Implementation of Intelligent Searching Using Self-Organizing Map for Webmining Used in Document Containing Information in Relation to Cyber Terrorism

Endy,; Lim, Charles; Eng, Kho I; Nugroho, Anto Satriyo

doi:10.1109/act.2010.35

Cited by 5 publications

(5 citation statements)

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“…For example, Elovici et al (2008) monitored the Internet protocol (IP) of a suspicious user and connected it to their real identity. Terrorists have also been detected based on the frequency of certain words in documents that have relevance to terrorism (Endy et al , 2010) or by using a structural and stylistic feature ranking approach (Chen, 2009). Content analysis on the dark web can be done from three viewpoints: content richness, technical complexity and web interactivity, as shown by Chen et al (2008a, b, c) and Qin et al (2007).…”

Section: Detecting Terrorist/extremists On the Webmentioning

confidence: 99%

“…As observed in Table 2, many studies have been performed on the dark web using content mining techniques only (Abbasi et al , 2008; Benjamin et al , 2013, 2014; Chen, 2008b, 2009; Chen et al , 2008c; Elovici et al , 2004; Endy et al , 2010; Fu et al , 2010; L'Huillier et al , 2010; Prentice et al , 2011; Scanlon, 2014; Simanjuntak et al , 2010; Yang et al , 2009). Some studies have used both structure and content mining techniques to perform feature selection, extraction or topical analysis (Bouchard et al , 2014; Chen, 2008a, b; Fu et al , 2010; Larson and Chen, 2009; Last et al , 2008; Patil et al , 2013; Reid et al , 2005; Zhang et al , 2010; Zhou et al , 2005).…”

Section: Detecting Terrorist/extremists On the Webmentioning

confidence: 99%

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Techniques to detect terrorists/extremists on the dark web: a review

Alghamdi

Selamat

2022

DTA

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PurposeWith the proliferation of terrorist/extremist websites on the World Wide Web, it has become progressively more crucial to detect and analyze the content on these websites. Accordingly, the volume of previous research focused on identifying the techniques and activities of terrorist/extremist groups, as revealed by their sites on the so-called dark web, has also grown.Design/methodology/approachThis study presents a review of the techniques used to detect and process the content of terrorist/extremist sites on the dark web. Forty of the most relevant data sources were examined, and various techniques were identified among them.FindingsBased on this review, it was found that methods of feature selection and feature extraction can be used as topic modeling with content analysis and text clustering.Originality/valueAt the end of the review, present the current state-of-the- art and certain open issues associated with Arabic dark Web content analysis.

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Section: Detecting Terrorist/extremists On the Webmentioning

confidence: 99%

Section: Detecting Terrorist/extremists On the Webmentioning

confidence: 99%

Techniques to detect terrorists/extremists on the dark web: a review

Alghamdi

Selamat

2022

DTA

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“…Dalam penelitian ini akan dikomparasikan kombinasi Self Organizing Map (SOM) dengan Frequent Pattern-Growth (FP-Growth), K-Medoids dengan Frequent Pattern-Growth (FP-Growth) dan Frequent Pattern-Growth (FP-Growth) yang berdiri sendiri tanpa di clustering dengan algoritma apapun sebelumnya. Algoritma Self Organizing Map (SOM) dipilih karena Self Organizing Map (SOM) merupakan algoritma clustering model aglomerative dan partitif selain itu juga dikenal sebagai algoritma non liner, teratur, pemetaan dari input data dimensi tinggi ke dalam array dimensi rendah [4][5].. Algoritma K-Medoids dipilih sebagai perbandingan untuk mendampingi Self Organizing Map (SOM) dan Frequent Pattern-Growth (FP-Growth). Pada dasarnya, algoritma clustering yang paling sering dipakai adalah K-Means.…”

Section: Pendahuluanunclassified

Analisa Pola Belanja Menggunakan Algoritma Fp Growth, Self Organizing Map (Som) Dan K Medoids

