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Journal articles on the topic 'Security log'

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Published: 4 June 2025

Last updated: 15 July 2025

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Lobo, Colin. "Security Log Management." Network Security 2003, no. 11 (2003): 6–9. http://dx.doi.org/10.1016/s1353-4858(03)01106-1.

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Rajebhosale, Sagar Shankar, and Mohan Chandrabhan Nikam. "Development of Secured Log Management System Over Blockchain Technology." International Journal of Cyber Research and Education 1, no. 1 (2019): 38–42. http://dx.doi.org/10.4018/ijcre.2019010104.

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A log is a record of events that happens within an organization containing systems and networks. These logs are very important for any organization, because a log file will able to record all user activities. Due to this, log files play a vital role and contain sensitive information, and therefore security should be a high priority. It is very important to the proper functioning of any organization, to securely maintain log records over an extended period of time. So, management and maintenance of logs is a very difficult task. However, deploying such a system for high security and privacy of log records may be overhead for an organization and require additional costs. Many techniques have been designed for security of log records. The alternative solution for maintaining log records is using Blockchain technology. A blockchain will provide security of the log files. Log files over a Blockchain environment leads to challenges with a decentralized storage of log files. This article proposes a secured log management over Blockchain and the use of cryptographic algorithms for dealing the issues to access a data storage. This proposed technology may be one complete solution to the secure log management problem.

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Stabell-Kulø, Tage. "Security and log structured file systems." ACM SIGOPS Operating Systems Review 31, no. 2 (1997): 9–10. http://dx.doi.org/10.1145/250007.250009.

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Liu, Ruiyang. "Data Security Threats of Log Aggregation." SHS Web of Conferences 170 (2023): 01007. http://dx.doi.org/10.1051/shsconf/202317001007.

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The development of cloud computing and distributed technologies has shifted log management operations to an aggregated form. Diverse information devices as well as information systems perform data collection and status analysis through a unified log aggregation management system. However, with the increasingly diverse business forms, the threats confronted by log aggregation systems are also mounting, among which data leakage, data tampering, data loss, and security vulnerabilities dominate the main positions. Data leakage imposes a severe challenge to the data privacy of enterprises and users, data tampering misleads the information system of enterprises and even adversely affects the decision-making of enterprises, data loss devastates the past data of enterprises and impinges on the operation of enterprises, and security vulnerabilities inflict potential safety hazards to the information systems accessed to log aggregation.

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Xing, Yanbo. "Design of a Network Security Audit System Based on Log Data Mining." Wireless Communications and Mobile Computing 2022 (February 25, 2022): 1–7. http://dx.doi.org/10.1155/2022/6737194.

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The purpose of network security auditing is to safeguard network and information security through the assessment of network security vulnerabilities. Data mining is mainly used to mine potential information from massive amounts of log data, which can provide both accurate and valuable auxiliary data for network security auditing and relevant information for monitoring and management of terminals in different network environments. Firstly, the theory of data mining technology is explained, secondly, some data mining algorithms applied to network security audit are discussed, and finally, the design difficulties and functions of the network security audit system based on data mining are studied, aiming to provide reference for securing network and identifying hidden risks.

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Abbott, Robert G., Jonathan McClain, Benjamin Anderson, Kevin Nauer, Austin Silva, and Chris Forsythe. "Log Analysis of Cyber Security Training Exercises." Procedia Manufacturing 3 (2015): 5088–94. http://dx.doi.org/10.1016/j.promfg.2015.07.523.

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Casey, Donal. "Turning log files into a security asset." Network Security 2008, no. 2 (2008): 4–7. http://dx.doi.org/10.1016/s1353-4858(08)70016-3.

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Kim, Nan Ju, Hoon Jeong, Hye Jin Pyo, and Eui In Choi. "Security Framework Using Forensic Function and Log Management." Applied Mechanics and Materials 590 (June 2014): 752–55. http://dx.doi.org/10.4028/www.scientific.net/amm.590.752.

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The explosive development of the IT field, the number of structured and unstructured data is increasing in geometrical. Also, using these data a number of cyber-crime has occurred. So, the security threat in many areas is a serious problem.The importance of Big Data and analysis techniques has been developing.But, efforts about security are showing a low level relatively.In this paper, we are discussed about security threat and security technology. And, we proposed a new security technique that complements traditional security techniques.

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Subakti, Adityo Jaya. "Analysis of Lapan Security Access Based on Firewall Log in Center Eight." Jurnal Manajemen Informatika Medicom (JMI) 10, no. 2 (2022): 26–31. http://dx.doi.org/10.35335/jmi.v10i2.5.

