Impact Analysis of Malware Based on Call Network API with Heuristic Detection Method

Malware is a program that has a negative influence on computer systems that don\u27t have user permissions. The purpose of making malware by hackers is to get profits in an illegal way. Therefore, we need a malware analysis. Malware analysis aims to determine the specifics of malware so that security can be built to protect computer devices. One method for analyzing malware is heuristic detection. Heuristic detection is an analytical method that allows finding new types of malware in a file or application. Many malwares are made to attack through the internet because of technological advancements. Based on these conditions, the malware analysis is carried out using the API call network with the heuristic detection method. This aims to identify the behavior of malware that attacks the network. The results of the analysis carried out are that most malware is spyware, which is lurking user activity and retrieving user data without the user\u27s knowledge. In addition, there is also malware that is adware, which displays advertisements through pop-up windows on computer devices that interfaces with user activity. So that with these results, it can also be identified actions that can be taken by the user to protect his computer device, such as by installing antivirus or antimalware, not downloading unauthorized applications and not accessing unsafe websites. &nbsp

Impact Analysis of Malware Based on Call Network API With Heuristic Detection Method

Malware is a program that has a negative influence on computer systems that don't have user permissions. The purpose of making malware by hackers is to get profits in an illegal way. Therefore, we need a malware analysis. Malware analysis aims to determine the specifics of malware so that security can be built to protect computer devices. One method for analyzing malware is heuristic detection. Heuristic detection is an analytical method that allows finding new types of malware in a file or application. Many malwares are made to attack through the internet because of technological advancements. Based on these conditions, the malware analysis is carried out using the API call network with the heuristic detection method. This aims to identify the behavior of malware that attacks the network. The results of the analysis carried out are that most malware is spyware, which is lurking user activity and retrieving user data without the user's knowledge. In addition, there is also malware that is adware, which displays advertisements through pop-up windows on computer devices that interfaces with user activity. So that with these results, it can also be identified actions that can be taken by the user to protect his computer device, such as by installing antivirus or antimalware, not downloading unauthorized applications and not accessing unsafe websites. &nbsp

Modeling the telephone call network

Author name used in this publication: Francis C. M. LauAuthor name used in this publication: Chi K. TseRefereed conference paper2006-2007 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Analysis of Malware Impact on Network Traffic using Behavior-based Detection Technique

Malware is a software or computer program that is used to carry out malicious activity. Malware is made with the aim of harming user’s device because it can change user’s data, use up bandwidth and other resources without user's permission. Some research has been done before to identify the type of malware and its effects. But previous research only focused on grouping the types of malware that attack via network traffic. This research analyzes the impact of malware on network traffic using behavior-based detection techniques. This technique analyzes malware by running malware samples into an environment and monitoring the activities caused by malware samples. To obtain accurate results, the analysis is carried out by retrieving API call network information and network traffic activities. From the analysis of the malware API call network, information is generated about the order of the API call network used by malware. Using the network traffic, obtained malware activities by analyzing the behavior of network traffic malware, payload, and throughput of infected traffic. Furthermore, the results of the API call network sequence used by malware and the results of network traffic analysis, are analyzed so that the impact of malware on network traffic can be determined

Dynamical Systems to Monitor Complex Networks in Continuous Time

In many settings it is appropriate to treat the evolution of pairwise interactions over continuous time. We show that new Katz-style centrality measures can be derived in this context via solutions to a nonautonomous ODE driven by the network dynamics. This allows us to identify and track, at any resolution, the most influential nodes in terms of broadcasting and receiving information through time dependent links. In addition to the classical notion of attenuation across edges used in the static Katz centrality measure, the ODE also allows for attenuation over time, so that real time "running measures" can be computed. With regard to computational efficiency, we explain why it is cheaper to track good receivers of information than good broadcasters. We illustrate the new measures on a large scale voice call network, where key features are discovered that are not evident from snapshots or aggregates

Scenarios and research issues for a network of information

This paper describes ideas and items of work within the framework of the EU-funded 4WARD project. We present scenarios where the current host-centric approach to infor- mation storage and retrieval is ill-suited for and explain how a new networking paradigm emerges, by adopting the information-centric network architecture approach, which we call Network of Information (NetInf). NetInf capital- izes on a proposed identifier/locator split and allows users to create, distribute, and retrieve information using a com- mon infrastructure without tying data to particular hosts. NetInf introduces the concepts of information and data ob- jects. Data objects correspond to the particular bits and bytes of a digital object, such as text file, a specific encod- ing of a song or a video. Information objects can be used to identify other objects irrespective of their particular dig- ital representation. After discussing the benefits of such an indirection, we consider the impact of NetInf with respect to naming and governance in the Future Internet. Finally, we provide an outlook on the research scope of NetInf along with items for future work

DAMPAK MALWARE BERDASARKAN API CALL NETWORK DENGAN METODE HEURISTIC DETECTION

Malware adalah sebuah program yang memiliki pengaruh negatif pada sistem komputer yang tidak memiliki user permission. Semakin berkembangnya dunia internet, semakin berkembang pula jumlah maupun jenis dari malware. Tujuan dari dibuatnya malware oleh para peretas ialah untuk menghasilkan uang dengan cara yang tidak sah. Dengan adanya bahaya tersebut membuat para user komputer merasa terancam. Oleh karena itu diperlukan suatu malware analysis. Malware analysis bertujuan untuk mengetahui spesifik dari malware sehingga dapat meningkatkan keamanan pada suatu sistem komputer. Banyak metode yang dapat digunakan dalam menganalisis malware, salah satunya adalah metode heuristic detection. Heuristic detection merupakan metode analisis yang memungkinkan untuk menemukan malware jenis baru dengan mencari perintah atau instruksi yang seharusnya tidak terdapat pada suatu aplikasi. Dengan adanya kemajuan teknologi, maka semakin banyak pula orang-orang yang akan mengakses internet, sehingga banyak malware yang dibuat untuk menyerang melalui jaringan internet. Berdasarkan kondisi tersebut, maka dilakukanlah malware analysis menggunakan API call network dengan metode heuristic detection. Hal ini bertujuan untuk mengidentifikasi bagaimana kecendrungan dari malware-malware yang menyerang dari sisi jaringan. Hasil analisis dari penelitian ini adalah malware dengan API network cendrung bersifat sebagai spyware, yaitu mengintai aktivitas dan mengambil data user tanpa seizin user. Selain itu, terdapat pula malware yang bersifat sebagai adware, yaitu menampilkan iklan-iklan melalui jendela pop-up pada perangkat komputer yang dapat mengganggu aktivitas user. Sehingga dengan adanya hasil tersebut, dapat diidentifikasi pula tindakan-tindakan yang harus dilakukan oleh user untuk melindungi perangkat komputernya, seperti dengan memasang antivirus atau antimalware, tidak mengunduh aplikasi yang tidak sah serta tidak mengakses website yang tidak aman