Computer
Yasser A. Yasser; Ahmed T. Sadiq; Wasim AlHamdani
Abstract
Honeywords are fake passwords that are typically companions to the real password “sugarword.” The honeyword technique is a password cracking detection technique that works effectively to improve the security of hashed passwords by making password cracking simpler to detect. The password database ...
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Honeywords are fake passwords that are typically companions to the real password “sugarword.” The honeyword technique is a password cracking detection technique that works effectively to improve the security of hashed passwords by making password cracking simpler to detect. The password database will contain many honeywords for each user in the system. A silent alarm will trigger, indicating that the password database has been compromised if the hacker signs in using a honeyword. The honeychecker is a separate server in charge of recognizing the real password and raising the silent alarm. Many honeyword creation techniques have been presented previously. They all have limitations in the generating process, supporting characteristics, and strengths of honeyword. The bees algorithm, an optimization metaheuristic swarm intelligence algorithm, is used in this article to suggest a novel approach for generating honeywords. The proposed bee algorithm succeeded in addressing the limitations of the previous methods by enhancing the honeyword generating process, supporting the honeyword characteristics, and addressing the honeyword system problems. The most important characteristics of the honeyword (flatness, DoS resistance, and storage) were supported by the proposed method to present unconditionally flatness, strong DoS resistance, and moderate storage.
Computer
Mohammed Abduljabbar Ali; Abir Jaafar Hussain; Ahmed T. Sadiq
Abstract
Crowd detection has various applications nowadays. However, detecting humans in crowded circumstances is difficult because the features of different objects conflict, making cross-state detection impossible. Detectors in the overlapping zone may therefore overreact. The proposal uses the YOLO v5 (You ...
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Crowd detection has various applications nowadays. However, detecting humans in crowded circumstances is difficult because the features of different objects conflict, making cross-state detection impossible. Detectors in the overlapping zone may therefore overreact. The proposal uses the YOLO v5 (You Only Look Once) method to improve crowd recognition and counting. This algorithm is entirely accurate and detects things in real-time. The idea relies on edge enhancement and pre-processing to solve overlapping feature regions in the image and improve performance. The CrowdHuman data set is used to train YOLO v5. The system counts the number of humans in the image to detect a crowd. Before training, this model enhanced the image with several filters. The YOLO v5 algorithm distinguishes a person inside a crowd by utilizing the surrounding box on the head and overall body. Therefore, the number of head detection is x- coordinated compared to the body. Assume the detected heads outnumber the bodies. A square of the head will be extracted, but not a body square. Also, cropping the image reduces interference between human beings and enhances the edge features. Thus, YOLOv5 can detect it. The idea improves head and body detection by 2.17 and 4.1 percent, respectively.
Sura Sabah Rasheed; Ahmed T. Sadiq
Volume 21, Issue 2 , June 2021, , Page 132-142
Abstract
Social media have been increasing obviously and widely due to the fact that it is a mediafor users who express their emotions using reviews and comments on a variety of areas in life. In thepresent study, a modest model has been suggested for the assessment of service departments with theuse of reviews ...
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Social media have been increasing obviously and widely due to the fact that it is a mediafor users who express their emotions using reviews and comments on a variety of areas in life. In thepresent study, a modest model has been suggested for the assessment of service departments with theuse of reviews and comments in social media pages of those departments from various governorates.The utilization of the text mining for the sentiment classification has been used through collectingIraqi dialect reviews on service department pages on Facebook to be analyzed with the use of thesentiment analysis to track the emotions from the comments and posts. Those have been classifiedafter that to positive, neutral or negative comment with the use of the algorithms of Naive Bayesian,Rough Set Theory, and K-Nearest Neighbors. Out of 13 Iraqi capital (Baghdad) service departmentshave been tackled, it has been found that 11% of those departments had very good assessment, 18%from these service departments have good assessment, 21% from these service departments havemedium assessment, 24% from these service departments have acceptance assessment and 26% fromthese service departments have bad assessment. The results of the evaluation showed the poorservices provided by service departments in the capital Baghdad. Experimental results were helpfulfor the service departments in improving their work and programs had responded quickly andsufficiently to the customer demands.