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Analysis of Network Security Access Space Agency Based Firewall Log In LAPAN Center. Supervisor LIPUR SUGIYANTA, Ph.D and Drs. BACHREN ZAINI, M.Pd. Increased Denial Of Service attacks, and other types of computer network interference, making security an important issue to be considered by all those who take advantage of the presence of the virtual world today. LAPAN Center system along with the information contained within is no exception contained attacks on access computer network security. Such attacks can be prevented at an early stage by analyzing at each access that will go on network security in LAPAN. This research at Analysis of Network Access to know and learn access of network security in firewall log LAPAN Center. This research was conducted with several stages of observation and interviews to employees of LAPAN Center infrastructure. After the stage of observation and interviews, the researchers conducted further data collection and analysis of firewall logs. The study states that the analysis of security access network based firewall log in LAPAN Center in the form of six messages that occur when a network will pass through the firewall. It can be concluded that by doing some analysis phases of network security access based firewall log in LAPAN Center produces a record of every incident inside the firewall logs. The study states that the analysis of security access network based firewall log in LAPAN Center in the form of six messages that occur when a network will pass through the firewall. It can be concluded that by doing some analysis phases of network security access based firewall log in LAPAN Center produces a record of every incident inside the firewall logs. The study states that the analysis of security access network based firewall log in LAPAN Center in the form of six messages that occur when a network will pass through the firewall. It can be concluded that by doing some analysis phases of network security access based firewall log in LAPAN Center produces a record of every incident inside the firewall logs

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Kurniawan, Kabul, Andreas Ekelhart, Elmar Kiesling, Dietmar Winkler, Gerald Quirchmayr, and A. Min Tjoa. "VloGraph: A Virtual Knowledge Graph Framework for Distributed Security Log Analysis." Machine Learning and Knowledge Extraction 4, no. 2 (2022): 371–96. http://dx.doi.org/10.3390/make4020016.

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The integration of heterogeneous and weakly linked log data poses a major challenge in many log-analytic applications. Knowledge graphs (KGs) can facilitate such integration by providing a versatile representation that can interlink objects of interest and enrich log events with background knowledge. Furthermore, graph-pattern based query languages, such as SPARQL, can support rich log analyses by leveraging semantic relationships between objects in heterogeneous log streams. Constructing, materializing, and maintaining centralized log knowledge graphs, however, poses significant challenges. To tackle this issue, we propose VloGraph—a distributed and virtualized alternative to centralized log knowledge graph construction. The proposed approach does not involve any a priori parsing, aggregation, and processing of log data, but dynamically constructs a virtual log KG from heterogeneous raw log sources across multiple hosts. To explore the feasibility of this approach, we developed a prototype and demonstrate its applicability to three scenarios. Furthermore, we evaluate the approach in various experimental settings with multiple heterogeneous log sources and machines; the encouraging results from this evaluation suggest that the approach can enable efficient graph-based ad-hoc log analyses in federated settings.

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Park, Ji Su. "Efficient Analysis and Detection of Intelligent Security Threats in Cloud Environment." Journal of Internet Technology 25, no. 4 (2024): 629–35. http://dx.doi.org/10.70003/160792642024072504013.

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Recently, as cloud environments have spread, security technologies have strengthened preemptive defense technologies that predict and prevent various attacks. For example, security intelligence solutions, which have experienced considerable challenges, do not have sufficient reference data to operate, but many companies are introducing reference operational solutions. Among them, the analysis of the correlation between the log and parsing log heterogeneity requires considerable time and manpower. In this study, we create a rule for the security scenario based on parsing techniques to extract and parse the log of the log using only meaningful data and propose a method for purifying that can detect an intelligent security threat.

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Pan, Lei, and Huichang Zhu. "An Intelligent Framework for Log Anomaly Detection Based on Log Template Extraction." Journal of Cases on Information Technology 25, no. 1 (2023): 1–23. http://dx.doi.org/10.4018/jcit.330145.

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Log anomaly detection holds great significance in computer systems and network security. A large amount of log data is generated in the background of various information systems and equipment, so automated methods are required to identify abnormal behavior that may indicate security threats or system malfunctions. The traditional anomaly detection methods usually rely on manual statistical discovery, or match by regular expression which are complex and time-consuming. To prevent system failures, minimize troubleshooting time, and reduce service interruptions, a log template-based anomaly detection method has been proposed in this context. This approach leverages log template extraction, log clustering, and classification technology to timely detect abnormal events within the information system. The effectiveness of this method has been thoroughly tested and compared against traditional log anomaly detection systems. The results demonstrate improvements in log analysis depth, event recognition accuracy, and overall efficiency.

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Zou, Feng, Xingshu Chen, Yonggang Luo, Tiemai Huang, Zhihong Liao, and Keer Song. "Spray: Streaming Log Parser for Real-Time Analysis." Security and Communication Networks 2022 (September 6, 2022): 1–11. http://dx.doi.org/10.1155/2022/1559270.

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Logs is an important source of data in the field of security analysis. Log messages characterized by unstructured text, however, pose extreme challenges to security analysis. To this end, the first issue to be addressed is how to efficiently parse logs into structured data in real-time. The existing log parsers mostly parse raw log files by batch processing and are not applicable to real-time security analysis. It is also difficult to parse large historical log sets with such parsers. Some streaming log parsers also have some demerits in accuracy and parsing performance. To realize automatic, accurate, and efficient real-time log parsing, we propose Spray, a streaming log parser for real-time analysis. Spray can automatically identify the template of a real-time incoming log and accurately match the log and its template for parsing based on the law of contrapositive. We also improve Spray’s parsing performance based on key partitioning and search tree strategies. We conducted extensive experiments from such aspects as accuracy and performance. Experimental results show that Spray is much more accurate in parsing a variety of public log sets and has higher performance for parsing large log sets.

