weapons than nuclear weapons for the whole world and their future use in all military regions.","PeriodicalId":36255,"journal":{"name":"Iranian Journal of Botany","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78015841","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 : 2022-06-16DOI: 10.33897/fujeas.v3i1.564
Rashid Amin
Sentiment analysis (SA) is a process that aims to classify text into positive, negative, or neutral categories. It has recently gained the research community's attention because of the abundance of opinion data to be processed for better understanding and decision-making. Deep learning techniques have recently shown tremendous performance, with a high tendency to reveal the underlying semantic meaning of text inputs. Since deep learning techniques are seen as black boxes, their effectiveness comes in the form of interpretability. The major goal of this article is to create an Urdu SA model that can comprehend review semantics without the need for language resources. The proposed model is tested on reviews to extract significant words using various scenarios and architectures. By emphasizing the most informative terms to the class label, the results demonstrated the suggested model's capacity to interpret a given review. Furthermore, the suggested models provide a visualization option for an intelligible explanation of the result. The impact of using transfer learning on the problem of Urdu SA is also investigated in this article.
{"title":"Urdu Sentiment Analysis Using Deep Attention-based Technique","authors":"Rashid Amin","doi":"10.33897/fujeas.v3i1.564","DOIUrl":"https://doi.org/10.33897/fujeas.v3i1.564","url":null,"abstract":"Sentiment analysis (SA) is a process that aims to classify text into positive, negative, or neutral categories. It has recently gained the research community's attention because of the abundance of opinion data to be processed for better understanding and decision-making. Deep learning techniques have recently shown tremendous performance, with a high tendency to reveal the underlying semantic meaning of text inputs. Since deep learning techniques are seen as black boxes, their effectiveness comes in the form of interpretability. The major goal of this article is to create an Urdu SA model that can comprehend review semantics without the need for language resources. The proposed model is tested on reviews to extract significant words using various scenarios and architectures. By emphasizing the most informative terms to the class label, the results demonstrated the suggested model's capacity to interpret a given review. Furthermore, the suggested models provide a visualization option for an intelligible explanation of the result. The impact of using transfer learning on the problem of Urdu SA is also investigated in this article.","PeriodicalId":36255,"journal":{"name":"Iranian Journal of Botany","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75477507","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 : 2022-06-16DOI: 10.33897/fujeas.v3i1.687
S. Rehman
Now a day, the fast growth of Internet access and the adoption of smart digital technology has resulted in new cybercrime strategies targeting regular people and businesses. The Web and social activities take precedence in most aspects of their lives, but also poses significant social risks. Static and dynamic analysis are inefficient in detecting unknown malware in standard threat detection approaches. Virus makers create new malware by modifying current malware using polymorphic and evasion tactics in order to fool. Furthermore, by utilizing selection of features techniques to identify more important features and minimizing amount of the data, these Machine Learning models' accuracy can be increased, resulting in fewer calculations. In the previous study traditional machine learning approaches were used to detect Malware. We employed Cuckoo sandbox, a malware detection and analysis system for detection and categorization, in this study we provide a Machine Learning based Intrusion analysis system to calculate exact and on spot Intrusion classification. We integrated feature extraction and component selection from the file, as well as selecting the much higher quality, resulting in exceptional accuracy and cheaper computing costs. For reliable identification and fine-grained categorization, we use a variety of machine learning algorithms. Our experimental results show that we achieved good, classified accuracy when compared to state-of-the-art approaches. We employed machine learning techniques such as K-Nearest Neighbor, Random Forest, Support Vector Machine, and Decision Tree. Using the Random Forest classifier on 108 features, we attained the greatest accuracy of 99.37 percent. We also discovered that Random Forest outscored all other classic machine learning techniques during the procedure. These findings can aid in the exact and accurate identification of Malware families.
{"title":"Intrusion Detection in Cyber Space Using Machine Learning Based Algorithm","authors":"S. Rehman","doi":"10.33897/fujeas.v3i1.687","DOIUrl":"https://doi.org/10.33897/fujeas.v3i1.687","url":null,"abstract":" \u0000Now a day, the fast growth of Internet access and the adoption of smart digital technology has resulted in new cybercrime strategies targeting regular people and businesses. The Web and social activities take precedence in most aspects of their lives, but also poses significant social risks. Static and dynamic analysis are inefficient in detecting unknown malware in standard threat detection approaches. Virus makers create new malware by modifying current malware using polymorphic and evasion tactics in order to fool. Furthermore, by utilizing selection of features techniques to identify more important features and minimizing amount of the data, these Machine Learning models' accuracy can be increased, resulting in fewer calculations. In the previous study traditional machine learning approaches were used to detect Malware. We employed Cuckoo sandbox, a malware detection and analysis system for detection and categorization, in this study we provide a Machine Learning based Intrusion analysis system to calculate exact and on spot Intrusion classification. We integrated feature extraction and component selection from the file, as well as selecting the much higher quality, resulting in exceptional accuracy and cheaper computing costs. For reliable identification and fine-grained categorization, we use a variety of machine learning algorithms. Our experimental results show that we achieved good, classified accuracy when compared to state-of-the-art approaches. We employed machine learning techniques such as K-Nearest Neighbor, Random Forest, Support Vector Machine, and Decision Tree. Using the Random Forest classifier on 108 features, we attained the greatest accuracy of 99.37 percent. We also discovered that Random Forest outscored all other classic machine learning techniques during the procedure. These findings can aid in the exact and accurate identification of Malware families. \u0000 ","PeriodicalId":36255,"journal":{"name":"Iranian Journal of Botany","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83908135","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}
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