Ghozali

Ehwan²,

Sugiharto

2017

Simet

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ABSTRAKDalam sebuah bisnis, diperlukan upaya memaksimalkan keuntungan. Diantaranya dengan melakukan promosi. Ketepatan promosi dapat dipelajari dari database sebuah perusahaan ritel utamanya pola belanja pada produk yang biasa dibeli bersamaan. Informasi tentang pola belanja pelanggan yang tidak akurat menyebabkan kebijakan promosi tidak tepat dan efisien.Salah satu upaya lazim untuk memperoleh dan menggali pola belanja pelanggan adalah menggunakan data mining yang dikenal sebagai Knowledge Discovery in Database (KDD). Pendekatan yang biasa digunakan adalah asosiasi. Permasalahannya aturan asosiasi cenderung mengabaikan dataset yang besar. Untuk mengatasi hal tersebut dilakukan klasifikasi barang yang dibeli dan tidak dibeli bersamaan. Algoritma Self Organizing Map (SOM) dan K-Medoids cocok untuk diterapkan dalam mengcluster dataset besar. Penelitian ini menguji kevalidan dan kecepatan algortima Self Organizing Map (SOM) dan K-Medoids jika dikombinasi dengan Frequent Pattern-Growth (FP-Growth).Kata kunci: analisis keranjang pasar, rekomendasi produk, self organizing map, k-medoids, fp-growth. ABSTRACTIn a business, it takes effort to maximize profits. Among them with promotions. The accuracy of the promotion can be learned from the database a major retail company spending patterns on products commonly purchased together. Information on customer shopping patterns inaccurate cause improper promotion policy and efisien.Salah a common effort to acquire and explore the shopping patterns of customers is using data mining known as Knowledge Discovery in Databases (KDD). The approach used is association. The problem tends to ignore the rules of association of large datasets. To overcome this problem do the classification of goods purchased and not purchased together. Algorithm Self Organizing Map (SOM) and K-Medoids mengcluster suitable to be applied in large datasets. This study tested the validity and speed algorithms Self Organizing Map (SOM) and K-Medoids when combined with Frequent Pattern-Growth (FP-Growth).

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mentioning

confidence: 99%

“…Endy et al (2010) used SOMs to visualize the topology of the data in order to perform cluster analysis of the textual documents related to cyber terrorism[57]. presented a network security evaluation model for quantitative analysis of the degree of intrusion danger level based on AIS theory, and demonstrated its advantages over traditional models for network security evaluation[58].Liu et al (2011) introduced an AIS-based intrusion detection mechanism into the Internet ofThings (IoT) environment, which simulates self-adaptation and self-learning mechanisms via dynamic adaptation to the environment.…”

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confidence: 99%

Applications of Artificial Intelligence Techniques to Combating Cyber Crimes: A Review

Dilek¹,

Çakır²,

Aydin³

2015

IJAIA

127

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With the advances in information technology (IT) criminals are using cyberspace to commit numerous cyber crimes. Cyber infrastructures are highly vulnerable to intrusions and other threats. Physical devices and human intervention are not sufficient for monitoring and protection of these infrastructures; hence, there is a need for more sophisticated cyber defense systems that need to be flexible, adaptable and robust, and able to detect a wide variety of threats and make intelligent real-time decisions. Numerous bio-inspired computing methods of Artificial Intelligence have been increasingly playing an important role in cyber crime detection and prevention. The purpose of this study is to present advances made so far in the field of applying AI techniques for combating cyber crimes, to demonstrate how these techniques can be an effective tool for detection and prevention of cyber attacks, as well as to give the scope for future work.

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Implementation of Intelligent Searching Using Self-Organizing Map for Webmining Used in Document Containing Information in Relation to Cyber Terrorism

Cited by 5 publications

References 0 publications

Techniques to detect terrorists/extremists on the dark web: a review

Techniques to detect terrorists/extremists on the dark web: a review

Analisa Pola Belanja Menggunakan Algoritma Fp Growth, Self Organizing Map (Som) Dan K Medoids

Applications of Artificial Intelligence Techniques to Combating Cyber Crimes: A Review

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