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Lee, Dong-Gun, Huy Kang Kim, and Eunjin Kim. "Study on security log visualization and security threat detection using RGB Palette." Journal of the Korea Institute of Information Security and Cryptology 25, no. 1 (2015): 61–73. http://dx.doi.org/10.13089/jkiisc.2015.25.1.61.

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Ali, Moch Dzikri Azhari, Asep Id Hadiana, and Melina Melina. "Securing Network Log Data Using Advance Encryption Standard Algorithm And Twofish With Common Event Format." International Journal of Quantitative Research and Modeling 5, no. 3 (2024): 341–53. https://doi.org/10.46336/ijqrm.v5i3.757.

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The rapid advancement of information technology demands enhanced security for data exchange in the digital world. Network security threats can arise from various sources, necessitating techniques to protect information transmitted between interconnected networks. Securing network logs is a critical step in strengthening overall network security. Network logs are records of activities within a computer network, including unauthorized access attempts, user activities, and other key events. This research focuses on developing a network log security system by comparing the performance of the Advanced Encryption Standard (AES) and Twofish algorithms, integrated with the Common Event Format (CEF) for encrypting network logs. Tests were conducted on network log datasets to evaluate system functionality and performance. Results indicate that the AES algorithm performs encryption and decryption faster than Twofish. Across five tests with different file sizes, AES took an average of 2.1386 seconds for encryption, while Twofish required 22.8372 seconds. For decryption, AES averaged 2.451 seconds compared to Twofish’s 26.140 seconds. The file sizes after encryption were similar for both algorithms. Regarding CPU usage, AES demonstrated higher efficiency. The average CPU usage during AES encryption was 0.5558%, whereas Twofish used 23.2904%. For decryption, AES consumed 0.4682% of CPU resources, while Twofish required 13.7598%. These findings confirm that AES is not only faster in both encryption and decryption but also more efficient in terms of CPU usage. This research provides valuable insights for optimizing network log security by integrating standardized log formats, like CEF, with appropriate encryption techniques, helping to safeguard against cyber threats.

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Kumar, Ravi, and Parvesh Kumar Chaudhary. "Network Security Enhancement using CTI and Log Analysis." International Journal of Engineering and Computer Science 7, no. 12 (2018): 24430–32. http://dx.doi.org/10.18535/ijecs/v7i12.03.

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Cyberattacks, ever increasing in severity, complexity and frequency are impacting the functioning of citizens, government, and businesses around the world. Protecting valuable intellectual property, business and personal information in digital form against theft, misuse, is an increasingly critical concern for everyone in the present digital era. The financial and reputational loss incurred due to Cyber-attacks motivate organizations to improve defensive measures to protect their organizational networks and information stored. This paper proposes an Cyber threat Intelligence (CTI) collection, log analysis and automated Threat alerting platform capable to analyze and respond to incidents that can lead to cyberattacks. The proposed system makes use of CTI received from Open Source intelligence (OSINT), Elasticsearch and Logstash to analyze, observe and generate alerts for malicious traffic/ activity in organization based on log analysis. At the same time an easy to understand visual representation can be made by the use using Kibana.

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AZIZI, Yassine, Mostafa AZIZI, and Mohamed ELBOUKHARI. "Log files Analysis Using MapReduce to Improve Security." Procedia Computer Science 148 (2019): 37–44. http://dx.doi.org/10.1016/j.procs.2019.01.006.

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Che, Jianhua, Weimin Lin, Yong Yu, and Wei Yao. "Optimized Hypergraph Clustering-based Network Security Log Mining*." Physics Procedia 24 (2012): 762–68. http://dx.doi.org/10.1016/j.phpro.2012.02.113.

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Onah, Fidelis I., and Samuel Ukoha. "A VPN Security Log Management and Visualization System." IUP Journal of Telecommunications 17, no. 1 (2025): 54–74. https://doi.org/10.71329/iupjtc/2025.17.1.54-74.

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Rasina Begum, B., and P. Chithra. "Cloud Based Secured Log Retrieval Using Fully Homomorphic Encryption." Asian Journal of Engineering and Applied Technology 9, no. 1 (2020): 13–17. http://dx.doi.org/10.51983/ajeat-2020.9.1.1084.

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Cloud computing provides a scalable platform for growing amount of data and processes that work on various applications and services by means of on-demand service providing. The storage services offered by clouds have become a new profit growth by providing a comparably cheapest, scalable, location-independent platform for managing users’ data. Client uses the cloud storage and enjoys the high-quality applications and services from a shared pool of configurable computing resources using cloud services. It reduces the trouble of local data storage and maintenance. But it gives severe security threats toward users’ outsourced data. Maintaining the log record securely over extended period is very crucial to the proper functioning of any organization. Log holds the record of system events specially user activities. It is the main target for malicious attackers. An attacker, breaking into a system would not try to leave traces of his or her activities behind. This proposed work mainly concentrates on the problem of log security in cloud computing system and using fully homomorphism encryption algorithm in the cloud computing log data.

