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

Author(s):  
Adib Fakhri Muhtadi ◽  
Ahmad Almaarif

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.

Author(s):  
Adib Fakhri Muhtadi ◽  
Ahmad Almaarif

Malware is a software or computer program that is used to carry out malicious activity. Malware is made with the aim of harming users because it can change users' data, use up bandwidth and other resources without the 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. P. 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 call network API , information is generated about the order of the call network API used by malware . Then from the network traffic, obtained malware activities by analyzing the behavior of network traffic malware, payload, and bandwidth of infected traffic. Furthermore, the results of the call network API sequence used by malware and the results of network traffic analysis, are analyzed so that the impact of malware can be determined on network traffic.


2014 ◽  
Vol 568-570 ◽  
pp. 1370-1375
Author(s):  
Heng Qin ◽  
Jin Hui Zhao

Insiders, who have the lawful authority in network information system, formed a huge threat to security by abuse and misuse of authority. It has become one of huge challenge to the security of information system. Against the features of more subtle and more difficult to find, this paper study how to perceive the trusted behavior of insiders with behavior-based attestation. Taking into account the impact of various uncertainties in monitoring and perception process, dynamic awareness model of insider threat is presented based on subjective logic. In order to find the insider threats, monitoring data of actual behaviors are compared with operation tree; legality of the user behavior dynamically analyzed according to historical experience and current experience; the trust of user behavior legitimacy is represented as trust point in subjective logic. Finally, experiments are employed to test the validity and applicability of proposed method.


Author(s):  
Mahesh Pawar ◽  
Anjana Panday ◽  
Ratish Agrawal ◽  
Sachin Goyal

Network is a connection of devices in either a wired or wireless manner. Networking has become a part and parcel of computing in the present world. They form the backbone of the modern-day computing business. Hence, it is important for networks to remain alive, up, and reliable all the time. A way to ensure that is network traffic analysis. Network traffic analysis mainly deals with a study of bandwidth utilization, transmission and reception rates, error rates, etc., which is important to keep the network smooth and improve economic efficiency. The proposed model approaches network traffic analysis in a way to collect network information and then deal with it using technologies available for big data analysis. The model aims to analyze the collected information to calculate a factor called reliability factor, which can guide in effective network management. The model also aims to assist the network administrator by informing him whether network traffic is high or low, and the administrator can then take targeted steps to prevent network failure.


2020 ◽  
Author(s):  
Sumit Kumari ◽  
Neetu Sharma ◽  
Prashant Ahlawat

2020 ◽  
Author(s):  
Ke Zeng ◽  
Weiguo Zhu ◽  
Caiyou Wang ◽  
Liyan Zhu

BACKGROUND The rapid spread of COVID-19 has created a severe challenge to China’s healthcare system. Hospitals across the country reacted quickly under the leadership of the Chinese government and implemented a range of informatization measures to effectively respond to the COVID-19. OBJECTIVE To understand the impact of the pandemic on the medical business of Chinese hospitals and the difficulties faced by hospital informatization construction. To discuss the application of hospital informatization measures during the COVID-19 pandemic. To summarize the practical experience of hospitals using information technology to fight the pandemic. METHODS Performing a cross-sectional on-line questionnaire survey in Chinese hospitals, of which the participants are invited including hospital information staff, hospital administrators, medical staff, etc. Statistical analyzing the collected data by using SPSS version 24. RESULTS A total of 804 valid questionnaires (88.45%) are collected in this study from 30 provinces in mainland China, of which 731 (90.92%) were filled out by hospital information staff. 473 (58.83%) hospitals are tertiary hospitals while the remaining 331 (41.17%) are secondary hospitals. The majority hospitals (82.46%) had a drop in their business volume during the pandemic and a more substantial drop is found in tertiary hospitals. 70.40% (n=566) of hospitals have upgraded or modified their information systems in response to the epidemic. The proportion of tertiary hospitals that upgraded or modified systems is significantly higher than that of secondary hospitals. Internet hospital consultation (70.52%), pre-check and triage (62.56%), telemedicine (60.32%), health QR code (57.71%), and telecommuting (50.87%) are the most used informatization anti-pandemic measures. There are obvious differences in the application of information measures between tertiary hospitals and secondary hospitals. Among these measures, most of them (41.17%) are aiming at serving patients and most of them (62.38%) are universal which continue to be used after pandemic. The informatization measures are mostly used to control the source of infection (48.19%), such as health QR Code, etc. During the pandemic, the main difficulties faced by the hospital information department are “information construction projects are hindered” (58.96%) and “increased difficulty in ensuring network information security” (58.58%). There are significant differences in this issue between tertiary hospitals and secondary hospitals. The shortcomings of hospital informatization that should be made up for are “shorten patient consultation time and optimize consultation process” (72.51%), “Ensure network information security” (72.14%) and “build internet hospital consultations platform” (59.95%). CONCLUSIONS A significant number of innovative medical information technology have been used and played a significant role in all phases of COVID-19 prevention and control in China. Since the COVID-19 brought many challenges and difficulties for informatization work, hospitals need to constantly improve their own information technology skills to respond to public health emergencies that arise at any moment.


2020 ◽  
Vol 8 (1) ◽  
pp. 33-41
Author(s):  
Dr. S. Sarika ◽  

Phishing is a malicious and deliberate act of sending counterfeit messages or mimicking a webpage. The goal is either to steal sensitive credentials like login information and credit card details or to install malware on a victim’s machine. Browser-based cyber threats have become one of the biggest concerns in networked architectures. The most prolific form of browser attack is tabnabbing which happens in inactive browser tabs. In a tabnabbing attack, a fake page disguises itself as a genuine page to steal data. This paper presents a multi agent based tabnabbing detection technique. The method detects heuristic changes in a webpage when a tabnabbing attack happens and give a warning to the user. Experimental results show that the method performs better when compared with state of the art tabnabbing detection techniques.


Author(s):  
Ayush Bahuguna ◽  
Ankit Agrawal ◽  
Ashutosh Bhatia ◽  
Kamlesh Tiwari ◽  
Deepak Vishwakarma

Sign in / Sign up

Export Citation Format

Share Document