Mesopotamian Journal of Cyber Security最新文献

{"title":"Machine Learning and Data Mining Methods for Cyber Security: A Survey","authors":"Ziaul Hasan1, Hassan r. Mohammad, Maka Jishkariani","doi":"10.58496/mjcs/2022/006","DOIUrl":"https://doi.org/10.58496/mjcs/2022/006","url":null,"abstract":"Data mining and machine learning (ML) methods are used more than ever in cyber security. The use of machine learning (ML) is one of the potential solutions that may be successful against zero-day attacks, starting with categorising IP traffic and filtering harmful traffic for intrusion detection. In this field, certain published systematic reviews were taken into consideration. Recent systematic reviews may incorporate older and more recent works in the topic of investigation.. Both security professionals and hackers use data mining capabilities. Applications for data mining may be used to analyze programme activity, surfing patterns, and other factors to identify potential cyber-attacks in the future. The new study uses statistical traffic features, ML, and data mining approaches. This research performs a concentrated literature review on machine learning and its usage in cyber analytics for email filtering, traffic categorization, and intrusion detection. Each approach was identified, and a summary was provided based on the relevancy and quantity of citations. Some well-known datasets are also discussed since they are a crucial component of ML techniques. On when to utilize a certain algorithm is also offered some advice. Four ML algorithms have been evaluated on MODBUS data gathered from a gas pipeline. Using ML algorithms, other assaults have been categorized, and then the effectiveness of each approach has been evaluated. This study demonstrates the use of ML and data mining for threat research and detection, focusing on malware detection with high accuracy and short detection times.","PeriodicalId":369414,"journal":{"name":"Mesopotamian Journal of Cyber Security","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128056383","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}

{"title":"Detection of False Data Injection Attack using Machine Learning approach","authors":"","doi":"10.58496/mjcs/2022/005","DOIUrl":"https://doi.org/10.58496/mjcs/2022/005","url":null,"abstract":"The \"False Data Injection\" (FDI) attack is one of the significant security risks that the deep neural Network is susceptible to. The purpose of the FDI attacks is to deceive industrial platforms by faking sensor readings. considered a few relevant systematic reviews that have been previously published. Recent systematic reviews may include both older and more recent works on the topic. Therefore, I restricted myself to recently published works. Specifically, we analyzed data from 2016-2021 for this work. Attacks using FDI have effectively beaten out traditional threat detection strategies. In this paper, we provide an innovative auto-encoder-based technique for FDI attack detection (AEs). use of the temporal and spatial correlation of sensor data, which may be used to spot fake data. Additionally, the fabricated data are denoised using AEs. Performance testing demonstrates that our method is effective in finding FDI attacks. Additionally, it performs much better than a similar technique based on a support vector machine. The ability of the denoising AE data cleaning method to recover clean data from damaged (attacked) data is also shown to be quite strong.","PeriodicalId":369414,"journal":{"name":"Mesopotamian Journal of Cyber Security","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126977930","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}

{"title":"The AI algorithm for text encryption using Steganography","authors":"","doi":"10.58496/mjcs/2022/003","DOIUrl":"https://doi.org/10.58496/mjcs/2022/003","url":null,"abstract":"The human being sought to find a human variety of techniques to assure data access with complete confidentiality. The transition from the use of. regular text and audio data to digital media has improved data access and transport. It has become relatively simple to intercept data sent across networks or get access to a variety of machines. Steganography is the study of concealing the existence of communication by embedding hidden data in multimedia payloads such as text, image, audio, and video. This project investigates how to improve steganography by merging text and image production to provide invisible encryption, security, and robustness in digital images. Text hide into another text using quality in the proposed system include Mean Square Error (MSE), Normalized Correlation (NC), and Normalized Cross–correlation Mean Squared Error, Histogram Analysis, Standard Deviation, and Statistical Test and Analysis. The algorithm of our suggested system seems to meet the greatest standards of security, perceptions, and capability. Using the standard of ASCII Control characters for a cover word, the procedure for using the processed system is supplied from the cover sentence. The same method is used for other words from different cover sentences, given the line numbers of each cover sentence and Stego sentence.","PeriodicalId":369414,"journal":{"name":"Mesopotamian Journal of Cyber Security","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129686792","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}