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Journal, IJSREM. "Smart Log Analyzer for Anomaly Detection in Distributed Systems." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–12. http://dx.doi.org/10.55041/ijsrem28072.

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Abstract—This research work outlines a novel approach to fortify the stability and security of distributed systems through the implementation of an AI-enabled smart log analyzer. The escalating proliferation of distributed systems has led to an unprecedented surge in the volume of generated log data, which holds crucial insights into system performance, security, and dependability. However, the substantial challenges associated with managing this data—such as its overwhelming volume, diverse nature, and real-time processing requirements—have posed significant hurdles. The proposed AI-enabled smart log analyzer, detailed in this report, harnesses the power of advanced machine learning and natural language processing techniques to address these challenges effectively. The methodology is struc- tured into three fundamental phases, namely, Data Preprocessing, Anomaly Detection, and Clustering. The Data Preprocessing phase encompasses the collection, parsing, filtering, and feature extraction of log data. Anomaly Detection integrates machine learning models to discern various anomalies, encompassing irregular access patterns, log flooding, error messages, suspicious content, and outliers in resource requests. The Clustering phase categorizes log entries into meaningful groups based on attributes such as log level, component, event, error code, and resource usage, facilitating a comprehensive understanding of system behavior. This holistic approach holds the promise of significantly enhancing system stability and security. Index Terms—Anomaly Detection, Clustering, Data Prepro- cessing, Distributed Systems, Log analyzer, System security, System stability

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Wishah, Raed, and Azmi Shawkat Abdulbaqi. "Security and integrity of medical log records management in the cloud storage." Journal of Discrete Mathematical Sciences & Cryptography 26, no. 7 (2023): 1961–70. http://dx.doi.org/10.47974/jdmsc-1784.

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Logs of medicinal events in Cloud Computing management have become crucial, particularly for medical event logs. Medical auditing, medical analysis, medical evidence, forensic medicine, and other reasons need the safe storage of medical log data in any hospital or medical facility for a certain time. At each level of the medical record management process, including the log creation stage, the transmission phase, and the storage step, the integrity and security of the medical Log record data are maintained in this work. Furthermore, medical log records often include patient information that should not be made public (sensitive information). This paper uses a Secure Provisional Cloud Log Records Management Algorithm (SPCLRMA) approaches to ensure security while preserving medical transaction history in the cloud over time. Security for TSCMEL is ensured in this study by encrypting medical log data before it is saved in the cloud storage (CldStg). They’re also kept in packs for convenient access. This project was created utilizing the Java programming language and Microsoft Visual Studio.

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Anton, Alin-Adrian, Petra Csereoka, Eugenia Ana Capota, and Răzvan-Dorel Cioargă. "Enhancing Syslog Message Security and Reliability over Unidirectional Fiber Optics." Sensors 24, no. 20 (2024): 6537. http://dx.doi.org/10.3390/s24206537.

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Standard log transmission protocols do not offer a robust way of segregating the log network from potential threats. A secure log transmission system and the realization of a data diode using affordable components are proposed. Unidirectional data flow prevents unauthorized access and eavesdropping, ensuring the integrity and confidentiality of sensitive log data. The system uses an encryption protocol that requires that the upstream and the downstream of the data diode are perfectly synchronized, mitigating replay attacks. It has been shown that message amplification can mitigate UDP packet loss, but this is only required when the data diode traffic is congested. The implementation of the encryption algorithm is suitable for resource-constrained devices and it has been shown to produce random-looking output even on a reduced number of rounds when compared to the parent cipher. Several improvements have been made to the original encryption algorithm for which an actual implementation was missing. Free software and datasets have been made available to reproduce the results. The complete solution is easy to reproduce in order to secure the segregation of a log network inside any scenario where logging is required by the law and log tampering must be prevented.

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Daniel, A., and I.A Suleiman. "Design and implementation of computer based security and monitoring system for forensic experts." Journal of Scientific and Engineering Research 10, no. 8 (2023): 28–39. https://doi.org/10.5281/zenodo.10464142.

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<strong>Abstract </strong>Log is an important document produced and retained by the computer system, it records a large number of criminals using computers to commit crimes, and it’s a very important source of clues and evidence against computer crime. To well using log to implement computer forensics, there are two problems that need to be solved: one is in a timely manner to the log system protection, in accordance with the procedure of computer forensics to extract the log; second is how to log analysis, find out the crime of "traces", as valid evidence demonstrating to the court. In this project, the process and steps of computer forensics technology are discussed. And existing problem in this project, the status of the computer forensics technology and research of log, analyzes the computer systems of all kinds of log files and format, proposed a relatively perfect supporting computer forensics security audit log method, and according to our current level of technology, design a more suitable for law enforcement agencies in the application of the computer log forensics system.

