Application of data mining technology in detecting network intrusion and security maintenance

In order to correct the deficiencies of intrusion detection technology, the entire computer and network security system are needed to be more perfect. This work proposes an improved k-means algorithm and an improved Apriori algorithm applied in data mining technology to detect network intrusion and security maintenance. The classical KDDCUP99 dataset has been utilized in this work for performing the experimentation with the improved algorithms. The algorithm’s detection rate and false alarm rate are compared with the experimental data before the improvement. The outcomes of proposed algorithms are analyzed in terms of various simulation parameters like average time, false alarm rate, absolute error as well as accuracy value. The results show that the improved algorithm advances the detection efficiency and accuracy using the designed detection model. The improved and tested detection model is then applied to a new intrusion detection system. After intrusion detection experiments, the experimental results show that the proposed system improves detection accuracy and reduces the false alarm rate. A significant improvement of 90.57% can be seen in detecting new attack type intrusion detection using the proposed algorithm.

An empirical study on vulnerability assessment and penetration detection for highly sensitive networks

With the advancement of internet and the emergence of network globalization, security has always been a major concern. During the trial operation, the management control platform discussed in this article included more than 600 network security vulnerabilities in the industry, with dozens of incidents, which were promptly dealt with and rectified, effectively improving the level of network security management and protection in the industry. As networks are very much vulnerable to denial of service attacks, much more emphasis has been given to security. By improving their network security, network administrators have often tried their best. To attempt penetration testing, it is the best way of ensuring the system security. With the development of information technology, the security requirement of information system is increasing day by day. The use of penetration testing technology is conducive to the realization of accurate positioning, accurate detection, and active alarm of security vulnerabilities, and the optimization of monitoring and rectification of the combination of network security management control system. Taking penetration testing technology as one of the core elements of management and control, the risk index model is optimized to make network security management controllable and efficient, and effectively achieve management and control objectives.

A Botnets Circumspection: The Current Threat Landscape, and What We Know So Far

Botnets have carved a niche in contemporary networking and cybersecurity due to the impact of their operations. The botnet threat continues to evolve and adapt to countermeasures as the security landscape continues to shift. As research efforts attempt to seek a deeper and robust understanding of the nature of the threat for more effective solutions, it becomes necessary to again traverse the threat landscape, and consolidate what is known so far about botnets, that future research directions could be more easily visualised. This research uses the general exploratory approach of the qualitative methodology to survey the current botnet threat landscape: Covering the typology of botnets and their owners, the structure and lifecycle of botnets, botnet attack modes and control architectures, existing countermeasure solutions and limitations, as well as the prospects of a botnet threat. The product is a consolidation of knowledge pertaining the nature of the botnet threat; which also informs future research directions into aspects of the threat landscape where work still needs to be done.

Efficient Dynamic Bloom Filter Hashing Fragmentation for Cloud Data Storage

Security is important in cloud data storage while using the cloud services provided by the service provider in the cloud. Most of the research works have been designed for a secure cloud data storage. However, cloud users still have security issues with their outsourced data. In order to overcome such limitations, a Dynamic Bloom Filter Hashing based Cloud Data Storage (DBFH-CDS) Technique is proposed. The main goal of DBFH-CDS Technique is to improve confidentiality and security of data storage in a cloud environment. The proposed Technique is implemented using data fragmentation model and Bloom filter. The DBFH-CDS Technique uses data fragmentation model for fragmenting the large cloud datasets. After that, Bloom Filter is employed in DBFH-CDS Technique for storing the fragmented sensitive data along with higher security. The DBFH-CDS Technique ensures high data confidentiality and security for cloud data storage with the help of Bloom Filter. The performance of proposed DBFH-CDS Technique is measured in terms of Execution time and Data retrieval efficiency. The experimental results show that the DBFH-CDS Technique is able to improve the cloud data storage security with minimum space complexity as compared to state-of-the-art-works.