Research on heteregeneous data for recognizing threat

The information increasingly large of volume dataset and multidimensional data has grown rapidly in recent years. Inter-related and update information from security communities or vendor network security has present of content vulnerability and patching bug from new attack (pattern) methods. It given a collection of datasets, we were asked to examine a sample of such data and look for pattern which may exist between certain pattern methods over time. There are several challenges, including handling dynamic data, sparse data, incomplete data, uncertain data, and semistructured/unstructured data. In this paper, we are addressing these challenges and using data mining approach to collecting scattered information in routine update regularly from provider or security community

Network-on-Chip Paradigm for System-on-Chip Communication

Developments of modern technologies in electronics, such as communication, Internet, pervasive and ubiquitous computing and ambient intelligence have figured largely our life. In our day micro-electronic products inspire the ways of learning, communication and entertainment. These products such as laptop computer, mobile phones, and personal handheld sets are becoming faster, lighter in weight, smaller in size, larger in capacity, lower in power consumptions, cheaper and functionally enhanced. This trend will persistently continue. Following this trend, we could integrate more and more complex applications and even systems onto a single chip. The System-on-Chip (SoC) technologies, where complex applications are integrated onto single ULSI chips became key driving force for the developments

Cross-Site Scripting Attack Detection using Rule-Based Signature

Rule-Based Signature or also known as Misuse Detection is IDS which rely on matching data captured on retrieval of attack pattern which in system that allow attacks. If the attack activity detected according to existing signature, then it will be read by system and called as attack. The advantage of this Signature-Based IDS is the accuracy of detecting matched attack which in the system with low false-positive result and high true-positive. Cross-Site Scripting is type of attack which is perform by injecting code (usually) JavaScript to a site. XSS is very often utilized by attacker to steal web browser resource such as cookie, credentials, etc. Dataset which used in this research is dataset which created by injecting script into a website. Once obtained the dataset, then feature extraction is performed to separate the attribute which used. XSS attack pattern can be easily recognized from URI, and then detected using engine which has been created. Detection result of algorithm which used is evaluated using confusion matrix to determine detection accuracy value which performed. Obtained accuracy detection of research result reached 99.4% with TPR 98.8% and FPR 0%

Malware Detection in Portable Document Format (PDF) Files with Byte Frequency Distribution (BFD) and Support Vector Machine (SVM)

Portable Document Format (PDF) files as well as files in several other formats such as (.docx, .hwp and .jpg) are often used to conduct cyber attacks. According to VirusTotal, PDF ranks fourth among document files that are frequently used to spread malware in 2020. Malware detection is challenging partly because of its ability to stay hidden and adapt its own code and thus requiring new smarter methods to detect. Therefore, outdated detection and classification methods become less effective. Nowadays, one of such methods that can be used to detect PDF files infected with malware is a machine learning approach. In this research, the Support Vector Machine (SVM) algorithm was used to detect PDF malware because of its ability to process non-linear data, and in some studies, SVM produces the best accuracy. In the process, the file was converted into byte format and then presented in Byte Frequency Distribution (BFD). To reduce the dimensions of the features, the Sequential Forward Selection (SFS) method was used. After the features are selected, the next stage is SVM to train the model. The performance obtained using the proposed method was quite good, as evidenced by the accuracy obtained in this study, which was 99.11% with an F1 score of 99.65%. The contributions of this research are new approaches to detect PDF malware which is using BFD and SVM algorithm, and using SFS to perform feature selection with the purpose of improving model performance. To this end, this proposed system can be an alternative to detect PDF malware

The Authority of Government in Clearing Hatefull and Hostilities Electronic Information Based on Tribe, Relegion, Race and Intergroup