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Qing, Xia. "Log-Based Network Security Audit System Research and Design." Advanced Materials Research 129-131 (August 2010): 1426–31. http://dx.doi.org/10.4028/www.scientific.net/amr.129-131.1426.

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This paper presents a security protection model of logs from the aspect of the system security architecture and technical feasibility, describes complete system architecture according to the system design objects, and gives the design process of each module in this system. Finally the proposed algorithm for association rules mining is experimentally tested in the network security audit system and there are satisfactory results.

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Zhao, Chenchen. "API Common Security Threats and Security Protection Strategies." Frontiers in Computing and Intelligent Systems 10, no. 2 (2024): 29–33. http://dx.doi.org/10.54097/k5djs164.

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This study analyzes the core role of APIs in modern digital ecology and the security threats they face, such as information leakage and overstepping access, and explores their security risks for technologies such as RESTful and GraphQL. It proposes to use OAuth/JWT authentication mechanism to strengthen access control, adopt HTTPS/TLS to secure data transmission, and combine with API gateway to defend against DDoS attacks. It also emphasizes the importance of fine-grained privilege management and log auditing. The study provides strategic guidance for improving API security protection and looks forward to the trend of intelligent protection.

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Trivedi, Devharsh, Aymen Boudguiga, Nesrine Kaaniche, and Nikos Triandopoulos. "SigML++: Supervised Log Anomaly with Probabilistic Polynomial Approximation." Cryptography 7, no. 4 (2023): 52. http://dx.doi.org/10.3390/cryptography7040052.

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Security log collection and storage are essential for organizations worldwide. Log analysis can help recognize probable security breaches and is often required by law. However, many organizations commission log management to Cloud Service Providers (CSPs), where the logs are collected, processed, and stored. Existing methods for log anomaly detection rely on unencrypted (plaintext) data, which can be a security risk. Logs often contain sensitive information about an organization or its customers. A more secure approach is always to keep logs encrypted (ciphertext). This paper presents “SigML++”, an extension of “SigML” for supervised log anomaly detection on encrypted data. SigML++ uses Fully Homomorphic Encryption (FHE) according to the Cheon–Kim–Kim–Song (CKKS) scheme to encrypt the logs and then uses an Artificial Neural Network (ANN) to approximate the sigmoid (σ(x)) activation function probabilistically for the intervals [−10,10] and [−50,50]. This allows SigML++ to perform log anomaly detection without decrypting the logs. Experiments show that SigML++ can achieve better low-order polynomial approximations for Logistic Regression (LR) and Support Vector Machine (SVM) than existing methods. This makes SigML++ a promising new approach for secure log anomaly detection.

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Putra, Willy Permana, Renol Burjulius, Muhammad Anis Al Hilmi, and A. Sumarudin. "Implementasi Sistem Manajemen Log untuk Penanggulangan Serangan Server dengan SIEM." IKRA-ITH Informatika : Jurnal Komputer dan Informatika 8, no. 3 (2024): 23–30. http://dx.doi.org/10.37817/ikraith-informatika.v8i3.4359.

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In the current digital era, information security has become a primary focus for organizationsworldwide. Rapid technological advancements have brought significant benefits but alsointroduced increasingly sophisticated cyber threats and attacks. One approach to addressing thesechallenges is through Security Information and Event Management (SIEM). SIEM integratesSecurity Information Management (SIM) and Security Event Management (SEM) to collect,analyze, and report security data from various network sources, enabling more effective detection,response, and management of security incidents. This study focuses on handling server attacksusing Wazuh SIEM as an early warning system. The methodology involves setting up a networktopology to detect Distributed Denial of Service (DDoS) attacks using SIEM, collecting andanalyzing log data, correlating data to identify threats, and responding to detected threats. Theresults indicate that SIEM is crucial in modern cybersecurity, providing real-time threat detectionand response capabilities. The system successfully detected and blocked 42 attacks during thetrial. In conclusion, SIEM offers greater security visibility and control, enabling organizations todetect and respond to complex security threats efficiently and effectively. Modern SIEM systems,equipped with advanced analytics and machine learning, can identify anomaly patterns and newthreats, thus strengthening an organization's cybersecurity defenses.

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Fan, Zhijie, Bo Yang, Jing Peng, Bingsen Pei, Changsong Zheng, and Xin Li. "Dynamic Adaptive Mechanism Design and Implementation in VSS for Large-Scale Unified Log Data Collection." International Journal of Information Security and Privacy 18, no. 1 (2024): 1–26. http://dx.doi.org/10.4018/ijisp.349569.

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This paper studies the collection of large-scale log data of information system and puts forward a dynamic adaptive mechanism for large-scale unified log data collection. Furthermore, we design and implement our method for pan-government industry safety operation management platform. The data flow processing architecture based on message queue is adopted to realize the decoupling of log collection, log processing and log reporting. The traffic peak clipping technology of message queue is adopted to ensure the safety and reliability of log transmission. According to the characteristics of log traffic, a design mode supporting dynamic adjustment of consumption group is proposed to meet the high-performance requirements of the system. The whole system can meet the centralized analysis, security threat perception and intelligent analysis of various security data. Meanwhile, we analyzed and compared with the traditional open-source log collection technology, our proposed method and system has clear advantages.