{"title":"SQL Injection Attack Detection Using Machine Learning Algorithm","authors":"T. Muhammad, Hamayoon Ghafory","doi":"10.58496/mjcs/2022/002","DOIUrl":"https://doi.org/10.58496/mjcs/2022/002","url":null,"abstract":"One of the most widely recognised cyber-assaults against web-based application weaknesses is the\u0000structured query language injection attack (SQLIA), which is utilised to execute unlawful information\u0000control language, evade confirmation strategies, and access confined information. Some published\u0000systematic reviews were considered in this area. Older and more current papers in the field are often\u0000included in more recent systematic reviews. As a result, all of the publications we looked at were\u0000recent. I used data from 2012 to 2020 for the present analysis. There are a few techniques and systems\u0000for identifying and forestalling SQLIA, including encryption, XML, design coordinating, parsing, and\u0000machine learning. Guarded coding is utilised to apply Machine Learning (ML) procedure, which has\u0000been shown to be significant for SQLIA alleviation. The machine learning approach needs a ton of\u0000information to prepare models really and support a few attack types. An extremely difficult visually\u0000impaired SQL injection attack might be relieved utilizing ML procedures. An exploratory\u0000examination of Logistic Regression (LRN), Stochastic Gradient Descent (SDG), Sequential Minimal\u0000Optimization (SMO), Bayes Network (BNK), Instance-Based Learner (IBK), Multilayer Perceptron\u0000(MLP), Naive Bayes (NBS), and J48 was completed in the Waikato Climate for Information\u0000Investigation. The presentation of the regulated learning grouping calculations was surveyed utilizing\u0000Wait (70%) and 10-crease Cross Validation appraisal methods to decide the best calculation.\u0000According to the findings of the Cross Validation approach, SMO, IBK, and J48 had accuracy values\u0000of 98.7785%, 98.4285%, and 98.2985%, respectively, while the Hold-Out technique revealed\u0000accuracy values of 98.7956%, 98.1526%, and 100 for SMO, IBK, and J48. In contrast, IBK and J48\u0000needed 10.15 seconds, 0.06 seconds, and 14.12 seconds, respectively, to create their models using the\u0000Cross Validation approach SMO, whereas they needed 9.71 seconds, 0.16 seconds, and 14.28\u0000seconds, respectively, to develop their models using the Hold-Out technique SMO. According to the\u0000results, IBK was selected as the classifier for SQLIA detection and prevention since it required the\u0000least amount of time to develop a model using the Cross Validation approach and performed better\u0000than other candidates in terms of accuracy, sensitivity, and specificity. For the best algorithm selection\u0000for predictive analytics, various performance assessment measures are also crucial in addition to\u0000accuracy.","PeriodicalId":369414,"journal":{"name":"Mesopotamian Journal of Cyber Security","volume":"175 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123723896","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}

{"title":"Cybersecurity Challenges in Smart Cities: An Overview and Future Prospects","authors":"Maad M. Mijwil","doi":"10.58496/mjcs/2022/001","DOIUrl":"https://doi.org/10.58496/mjcs/2022/001","url":null,"abstract":"Today, most governments in the world are considering establishing smart cities that work through the use of the latest technological means. Where smart cities are considered economically, socially and environmentally sustainable cities as they have the ability to develop sustainable development, increase the quality of life of citizens, increase the efficiency of available resources and active citizen participation with confidence and quickly. Nations are looking forward to creating a more profitable future for them by employing a set of main things, which are the economy, citizens, government, mobility, environment, and health. Smart cities are one of the main pillars that promote economic development in these nations. Smart cities have appeared in Japan, the UAE and Germany, where these cities constitute an excellent future environment in which they can live and are more suitable than ordinary cities. In this report, the most critical challenges that cybersecurity faces in preserving smart cities from hacking operations will be reviewed in general. This report concluded that there is a relationship between cybersecurity and smart cities, as such cities cannot be established without providing an appropriate electronic and physical security environment to protect these cities from attack and penetration by unauthorised or unknown individuals.","PeriodicalId":369414,"journal":{"name":"Mesopotamian Journal of Cyber Security","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130317358","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}

