Data Mining Practical Machine Learning Tools and Techniques

With the development of the Internet, cyber-attacks are changing rapidly and the cyber security situation is not optimistic. This survey report describes key literature surveys on machine learning (ML) and deep learning (DL) methods for network analysis of intrusion detection and provides a brief tutorial description of each ML/DL method. Papers representing each method were indexed, read, and summarized based on their temporal or thermal correlations. Because data are so important in ML/DL methods, we describe some of the commonly used network datasets used in ML/DL, discuss the challenges of using ML/DL for cybersecurity and provide suggestions for research directions.

Machine Learning and Deep Learning Methods for Cybersecurity

Internet of Things in agriculture, recent advances and future challenges

The relative potential of various technologies for the confirmation of food authenticity and quality are discussed. Techniques that have found new applications in the field of quality assurance since 2001 are discussed in terms of their potential ease of application in an industrial setting. The use of specific techniques with chemometric analysis for the classification of food samples based on quality attributes is also included in this review. The techniques discussed are spectroscopy (UV, NIR, MIR, visible, Raman), isotopic analysis, chromatography, electronic nose, polymerase chain reaction, enzyme-linked immunosorbent assay and thermal analysis.

/pdf/recent-technological-advances-for-the-determination-of-food-z0n56pt0zc.pdf

Recent technological advances for the determination of food authenticity

Decision support systems for agriculture 4.0: Survey and challenges

Wireless sensor networks (WSNs) security is a topic of great importance because of advancement of many security sensitive applications in diverse fields involving WSNs. Compared to traditional wireless and wired networks, WSNs also possess numerous additional vulnerabilities such as dynamic network topology, broadcast nature of the medium, resource constrained nodes, immense network scale and lack of physical infrastructure. The open communication environment makes WSNs more vulnerable than wired communications to several types of attacks including the passive type of eavesdropping attack leading to intercepted transmissions and active type of jamming attack leading to disrupted transmissions. These additional vulnerabilities enable the adversary to launch severe and even more complicated attacks. Thus a thorough investigation of attacks that can be launched against WSNs is required. Therefore, this article is motivated for examining the wireless security vulnerabilities and the imposed threats to devise reliable and efficient defence technique for improvement of WSNs security. We first summarize the security challenges and the security requirements of wireless networks. The paper then throws light on security vulnerabilities in wireless networks and classifies various attacks in WSNs according to different OSI protocol layers. Finally, some open technical challenges which are still unsolved are summarized and future works in WSNs security are discussed.

Security vulnerabilities, attacks and countermeasures in wireless sensor networks at various layers of OSI reference model: A survey

In the era of information and communication technology, Security is an important issue. A lot of effort and finance are being invested in this sector. Intrusion detection is one of the most prominent fields in this area. Data mining in network intrusion detection can automate the network intrusion detection field with a greater efficiency. This paper presents a literature survey on intrusion detection system. The research papers taken in this literature survey are published from 2000 to 2012. We can see that almost 67 % of the research papers are focused on anomaly detection, 23 % on both anomaly and misuse detection and 10 % on misuse detection. In this literature survey statistics shows that 42 % KDD cup dataset, 20 % DARPA dataset and 38 % other datasets are used by the different researchers for testing the effectiveness of their proposed method for misuse detection, anomaly detection or both.

/pdf/data-mining-in-intrusion-detection-a-comparative-study-of-lnyslob2a7.pdf

Data Mining in Intrusion Detection: A Comparative Study of Methods, Types and Data Sets

Multibiometric fusion strategy and its applications: A review

The paper presents a review of the current literature published in journals in the field of information system application 'enterprise resource planning' ERP to identify challenges faced in ERP implementation projects. ERP implementation is a challenging and expensive task that not only requires rigorous efforts but also demands to have a detailed analysis of such factors that are critical to the adoption or implementation. Though ERP implementation and its critical issues, success factors and implementation problems have been investigated in the past but they are not filtered comprehensively in terms of the different perspectives. The result of literature review presented of our work focuses on the ERP implementation challenges from four different perspectives such as: technology selection; change management, knowledge management, emerging technologies. Considering these perspectives in ERP implementation projects organisations can realise several benefits such as reducing costs and saving time or extra effort.

Literature review on ERP implementation challenges

To maintain the integrity, availability, reliability of the data and services available on web requires a strong network security framework, in such consequence IDS based on data mining are the best solution. In this paper we proposed an intrusion detection system which is based on the fuzzy min max neural network and the particle swarm optimization. The proposed system is tested with the help of preprocessed KDD CUP data set. Classification accuracy and classification error are taken as a performance evaluation parameter to test the effectiveness of the system. The proposed system is compared with the some of the well-known methods, the results shows that the proposed system performed well as compared to the other systems.

Vijay Kumar Jha

Papers

Security vulnerabilities, attacks and countermeasures in wireless sensor networks at various layers of OSI reference model: A survey

Data Mining in Intrusion Detection: A Comparative Study of Methods, Types and Data Sets

Multibiometric fusion strategy and its applications: A review

Literature review on ERP implementation challenges

Fuzzy min---max neural network and particle swarm optimization based intrusion detection system