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Malec, Przemysław, and Anna Piwowar. "Detecting Security Violations Based on Multilayered Event Log Processing." Journal of Telecommunications and Information Technology, no. 4 (December 30, 2015): 30–36. http://dx.doi.org/10.26636/jtit.2015.4.981.

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The article proposes a log analysis approach to detection of security violations, based on a four layer design. First layer, named the event source layer, describes sources of information that can be used for misuse investigation. Transport layer represents the method of collecting event data, preserving it in the form of logs and passing it to another layer, called the analysis layer. This third layer is responsible for analyzing the logs’ content, picking relevant information and generating security alerts. Last layer, called normalization layer, is custom software which normalizes and correlates produced alerts to raise notice on more complex attacks. Logs from remote hosts are collected by using rsyslog software and OSSEC HIDS with custom decoders and rules is used on a central log server for log analysis. A novel method of handling OSSEC HIDS alerts by their normalization and correlation is proposed. The output can be optionally suppressed to protect the system against alarm flood and reduce the count of messages transmitted in the network.

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Zawoad, Shams, Marjan Mernik, and Ragib Hasan. "Towards building a forensics aware language for secure logging." Computer Science and Information Systems 11, no. 4 (2014): 1291–314. http://dx.doi.org/10.2298/csis131201051z.

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Trustworthy system logs and application logs are crucial for digital forensics. Researchers have proposed different security mechanisms to ensure the integrity and confidentiality of logs. However, applying current secure logging schemes on heterogeneous formats of logs is tedious. Here, we propose Forensics Aware Language (FAL), a domain-specific language (DSL) through which we can apply a secure logging mechanism on any format of logs. Using FAL, we can define log structure, which represents the format of logs and ensures the security properties of a chosen secure logging scheme. This log structure can later be used by FAL to serve two purposes: it can be used to store system logs securely and it will help application developers for secure application logging by generating the required source code.

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Singh, Sweta, and Prashant Shukla. "Log File Compression and its Security in Web Server." International Journal of Computer Applications 151, no. 3 (2016): 32–36. http://dx.doi.org/10.5120/ijca2016911715.

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Kahonge, Andrew Mwaura, William Okello-Odongo, Evans K. Miriti, and Elisha Abade. "Web Security and Log Management: An Application Centric Perspective." Journal of Information Security 04, no. 03 (2013): 138–43. http://dx.doi.org/10.4236/jis.2013.43016.

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Pathak, Purvi, Kumar Rangasamy, and Theophilus Selvaraj. "Security Analytics and Benchmarking Log Aggregation in the Cloud." EAI Endorsed Transactions on Cloud Systems 3, no. 11 (2018): 154464. http://dx.doi.org/10.4108/eai.11-4-2018.154464.

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Håstad, Johan, and Mats Nåslund. "The security of all RSA and discrete log bits." Journal of the ACM 51, no. 2 (2004): 187–230. http://dx.doi.org/10.1145/972639.972642.

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Forte, Dario. "The importance of log files in security incident prevention." Network Security 2009, no. 7 (2009): 18–20. http://dx.doi.org/10.1016/s1353-4858(09)70090-x.

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Forte, Dario, and Richard Power. "Guaranteeing convergence in security management with consolidated log management." Computer Fraud & Security 2008, no. 7 (2008): 5–6. http://dx.doi.org/10.1016/s1361-3723(08)70110-6.

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Luo, Zhengping, Zhe Qu, Tung Nguyen, Hui Zeng, and Zhuo Lu. "Security of HPC Systems: From a Log-analyzing Perspective." ICST Transactions on Security and Safety 6, no. 21 (2019): 163134. http://dx.doi.org/10.4108/eai.19-8-2019.163134.

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Sidorova, Diana N., and Evgeniy N. Pivkin. "Algorithms and methods of data clustering in the analysis of information security event logs." Digital technology security, no. 1 (March 30, 2022): 41–60. http://dx.doi.org/10.17212/2782-2230-2022-1-41-60.

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Security event log files give an idea of the state of the information system and allow you to find anomalies in user behavior and cybersecurity incidents. The existing event logs (application, system, security event logs) and their division into certain types are considered. But automated analysis of security event log data is difficult because it contains a large amount of unstructured data that has been collected from various sources. Therefore, this article presents and describes the problem of analyzing information security event logs. And to solve this problem, new and not particularly studied methods and algorithms for data clustering were considered, such as Random forest (random forest), incremental clustering, IPLoM algorithm (Iterative Partitioning Log Mining - iterative analysis of the partitioning log). The Random forest algorithm creates decision trees for data samples, after which it is provided with a forecast for each sample, and the best solution is selected by voting. This method reduces overfitting by averaging the scores. The algorithm is also used in such types of problems as regression and classification. Incremental clustering defines clusters as groups of objects that belong to the same class or concept, which is a specific set of pairs. When clusters are defined, they can overlap, allowing for a degree of "fuzziness for samples" that lie at the boundaries of different clusters. The IPLoM algorithm uses the unique characteristics of log messages to iteratively partition the log, which helps to extract message types efficiently.

