There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Data Mining - Concepts and Techniques.

Black-box web application vulnerability scanners are automated tools that probe web applications for security vulnerabilities In order to assess the current state of the art, we obtained access to eight leading tools and carried out a study of: (i) the class of vulnerabilities tested by these scanners, (ii) their effectiveness against target vulnerabilities, and (iii) the relevance of the target vulnerabilities to vulnerabilities found in the wild To conduct our study we used a custom web application vulnerable to known and projected vulnerabilities, and previous versions of widely used web applications containing known vulnerabilities Our results show the promise and effectiveness of automated tools, as a group, and also some limitations In particular, "stored" forms of Cross Site Scripting (XSS) and SQL Injection (SQLI) vulnerabilities are not currently found by many tools Because our goal is to assess the potential of future research, not to evaluate specific vendors, we do not report comparative data or make any recommendations about purchase of specific tools

/pdf/state-of-the-art-automated-black-box-web-application-2o7akal2al.pdf

State of the art automated black-box web application vulnerability testing

Image-Based malware classification using ensemble of CNN architectures (IMCEC)

A new network forensic framework based on deep learning for Internet of Things networks: A particle deep framework

Instant Messaging is a popular smartphone’s application. One example of Instant Messaging application is WhatsApp. WhatsApp is widely used judging from its users that reach more than 1 Billion users in January 2017. WhatsApp’s security recently has been updated with latest encryption type and technology by implementing end-to-end encryption. The number of users or possible crime target and security features in WhatsApp can lead to crime by people that have criminal intentions. Investigators need to use mobile forensic methodologies and tools for investigating smartphone and finding out the crime evidence. However, investigators are often facing challenges during the investigation because of incompatibility between forensic tools and mobile technology. This research will experiment using available forensic tools with NIST forensic method for extracting latest WhatsApp’s artifacts. Forensics tools capabilities will be evaluated and compared to find its strengths and weaknesses.

/pdf/mobile-forensic-tools-evaluation-for-digital-crime-1b53pftqoo.pdf

Mobile Forensic Tools Evaluation for Digital Crime Investigation

Di s tributed Denial of Service (DDoS) is a type of attack using the volume, intensity, and m ore costs m itigation to increase in this era . A ttack ers used many zombie computers to exhaust the resources available to a network, application or service so that authorize users cannot gain access or the network service is down, and it is a great loss for Internet users in computer networks affected by DDoS attacks. In the Network Forensic, a crime that occurs in the system network services can be sued in the court and the attackers will be punished in accordance with law. This research has the goal to develop a new approach to detect DDoS attacks based on network traffic activity were statistically analyzed using Naive Bayes method. Data were taken from the training and testing of network traffic in a core router in Master of Information Technology Research Laboratory University of Ahmad Dahlan Yogyakarta. The new approach in detecting DDoS attacks is expected to be a relation with Intrusion Detection System (IDS) to predict the existence of DDoS attacks.

http://journal.portalgaruda.org/index.php/EEI/article/download/605/914

Review of Detection DDOS Attack Detection Using Naive Bayes Classifier for Network Forensics

As the education fees are becoming more expensive, more students apply for scholarships. Consequently, hundreds and even thousands of applications need to be handled by the sponsor. To solve the problems, some alternatives based on several attributes (criteria) need to be selected. In order to make a decision on such fuzzy problems, Fuzzy Multiple Attribute Decision Making (FMDAM) can be applied. In this study, Unified Modeling Language (UML) in FMADM with TOPSIS and Weighted Product (WP) methods is applied to select the candidates for academic and non-academic scholarships at Universitas Islam Negeri Sunan Kalijaga. Data used were a crisp and fuzzy data. The results show that TOPSIS and Weighted Product FMADM methods can be used to select the most suitable candidates to receive the scholarships since the preference values applied in this method can show applicants with the highest eligibility

A Fuzzy Topsis Multiple-Attribute Decision Making for Scholarship Selection

Malware analysis is a process to perform analysis of malware and how to study the components and behavior of malware. On this paper it will use two methods of malware analysis, static analysis and dynamic analysis. Static analysis is a method of malware analysis which done without running the malware. While dynamic analysis is a method of malware analysis which the malware is running in a secure system [7]. Malware analysis is important, since many malware at this day which is not detectable by antivirus. Now viruses are made with special ability to avoid detection from antivirus [9]. On this research we will focus on implementation of malware analysis using static analysis and dynamic analysis method. General Terms Computer Security

/pdf/implementation-of-malware-analysis-using-static-and-dynamic-3hcwbbapbc.pdf

Implementation of Malware Analysis using Static and Dynamic Analysis Method

Biaya pendidikan semakin mahal, banyak mahasiswa mengajukan beasiswa. Ratusan bahkan
ribuan formulir pengajuan beasiswa harus diseleksi oleh sponsor. Permasalahan tersebut bertujuan untuk
memilih beberapa alternatif terbaik berdasarkan beberapa atribut (kriteria) yang digunakan. Dalam rangka
pengambilan keputusan pada permasalahan yang bersifat fuzzy dapat digunakan Fuzzy Multiple Attribute
Decision Making (FMADM). Pada penelitian ini dilakukan pemodelan menggunakan Unified Modelling
Language (UML) pada FMADM dengan metode TOPSIS dan Weighted Product untuk menyeleksi calon
penerima beasiswa akademik dan non akademik di Universitas Islam Negeri Sunan Kalijaga. Data yang
digunakan adalah data fuzzy dan crisp. Hasil penelitian menunjukkan bahwa Metode TOPSIS dan
Weighted Product pada FMADM dapat digunakan untuk seleksi beasiswa. Hasil seleksi
merekomendasikan mahasiswa yang memiliki tingkat kelayakan paling tinggi untuk mendapatkan
beasiswa berdasarkan nilai preferensi yang dimiliki.
Kata kunci: Fuzzy Multiple Attribute Decision Making, TOPSIS, Weighted Product, Scholarship

Imam Riadi

Papers

Mobile Forensic Tools Evaluation for Digital Crime Investigation

Review of Detection DDOS Attack Detection Using Naive Bayes Classifier for Network Forensics

A Fuzzy Topsis Multiple-Attribute Decision Making for Scholarship Selection

Implementation of Malware Analysis using Static and Dynamic Analysis Method

A Fuzzy Topsis Multiple-Attribute Decision Making for Scholarship Selection