The Experts below are selected from a list of 567 Experts worldwide ranked by ideXlab platform
Chunyan Jiang - One of the best experts on this subject based on the ideXlab platform.
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effective evaluation about the Antivirus Solution for smart phone
Journal of information and communication convergence engineering, 2011Co-Authors: Sukjo Shin, Seonjoo Kim, Chunyan JiangAbstract:Smartphone has formed a new market and introduced a new environment. They have an operating system like PCs, enabling free installation and removal of application programs. As the number of Smartphone users is increasing, more personal information is also exposed to malicious codes. There are problem of modification and deletion of files, battery consumption, and information leakage due to malicious codes. As the needs of Smartphone Antivirus Solutions are increasing, the Antivirus Solutions should be evaluated with quality characteristics. In this paper, we propose an effective evaluation method for functionality and performance of Smartphone Antivirus Solutions, and the best practices for evaluation.
Eliasson Oskar - One of the best experts on this subject based on the ideXlab platform.
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The Development and Effectiveness of Malware Vaccination : An Experiment
Blekinge Tekniska Högskola Institutionen för datavetenskap, 2020Co-Authors: Ädel Lukas, Eliasson OskarAbstract:Background. The main problem that our master thesis is trying to reduce is malware infection. One method that can be used to accomplish this goal is based on the fact that most malware does not want to get caught by security programs and are actively trying to avoid them. To not get caught malware can check for the existence of security-related programs and artifacts before executing malicious code and depending on what they find, they will evaluate if the computer is worth infecting. The idea is that by identifying these checks we could "vaccinate" a system with data-points that trigger these checks and trick the malware into believing that a system is protected and skip it. Objectives. This thesis will research common malware evasion techniques to find what data-points malware avoids and develop a vaccine with the found data-points. To test the effectiveness of the vaccine an experiment will be conducted where malware will be executed on different systems to observe their behavior. Methods. The vaccine concept will be tested by gathering data-points with a background review of related works and performing an experiment. In the experiment a virtual machine without protective measures is used as a baseline which can be compared to a virtual machine with the vaccine. It is also interesting to see how a vaccine compares to an Antivirus Solution and how / if it would cooperate with an Antivirus Solution, so two more virtual machines are added to the experiment, one with just an Antivirus software installed, and a second one with Antivirus installed plus the vaccine. On these four systems, a set of malware will be executed and their behavior and activity (Windows API calls) will also be measured and compared. Results. This experiment showed that our vaccine was effective in reducing malware behavior, 70% of the malware did reduce their activity when exposed to the vaccine compared to the baseline. The results also indicate that the vaccine was effective in cooperation with an Antivirus program, 85% of the malware did reduce their activity on this virtual machine compared to the baseline. Conclusions. From the results, we can conclude that of our created systems the system that reduced the most malware activity was the system with Antivirus plus vaccine. This shows that vaccination can be a viable option for researchers to further study
Sukjo Shin - One of the best experts on this subject based on the ideXlab platform.
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effective evaluation about the Antivirus Solution for smart phone
Journal of information and communication convergence engineering, 2011Co-Authors: Sukjo Shin, Seonjoo Kim, Chunyan JiangAbstract:Smartphone has formed a new market and introduced a new environment. They have an operating system like PCs, enabling free installation and removal of application programs. As the number of Smartphone users is increasing, more personal information is also exposed to malicious codes. There are problem of modification and deletion of files, battery consumption, and information leakage due to malicious codes. As the needs of Smartphone Antivirus Solutions are increasing, the Antivirus Solutions should be evaluated with quality characteristics. In this paper, we propose an effective evaluation method for functionality and performance of Smartphone Antivirus Solutions, and the best practices for evaluation.
Shi Yunhui - One of the best experts on this subject based on the ideXlab platform.
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overall Antivirus Solution for the campus network thought
Communications Technology, 2009Co-Authors: Shi YunhuiAbstract:The construction of education informatization is in full swing, and the healthy development of education network is incessantly threatened by the virus, hacker, illegal intrusion. For the fundamental changes in the overall network of campus anti-virus system, the classifications, the main technical characteristics of network anti-virus security technology and the idea of all-direction security system of campus network, the software and hardware deployment and anti-virus management strategy of the campus network security system is proposed. According the requirement of network architecture and overall deploymentd, the detailed research and analysis is carried out, and an overall secure and relable campus network system could thus be constructed.
Grini, Lars Strande - One of the best experts on this subject based on the ideXlab platform.
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Feature Extraction and Static Analysis for Large-Scale Detection of Malware Types and Families
2016Co-Authors: Grini, Lars StrandeAbstract:There exist different methods of identifying malware, and widespread method is the one found in almost every Antivirus Solution on the market today; the signature based approach. This approach uses a one-way cryptographic function to generate a unique hash of each file. Afterwards, each hash is checked against a database of hashes of known malware. This method provides close to none false positives, but this does also mean that this approach can only detect previously known malware, and will in many cases also provide a number of false negatives. Malware authors exploit this weakness in the way that they change a small part of the malicious code, and thereby changes the entire hash of the file, which then leaves the malicious code undetectable until the sample is discovered, analyzed and updated in the vendors database(s). In the light of this relatively easy mitigation for malware authors, it is clear that we need other ways to identify malware. The other two main approaches for this are static analysis and behavior based/dynamic analysis. The primary goal of such analysis and previous research has been focused around detecting whether a file is malicious or benign (binary classification). There has been comprehensive work in these fields the last few years. In the work we are proposing, we will leverage results from static analysis using machine learning methods, to distinguish malicious Windows executables. Not just benign/malicious as in many researches, but by malware family affiliation. To do this we will use a database consisting of about of 330.000 malicious executables. A challenge in this work will be the naming of the samples and families as different Antivirus vendors labels samples with different names and follows no standard naming scheme. This is exemplified by e.g. the VirusTotal online scanner which scans a hash in 57 malware databases. For the static analysis we will use the VirusTotal scanner as well as an open source tool for analyzing portable executables, PEframe. The work performed in the thesis presents a novel approach to extract and construct features that can be used to make an estimation of which type and family a malicious file is an instance of, which can be useful for analysis and Antivirus scanners. This contribution is novel because multinominal classification is applied to distinguish between different types and families