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Copstein, Rafael, Egil Karlsen, Jeff Schwartzentruber, Nur Zincir-Heywood, and Malcolm Heywood. "Exploring syntactical features for anomaly detection in application logs." it - Information Technology 64, no. 1-2 (2022): 15–27. http://dx.doi.org/10.1515/itit-2021-0064.

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Abstract In this research, we analyze the effect of lightweight syntactical feature extraction techniques from the field of information retrieval for log abstraction in information security. To this end, we evaluate three feature extraction techniques and three clustering algorithms on four different security datasets for anomaly detection. Results demonstrate that these techniques have a role to play for log abstraction in the form of extracting syntactic features which improves the identification of anomalous minority classes, specifically in homogeneous security datasets.

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Chae, Hyun Tak, and Sang-Jin Lee. "Security Policy Proposals through PC Security Solution Log Analysis (Prevention Leakage of Personal Information)." Journal of the Korea Institute of Information Security and Cryptology 24, no. 5 (2014): 961–68. http://dx.doi.org/10.13089/jkiisc.2014.24.5.961.

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G.L.D., Maduranga. "Network Security Enhancement through Effective Log Analysis using Elk for Small and Medium-Sized Business Environments." Journal of Network Security and Data Mining 4, no. 1 (2021): 1–7. https://doi.org/10.5281/zenodo.4700045.

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Security management is a critical activity for any organization. Modern security information and event management systems (SIEM) provide a direct approach to identify and prevent cyber-attacks. The commercial SIEM solutions are considerably expensive and also the expected performance will not be achieved by small-scale companies. Integration of Elasticsearch, Logstash, Kibana (ELK stack acronym for three open-source projects) will be facilitated to develop a free SIEM solution. Hence, this research suggests that the application of a more specific SIEM solution for a regular operation could be advantageous in terms of performance and productivity.

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Lakshmi Narasimha, Lakshmi Narasimha. "Logs-The First Pillar of Observability." International Journal of Multidisciplinary Research and Growth Evaluation. 2, no. 2 (2021): 302–6. https://doi.org/10.54660/.ijmrge.2021.2.2.302-306.

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Logs are considered the foundation of observability. Logs provide a detailed record of system events, application behavior, and security incidents. As the first pillar of observability, logs play a critical role in helping IT teams understand the health of the systems. This paper explores the different types of logs namely application, system, security, network, and cloud logs. It highlights their importance in modern IT environments. It also examines the challenges of log management, including data volume, noise reduction, real-time analysis, and security concerns. The paper also provides some best practices for effective logging, such as structured logging, centralized log management, and AI-driven log analysis. This paper serves as a comprehensive guide to understanding logs and their role in the broader observability strategy.

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Azizi, Yassine, Mostafa Azizi, and Mohamed Elboukhari. "Log Data Integrity Solution based on Blockchain Technology and IPFS." International Journal of Interactive Mobile Technologies (iJIM) 16, no. 15 (2022): 4–15. http://dx.doi.org/10.3991/ijim.v16i15.31713.

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Threats towards information systems have continued to increase and become more sophisticated, making security approaches a necessity for all types of organizations to ensure their protection. To implement an appropriate computer security policy, it is necessary to efficiently exploit the data that has become a valuable asset for these security systems, provided it is well used, controlled and monitored.In this paper, we focus on developing a decentralized solution based on Blockchain technology and IPFS (InterPlanetary File System) that can maintain and ensure the integrity of log files and sensitive information. The obtained results are promising, we obtained a distributed ledger of all log file transactions in a chronological sequence, which was shared among all Ethereum participants, allowing us to verify the log files' integrity, validity, and auditability throughout their life cycle.

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S. Madhumitha and Ms. N. Vaishnavi. "Banking Security System Using Cyber Security." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 1184–90. https://doi.org/10.32628/cseit25112451.

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The goal of the Intelligent Banking Security System project is to create an advanced security system that protects customer data and banking operations by utilizing cutting-edge technologies. Conventional security systems are insuffici ent since cyber attacks are becoming more complex. To provide a dynamic and efficient security system, the system integrates end-to-end encryption, biometric identification, machine learning, and artificial intelligence (AI). The AI-powered fraud detection engine continuously analyzes transaction patterns to identify unusual activity and stop impending fraud in real time. While MFA offers additional security, biometric authentication using fingerprint scanning and face recognition safely verifies the user. Blockchain technology provides transparent and unchangeable transaction log security. The technology of blockchain is used to secure transaction logs with transparency and immutability. The system would be capable of real-time monitoring of threats, anomaly detection, and automated responses to incidents so that security violations can be resolved quickly. This project would serve the purpose not only of increasing security but also of building trust among customers, ensuring regulatory compliance, and ensuring a secure and efficient banking experience.