The Act Number 19 of 2016 concerning the amendment of The Act Number 11 of 2008 concerning Information and Electronic Transaction governing the authority of government in clearing hateful and hostilities electronic information based on tribe, religion, race and intergroup. On the one hand, the government authority aims to protect the public interest and the integrity of the nation, but on the other hand, termination of access to information would restrict the right to freedom of opinion and the right to privacy of Internet users. This study was a normative legal research, which examined the basic authority for the government to shut down negative content in cyberspace. Legal materials were collected through library research. The analysis was conducted qualitatively. This study examined three issues, namely; spreading hatred and hostility in cyberspace, legality government to close the spreading hatred and hostility and electronic evidence in spreading hatred and hostility. Spreading hatred and hostility were criminal acts that used the Internet as facilities. Internet was used by extremists to disseminate his teachings, even being used to commit acts of terrorism (cyber terrorism). In maintaining the unity and integrity, then the government had the authority to shut down access to the unlawful electronic system. The closure should be accompanied by proof of electronic information that contains hatred and hostility based on tribe, religion, race and intergrou

Analisis Forensik Jaringan pada WhatsApp

Saat ini penggunaan aplikasi WhatsApp telah digunakan secara luas lebih dari satu milyar pengguna. Kelebihan utama dari aplikasi messaging ini adalah penggunaan enkripsi end-to-end berdasarkan arsitektur keamanan Open Whisper Signal. Dalam tulisan ini, akan dijelaskan bagaimana kami dapat membajak lalu lintas jaringan dan mendapatkan artefak forensik yang terkait dengan fitur pesan teks, pesan grup, panggilan audio/video. Tujuan penelitian ini adalah untuk mengetahui komunikasi yang tersembunyi yang menggunakan enkripsi untuk melindungi integritas pesan yang dipertukarkan dengan mengetahuinya melalui teknik forensik dan sniffing. Penulis menggunakan alat dan metode yang berbeda untuk menjalankan penyelidikan

The Implementation of Deep Neural Networks Algorithm for Malware Classification

Malware is very dangerous while attacked a device system. The device that can be attacked by malware is a Mobile Phone such an Android. Antivirus in the Android device is able to detect malware that has existed but antivirus has not been able to detect new malware that attacks an Android device. In this issue, malware detection techniques are needed that can grouping the files between malware or non-malware (benign) to improve the security system of Android devices. Deep Learning is the proposed method for solving problems in malware detection techniques. Deep Learning algorithm such as Deep Neural Network has succeeded in resolving the malware problem by producing an accuracy rate of 99.42%, precision level 99% and recall 99.4%

Fortifying Big Data infrastructures to Face Security and Privacy Issues

The explosion of data available on the internet is very increasing in recent years. One of the most challenging issues is how to effectively manage such a large amount of data and identify new ways to analyze large amounts of data and unlock information. Organizations must find a way to manage their data in accordance with all relevant privacy regulations without making the data inaccessible and unusable. Cloud Security Alliance (CSA) has released that the top 10 challenges, which are as follows: 1) secure computations in distributed programming frameworks, 2) security best practices for non-relational data stores, 3) secure data storage and transactions logs, 4) end-point input validation/filtering, 5) real-time security monitoring, 6) scalable and composable privacy-preserving data mining and analytics, 7) cryptographically enforced data centric security, 8) granular access control, 9) granular audits, 10) data Provenance. The challenges themselves can be organized into four distinct aspects of the Big Data ecosystem

Attack and Vulnerability Penetration Testing: FreeBSD

 Computer system security has become a major concern over the past few years. Attacks, threats or intrusions, against computer system and network have become commonplace events. However, there are some system devices and other tools that are available to overcome the threat of these attacks. Currently, cyber attack is a major research and inevitable. This paper presents some steps of penetration in FreeBSD operating system, some tools and new steps to attack used in this experiment, probes for reconnaissance, guessing password via brute force, gaining privilege access and flooding victim machine to decrease availability. All these attacks were executed and infiltrate within the environment of Intrusion Threat Detection Universiti Teknologi Malaysia (ITD UTM) data set. This work is expected to be a reference for practitioners to prepare their systems from Internet attacks