Malware analysis performance enhancement using cloud computing

Nowadays, computer based technology has taken a central role in every person life. Hence, damage caused by malicious software (malware) can reach and effect many people globally as what could be in the early days of computer. A close look at the current approaches of malware analysis shows that the respond time of reported malware to public users is slow. Hence, the users are unable to get prompt feedback when reporting suspicious files. Therefore, this paper aims at introducing a new approach to enhance malware analyzer performance. This approach utilizes cloud computing features and integrates it with malware analyzer. To evaluate the proposed approach, two systems had been prepared carefully with the same malware analyzer, one of them utilizes cloud computing and the other left without change. The evaluation results showed that the proposed approach is faster by 23 % after processing 3,000 samples. Furthermore, utilizing cloud computing can open door to crowd-source this service hence encouraging malware reporting and accelerate malware detection by engaging the public users at large. Ultimately this proposed system hopefully can reduce the time taken to detect new malware in the wild

SCARECROW: scalable malware reporting, detection and analysis

Malware is the main computer security threat that can cause damage to user's devices and company's infrastructure. End users who want to download executable files from the Internet are currently presented by a binary choice (OK or Cancel) but there is no viable third alternative for uncertainty (Not Sure). Reporting to any security agency or company for status inquiry regarding executable files normally lack of efficiency in terms of reporting back to the users in a timely manner. As a consequence, developing a more efficient approach that provide a prompt response to the users on reported suspicious files is important in order to encourage more end users engagement in malware reporting thus ultimately reducing the number of unknown malware in the wild. This study proposes a new automatic and scalable malware analyzer that is able to quickly scrutinize and help generate report for each malware detected. The implementation of the approach includes both the client (user's system) and the backend processing (security agency). The client side provides a user friendly and integrated reporting mechanism. The backend is based on both static and dynamic analysis for comprehensive malware detection and profiling. The backend utilizes cloud computing infrastructure to scale, speed up and automate the overall analysis and feedback processes. The system provides a win-win situation for both end user and security agency by providing sustainable and successful symbiotic anti-malware eco-system

Enhanced cuckoo malware analysis performance using cloud computing

Modem information technology affects almost every aspect of human existence. Along with numerous positive outcomes, such comprehensive influence of modem technology on everyday life can also create unprecedented opportunities for the dissemination of malicious software within very short time frames. The damage caused by malicious software can have a profound and lasting impact on many people across the globe. A close look at the current approaches of mal ware analyzers illustrates that response time to community users is inadequately slow at present. It also demonstrates that these analyzers are not scalable to fit the escalating demand for analysis. As a consequence, they will not be able to respond to end-users enquiries in proper time. to present a new approach to ways of enhancing the malware analyzer performance, in order for the end-users to get feedback faster than present indicators. This approach utilizes cloud computing scalability feature to reach appropriate levels of response time. Cloud computing is emerging scalability as the main advantage to help application scale to cope with increasing customer demands. Integrating this technique with modem applications and services will provide faster solution due to scalability. For the purposes of evaluating this approach, two systems were carefully prepared with the same malware analyzer. One of them utilizes cloud computing, and the other one is left with no changes. Both systems were put under investigation with real malware samples to drive a comparison test between the two approaches. Samples were divided into multiple groups with incremental size to study the two systems' behavior towards different submission loads. Results obtained after processing 3000 samples indicated that cloud based malware analyzer is 23% faster than the standalone system. Although cloud enabled system was performing worse than the standalone system when low samples were submitted, it started to take the lead with noticeable performance when increasing numbers of analysis requests were submitted. With greater enhancements in cloud computing implementation levels, this percentage could increase dramatically to save time consumed while analyzing malware. Applying this approach in Malaysia will help community members get faster replies regarding suspicious applications with respect to the huge number of IT consumers. This research could be easily extended to the nationwide malware reporting system which can improve the quality of signatures and anti-viruses

Retardation of Bacterial Biofilm Formation by Coating Urinary Catheters with Metal Nanoparticle-Stabilized Polymers

Urinary catheter infections remain an issue for many patients and can complicate their health status, especially for individuals who require long-term catheterization. Catheters can be colonized by biofilm-forming bacteria resistant to the administered antibiotics. Therefore, this study aimed to investigate the efficacy of silver nanoparticles (AgNPs) stabilized with different polymeric materials generated via a one-step simple coating technique for their ability to inhibit biofilm formation on urinary catheters. AgNPs were prepared and characterized to confirm their formation and determine their size, charge, morphology, and physical stability. Screening of the antimicrobial activity of nanoparticle formulations and determining minimal inhibitory concentration (MIC) and their cytotoxicity against PC3 cells were performed. Moreover, the antibiofilm activity and efficacy of the AgNPs coated on the urinary catheters under static and flowing conditions were examined against a clinical isolate of Escherichia coli. The results showed that the investigated polymers could form physically stable AgNPs, especially those prepared using polyvinyl pyrrolidone (PVP) and ethyl cellulose (EC). Preliminary screening and MIC determinations suggested that the AgNPs-EC and AgNPs-PVP had superior antibacterial effects against E. coli. AgNPs-EC and AgNPs-PVP inhibited biofilm formation to 58.2% and 50.8% compared with AgNPs-PEG, silver nitrate solution and control samples. In addition, coating urinary catheters with AgNPs-EC and AgNPs-PVP at concentrations lower than the determined IC50 values significantly (p t-test) inhibited bacterial biofilm formation compared with noncoated catheters under both static and static and flowing conditions using two different types of commercial Foley urinary catheters. The data obtained in this study provide evidence that AgNP-coated EC and PVP could be useful as potential antibacterial and antibiofilm catheter coating agents to prevent the development of urinary tract infections caused by E. coli