i-2NIDS Novel Intelligent Intrusion Detection Approach for a Strong Network Security

Ennaji, Sabrine; Akkad, Nabil El; Haddouch, Khalid

doi:10.4018/ijisp.317113

Cited by 6 publications

(5 citation statements)

References 32 publications

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“…In order to further study the progressiveness of the model in this paper in campus network security intrusion detection, the model proposed in this paper is compared with the existing research. The design experiment compares the model in this paper with literature [21] and literature [22], and evaluates its defense attack effect through expert evaluation. The quantitative evaluation method is the hundred point system.…”

Section: Campus Network Security Intrusion Detection System Based On ...mentioning

confidence: 99%

“…For event generators, their purpose is to retrieve events from the computing environment and provide them to other parts of the system; For event analyzers, analyze the data and obtain analysis results; For the response unit, the functional unit of the actual response analysis results can change the file attributes, issue alarms, or disconnect. For event databases, it mainly stores all intermediate and final data, which can be simple text files or complex databases [21].…”

Section: Introductionmentioning

confidence: 99%

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Campus Network Security Intrusion Detection Based on Feature Segmentation and Deep Learning

Chen

2024

JCSANDM

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At present, the secure campus network strategy adopts technical means such as distinguishing applications and limiting them separately, but they have triggered other new problems, greatly affecting the unity of network resources and data. The relatively dispersed network architecture will inevitably limit the further development and expansion of the campus network. Therefore, when universities plan their networks, they must consider whether the network is safe, complete, smooth, and sustainable for smooth upgrading and development. In order to improve the effect of campus network security intrusion detection, this paper combines feature segmentation and deep learning technology to construct a campus network security intrusion detection model. To reduce the transmission time of query requirements in the grid, this paper improves the replica management mechanism and requires the information server to cache the Bloom Filter structure of nodes in its successor node list. Moreover, this paper uses the Compressed Bloom Filter algorithm to compress the Bloom Filter structure that needs to be transmitted, therefore reducing the network traffic generated during the update process of the Bloom Filter structure copy and avoiding network congestion. It also constructs a campus network security intrusion detection model based on feature segmentation and deep learning. Through experimental verification, the effectiveness of the system in intrusion detection, user evaluation, information processing, and other aspects is verified, and it has certain advantages compared to traditional algorithms. Through experimental research, it can be seen that the campus network security intrusion detection model based on feature segmentation and deep learning proposed in the paper can effectively improve the effect of campus network security monitoring. The method proposed in this article can not only be applied to campus network security, but also to the network security management of enterprises and other units, with certain scalability

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Section: Campus Network Security Intrusion Detection System Based On ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Campus Network Security Intrusion Detection Based on Feature Segmentation and Deep Learning

Chen

2024

JCSANDM

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“…Vocational schools should strengthen teacher training and induction training, train high-quality teachers, and provide a better guarantee for the development of network security. [9][10].…”

Section: Big Data Technologymentioning

confidence: 99%

Exploration of Network Security Professional Classroom in Higher Vocational Colleges in the Era of Big Data

2023

IJNDE

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With the rapid development of information technology, big data has gradually penetrated into all walks of life, and has had a profound impact on various fields. As higher vocational colleges, the classroom education of network security majors should also keep pace with the times and explore how to better cultivate network security talents in the era of big data. This paper mainly applies the factor analysis method to obtain the important index of network security classroom evaluation, and uses the questionnaire survey method to collect the student satisfaction data. According to the experimental and survey data, 88% of the students are satisfied with the environment of the training room. As to whether the course is combined with the reality, 68% of the students expressed satisfaction and high degree of union, and 30% thought it was general. Therefore, it is necessary to further explore this major.

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“…Furthermore, password attacks have become commonplace in the present era, with cybercriminals employing sophisticated methods like brute force attacks, statistical attacks, and dictionary attacks to compromise systems and gain access to user accounts. Password hashes, which represent strings generated from passwords, were once considered an effective security measure [6][7][8][9][10]. However, with technological advances, attackers have successfully exploited weaknesses in traditional hashing algorithms, underscoring the urgent need to find more robust and innovative solutions to secure passwords.…”

Section: Introductionmentioning

confidence: 99%

Efficient Braille Transformation for Secure Password Hashing

Touil,

Akkad,

Satori

et al. 2024

IEEE Access

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In this work, we propose a novel approach to enhancing the security of passwords before storing them in databases. Our method utilizes Braille transformation to encrypt the password after generating the corresponding hash. The hash is divided into multiple blocks, each representing a character treated as a transformation unit. Each character is then associated with its corresponding Braille code, which consists of 6 digits. To further enhance security, we randomly replace each occurrence of "0" in the generated string with one of the digits 7, 8, or 9. The final string, six times larger than the original hash, is then stored in the database. To evaluate our approach, we conducted several experiments and comparisons. The results demonstrate that Braille transformation is resistant to brute-force attacks, statistical attacks, and differential attacks. These results were justified using various evaluation criteria, such as execution time and memory space occupied. Braille transformation is susceptible to any modification made to the hash or the generated string, further reinforcing its security. Our Braille-based approach offers an effective solution to strengthen the security of database passwords. It provides advantages in terms of protection against different attacks and offers a robust evaluation based on relevant criteria.

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i-2NIDS Novel Intelligent Intrusion Detection Approach for a Strong Network Security

Cited by 6 publications

References 32 publications

Campus Network Security Intrusion Detection Based on Feature Segmentation and Deep Learning

Campus Network Security Intrusion Detection Based on Feature Segmentation and Deep Learning

Exploration of Network Security Professional Classroom in Higher Vocational Colleges in the Era of Big Data

Efficient Braille Transformation for Secure Password Hashing

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