Current Issue : April-June Volume : 2023 Issue Number : 2 Articles : 5 Articles
Intrusion detection is one of the key research directions of network information security. In order to make up for the deficiencies of traditional security technologies such as firewall, encryption, and authentication, by analyzing the characteristics of network attacks and existing intrusion detection models, the advantages of triadic concept analysis and the application of fuzzy set theory in network intrusion detection are analyzed. The intrusion detection model FCTA based on triadic concept analysis is proposed, which promotes the further development of network intrusion detection. First, we analyze the characteristics of the data and use TF-IDF and Z-Score to normalize and standardize the data to construct a fuzzy triadic background containing quadratic characteristics. It is used to describe the triadic relationship between network connections, network connection characteristics, and intrusion types of network packets. Then, the (i)-induced operator is used to construct the fuzzy triadic concept set based on the fuzzy triadic background and transformed into a fuzzy attribute triadic concept set. Then, the new samples are classified by calculating the similarity between the new samples and the elements in the fuzzy attribute triadic concept set by using the Euclidean distance formula. In order to reduce the model space complexity, compression storage technology is adopted in the model building process.. Finally, by using the IDS-2018 dataset, the rationality and effectiveness of the FCTA model are demonstrated. The average accuracy and average intrusion detection rate of FCTA classification are much higher than BP neural network, SVM algorithm, and KNN algorithm, and the FCTA misjudgment rate is much lower than the BP neural network algorithm, the KNN algorithm, and the SVM algorithm; with the increase of data volume, the accuracy rate and intrusion detection rate increase significantly....
With the rapid advancement of society and the level of programming and the rapid development of computer technology, networking and information are playing an increasingly important role in the social life of the masses. Creating and developing a network information security monitoring system have become one of the most important ways to keep a computer running smoothly. Traditional information security systems cannot adapt to the ever-changing network environment. If only relying on it to maintain network security, it will be far from effective monitoring and defense. Based on the theory of network information security, this study analyzes the characteristics of network information and the information security monitoring technology at the current stage. Based on the background of big data era, a new type of computer network information security monitoring system is proposed. This system is compared with the traditional network information security monitoring system, and the performance and stability of the system are investigated, respectively. The experimental data show that the network information security monitoring system designed in this study can achieve more than 99% detection rate of external attacks in a network environment with a background traffic of 10M. Its false alarm rate is lower than 4%, and the false alarm rate is lower than 7%. The qualitative mean reaches 93.02%, indicating its good monitoring accuracy and stability. By popularizing it in the current network environment, it can effectively identify and defend information attacks and maintain the development of network information security....
All modern computer users need to be concerned about information system security (individuals and organisations). Many businesses established various security structures to protect information system security from harmful occurrences by implementing security procedures, processes, policies, and information system security organisational structures to ensure data security. Despite all the precautions, information security remains a disaster in Tanzania’s learning institutions. The fundamental issue appears to be a lack of awareness of crucial information security factors. Various companies have different security issues due to differences in ICT infrastructure, implementations, and usage. The study focuses on identifying information system security threats and vulnerabilities in public higher learning institutions in Tanzania, particularly the Institute of Accountancy Arusha (IAA). The study involved all employees of IAA, academics, and other supporting staff, which totalled 302, and the sample size was 170. The study utilised a descriptive research design, where the quantitative methodology was used through a five-point Likert scale questionnaire, and found that key factors that affect the security of information systems at IAA include human factors, policy-related issues, work environment and demographic factors. The study proposed regular awareness and training programs; an increase in women’s awareness of information system security; proper policy creation and reviews every 4 years; promote actions that lessen information system security threats and vulnerabilities, and the creation of information system security policy documents independently from ICT policy....
In the present context, the deep learning approach is highly applicable for identifying cyber-attacks on intrusion detection systems (IDS) in cyber-physical security systems. As a key part of network security defense, cyber-attacks can change and penetrate the security of the network system, then, the role of an IDS is to detect suspicious behaviors and act appropriately to protect the network from the onset of attacks. Machine learning and deep learning techniques are important for current intrusion detection systems. However, traditional intrusion detection systems are far from being able to quickly and accurately identify complex and diverse network attacks and obtained low accuracy and detection rates, thus, these methods frequently fail to manage big amounts of data in a vast network infrastructure and utilize a lot of features leads to poor performance. For addressing these issues and improving the accuracy and scalability, in this paper, we have implemented the deep learning method based on a new approach multilayer long short-term memory (LSTM) model for detecting attacks on a network. The novelty of the proposed scheme is that the optimum multilayer architecture is built to achieve maximum accuracy in the network architecture in order to boost performance using stacking multiple layers of LSTM cells in a more effective manner, and better stability to perform consistently in both binary classification and multiclass classification on NSL-KDD datasets. Experimental tests with KDDTest + datasets show that the proposed multilayer LSTM model provides outstanding results with 95% and 96% accuracy, respectively, in binary and multiclass classification. In order to deal with actual datasets and obtain good performance in the network design, our optimum multilayer architecture must be put into practice in order to execute real-time applications. Therefore, the results are better and more robust than the existing state-of-the-art methods....
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