{"title":"Improved feature selection method for features reduction in intrusion detection systems","authors":"","doi":"10.58496/mjcs/2021/003","DOIUrl":"https://doi.org/10.58496/mjcs/2021/003","url":null,"abstract":"Many methods have been used to build intrusion detection system based on the intended aim to be achieved in with the selected method. The hybrid methods (more than one method) usually gives better results and accuracy. The recent developments and popularization of network & information technologies have necessitated the need for network information security. Human-based smart intrusion detection systems (IDSs) are built with the capability to either warn or intercept network intrusion; this is not possible with the conventional network security systems. However, most information security studies have focused on improvement of the effectiveness of smart network IDSs. This study used TLBO algorithm as a feature selection algorithm to choose the best subset features and SVM classifier to classify the packet if it is intrusion or normal packet, two machine learning datasets used to test the proposed algorithm, the results show that the proposed algorithm perform better than many of the existing work in IDS.","PeriodicalId":369414,"journal":{"name":"Mesopotamian Journal of Cyber Security","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133638163","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}

{"title":"Challenges and Future Directions for Intrusion Detection Systems Based on AutoML","authors":"","doi":"10.58496/mjcs/2021/004","DOIUrl":"https://doi.org/10.58496/mjcs/2021/004","url":null,"abstract":"","PeriodicalId":369414,"journal":{"name":"Mesopotamian Journal of Cyber Security","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133080784","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}

{"title":"Harmony Search for Security Enhancement","authors":"","doi":"10.58496/mjcs/2021/002","DOIUrl":"https://doi.org/10.58496/mjcs/2021/002","url":null,"abstract":"The honeyword system can be considered a mechanism for detecting passwords aiming to develop hashed password security via creating a password cracking easier to be detected. A lot of false passwords come with true passwords for forming the false and true passwords for all users. When a hacker logs in and uses a honeyword, a silent alarm trigger illustrates that the honeyword system could be compromised. A lot of mechanisms of honeyword generating have been submitted and all of them have a defect in the generation, a support shortage for every honeyword characteristic, and a slew of honeyword complications. HSA (harmony search algorithm), a metaheuristic intelligence algorithm getting inspiration from music, can be utilized here for offering a way to generate honeyword. The suggested mechanism of honeyword generating is enhancing the generating, enhancing every honeyword characteristic, and addressing the shortcomings of each prior approach. This paper is showing many previous mechanisms of honeyword generating, clarifying the proposed one, discussing every experimental finding, and comparing the novel mechanism of honeyword generating with the ones before it","PeriodicalId":369414,"journal":{"name":"Mesopotamian Journal of Cyber Security","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123521386","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}

{"title":"Theoretical Background of steganography","authors":"","doi":"10.58496/mjcs/2021/005","DOIUrl":"https://doi.org/10.58496/mjcs/2021/005","url":null,"abstract":"","PeriodicalId":369414,"journal":{"name":"Mesopotamian Journal of Cyber Security","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128688312","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}

{"title":"Intrusion Detection: A Review","authors":"","doi":"10.58496/mjcs/2021/001","DOIUrl":"https://doi.org/10.58496/mjcs/2021/001","url":null,"abstract":"Due to the processes involved in the electronic transformation of data, computer systems, and the Internet have led to significant security, privacy, and confidentiality issues in recent years. Intrusion detection systems (IDSs) are one of the most promising data and network protection tools; there are many approaches and techniques used for the detection of intrusions based on the intrusion type; in this paper, different types of intrusions are reviewed, and the different kinds of systems for detecting intrusions, challenges and future directions for researchers determined at the end of this paper.","PeriodicalId":369414,"journal":{"name":"Mesopotamian Journal of Cyber Security","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133327471","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}