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Madan, Mohan, Malviya Aadarsh, and Mishra Anuranjan. "Importance of Security in Big Data Log Files on Cloud." International Journal of Engineering and Advanced Technology (IJEAT) 11, no. 1 (2021): 51–54. https://doi.org/10.35940/ijeat.A3138.1011121.

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Today cloud computing is a very popular technology, and many people use this technology in many ways. it's important to have it safe. This technology was primarily used to keep data safer and safer in the cloud, so in this article we suggest a security framework for large data logs in the cloud. There are many and many risks that threaten the integrity of this information in the great information. Therefore, in line with the development of technology, the level of security has also increased significantly over the years. Various technology techniques access several online activities, such as interaction with different internet sites and services, making the web more accessible to their plug-ins. As a result, these activities have created a global platform for malicious activities to add these devices that expose large data logs harmful attacks. Sky system is an online platform that requires proper security integration. In addition, the current state of online security threatens high data in the cloud, which has affected the performance and service model.

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Negi, Shalabh, and Jayanthi R. "SECURITY IMPROVEMENT AND TRUST ENHANCEMENT IN CLOUD-BASED SERVICES." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (2017): 481. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.20525.

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Management of trust is one of the most challenging parts in cloud computing. Cloud service’s features such as distributed, dynamic and nontransparentintroduces several challenges for availability, privacy, and security. A method is proposed to have trust as a service between cloud serviceprovider and customer by creating a cloud armor. This proposed methodology is used to make cloud server end and customer end system secure ofany intrusion to their privacy. In this methodology, we will have a user usage pattern log as ever user has its own unique way of using his/her system;hence, in case if any odd user pattern is being encountered it will automatically block the system and simultaneously shoots a message as well as a callto the user notifying him/her about this unauthorized access. This log is set a various small pattern log, for example, keylog, preferable website visited,and time of accessing the system. Further, we have secured these logs using Rivest-Shamir-Adleman algorithm with very large key size.

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Zhong, Ming, Yajin Zhou, and Gang Chen. "A Security Log Analysis Scheme Using Deep Learning Algorithm for IDSs in Social Network." Security and Communication Networks 2021 (March 23, 2021): 1–13. http://dx.doi.org/10.1155/2021/5542543.

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Due to the complexity of the social network server system, various system abnormalities may occur and in turn will lead to subsequent system failures and information losses. Thus, to monitor the system state and detect the system abnormalities are of great importance. As the system log contains valuable information and records the system operating status and users’ behaviors, log data in system abnormality detection and diagnosis can ensure system availability and reliability. This paper discloses a log analysis method based on deep learning for an intrusion detection system, which includes the following steps: preprocess the acquired logs of different types in the target system; perform log analysis on the preprocessed logs using a clustering-based method; then, encode the parsed log events into digital feature vectors; use LSTM-based neural network and log collect-based clustering methods to learn the encoded logs to form warning information; lastly, trace the source of the warning information to the corresponding component to determine the point of intrusion. The paper finally implements the proposed intrusion detection method in the server system, thereby improving the system’s security status.

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Jin, Jingquan, and Xin Lin. "Web Log Analysis and Security Assessment Method Based on Data Mining." Computational Intelligence and Neuroscience 2022 (August 25, 2022): 1–9. http://dx.doi.org/10.1155/2022/8485014.

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Web content mining describes the classification, clustering, and attribute analysis of a large number of text documents and multimedia files on the web. Special tasks include retrieval of data from the Internet search engine tool W; structured processing and analysis of web data. Today’s blog analysis has security concerns. We do experiments to investigate its safety. Through experiments, we draw the following conclusions: (1) Web log extraction can use efficient data mining algorithms to systematically extract logs from web servers, then determine the main access types or interests of users, and then to a certain extent, based on the discovered user patterns, analyze the user’s access settings and behavior. (2) No matter in the test set or the mixed test set, the curve value of deep mining is very stable, the curve value has been kept at 0.95, and the curve value of fuzzy statistics method and quantitative statistics method is stable within the interval of 0.90–095. The results also show that the data mining method has the highest identification accuracy and the best security performance. (3) Web usage analysis requires data abstraction for pattern discovery. This data abstraction can be achieved through data preprocessing, which introduces different formats of web server log files and how web server log data is preprocessed for web usage analysis. One of the most critical parts of the web mining field is web log mining. Web log mining can use powerful data mining algorithms to systematically mine the logs in the web server and then learn the user’s access or preferred interests and then conduct a certain degree of user preferences and behavior patterns according to the discovered user patterns. Based on the above analysis, the current web log analysis is faced with security problems. We conduct experiments to study to verify the security performance of web logs and draw conclusions through experiments.

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Mohan, Madan, Aadarsh Malviya, and Anuranjan Mishra. "Importance of Security in Big Data Log Files on Cloud." International Journal of Engineering and Advanced Technology 11, no. 1 (2021): 51–54. http://dx.doi.org/10.35940/ijeat.a3138.1011121.

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