High resolution SOM approach to improving anomaly detection in intrusion detection systems

Machine learning in general and artificial neural networks in particular are commonly used to address the problem of detecting anomalies in intrusion detection systems. Self-Organizing Maps (SOMs) have been shown to be a promising tool for this purpose, but the limitation of the cardinality of their display space has resulted in SOMs being a black box method and impeded the design of a simpler network architecture. High resolution SOMs are a very recent development that can overcome these problems. This paper explores how high resolution SOMs can help with anomaly detection in intrusion detection systems. Experiments on a large and well established benchmark problem show that high resolution SOMs improve results while allowing a simple network architecture. It is also shown that high resolution SOMs allow the development of better understanding of the results and the problem domain

Machine Learning Algorithms for Network Intrusion Detection

Network intrusion is a growing threat with potentially severe impacts, which can be damaging in multiple ways to network infrastructures and digital/intellectual assets in cyberspace. The approach most commonly employed to combat network intrusion is the development of attack detection systems via machine learning and data mining techniques. These systems can identify and disconnect malicious network traffic, thereby helping to protect networks. This chapter systematically reviews two groups of common intrusion detection systems using fuzzy logic and artificial neural networks, and evaluates them by utilizing the widely used KDD 99 benchmark dataset. Based on the findings, the key challenges and opportunities in addressing cyber-attacks using artificial intelligence techniques are summarized with future work suggested

Numerical classification and ordination of Esenli (Giresun) forest vegetation

KARAKOSE, MUSTAFA/0000-0003-0534-3996WOS: 000503432200003The forest vegetation of Esenli Forest Planning Unit was investigated between 2015 and 2018 from the phytosociological point of view. The study area is situated in the Euxine province of Euro-Siberian Region. Phytosociological studies were carried out in accordance with the classical Braun-Blanquet methodology, and 131 releves were collected during the field survey. The releves were classified using the Modified TWINSPAN classification, and general distribution patterns of vegetation were analysed using indirect ordination analysis (Principal Component Analysis) with the R-Project available in the JUICE program. In addition to topographic factors, ecological factors were assessed using the mean Ellenberg Indicator Values to observe the ecological relationships among communities. Four new plant associations (Cirsio trachylepidis-Pinetum sylvestris, Angelico sylvestri-Alnetum barbatae, Circaeo lutetianae-Fagetum orientalis, and Veronico chamaedryo-Piceetum orientalis) were described as belonging to humid montane coniferous and thermophilous deciduous forests within four classes. Distribution pattern of plant communities was strictly influenced by altitude, inclination, moisture, nutrient content, and light