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Malware Security: Combating Viruses, Worms, and Root Kits
Abstract
To many, the very mention of protecting a Mac against malware is actually a pretty inappropriate conversation. But it’s an obsession to many at Apple. And because they do a pretty darn good job of protecting users, there’s not a lot of concern that needs to be had. However, some caution goes a long way in case things get through Apple’s vaunted defense system. That build-in defense system includes technologies like Xprotect, which is like a built-in signature-based anti-virus solution, LSQuarantine, which marks anything downloaded as protected, SIP, which protects Apple’s protected space and drivers from infection by third party software, and a robust signing requirement, which makes it difficult for a user to get malware on their system. But it can happen, so we’ll look at what you need to do when it does.
... The first computer virus detected was Elk Cloner, which infected Apple II operating systems with floppy disks, and displayed a comic message on infected computers. [1] Elk Cloner, developed by 15-year-old Richard Skrenta in 1982, was considered a joke, but it showed the possibility and possibility of a possible malware being installed in Apple computer memory, with the ability to prevent users from removing it. The term computer virus was not used again until a year later, when Fred Cohen, the scientific paper has been published in title Computer viruses with theory and experiments in 1983 the work was for students are graduated from California University. ...
Currently, viruses are the most dangerous things that happens to computers, whether by transferring data or from the Internet cloud. This research paper shows how to implement a virus that has an ability to move and fortify itself from deletion or destroying. A strong virus must have automatic multiplication, automatic transmission between devices, copying itself in important locations on the computer and performing unauthorized actions and instructions in the computer. So in this research paper it will also shows how to get rid of such viruses and ways to prevent them or providing a good security for the users.
... Embedding the code in mailing lists [7]. This method is implemented on the basis of sending spam messages, which usually contain malicious code or malware. ...
Mining a cryptocurrency is profitable on someone’s resources. It is becoming increasing problem
in the enterprise to control the operations of its infrastructure while in idle or “off-work” time. In
this paper we present enterprise security assessment framework for cryptocurrency mining based
on Monero cryptocurrency. The framework consists from surveying power consumption on GPU
mining farms and traditional desktop PCs, analysis of web resources for browser-based mining
on both internal and external domain names, the handy network logs analysis tool based on the
regular expressions. While there had been significant difference between GPU and traditional
desktop PC’s power consumption, computational ratio of the idle PCs after working time remains
questionable. In the browser-based cryptocurrency mining, there were no data on public domains,
however there had been a possibility for using the private domain names, thus further research
and different tools are required. In the network analysis, there were not enough evidences on the
network mining, and this leads to the different research question that attackers may use proxy
techniques to bypass traffic filtering and network analysis.
The main worry with the rapid growth of technology has been cyber assaults. To counter these threats, sophisticated security systems have been-created, however none of them function completely error-free. This study uses face detection and recognition by Haar cascade classifier and LBPH for authentication initially, and then an intrusion detection system (IDS) using machine learning algorithm like FNT and KNN can identify fraudulent behavior. The typical accuracy for face detection is 90.2%. Whereas in recognition, it can be demonstrated that LBPH performs better in both still images and video than Eigen faces with respect to detection accuracy and execution speed. With a false positive rate of 1.6%, known and unknown intrusions accuracy detected by FNT is 97.2%. The detection rates for DOS, probe, U2R, and R2L in the known intrusion classifier by KNN are 98.7%, 97.4%, 97.8%, and 96.6%, respectively, whereas the false positive rates are 0.4%, 0.0.1.45%, 2.19%, and 1.97% respectively. The proposed known intrusion mechanism is demonstrated to outperform competing methods. The percentage of intrusion detection in the unknown intrusion detected by C-means clustering is 98.6%, and the rate of false positives is 1.32%.
Intrusion detection is one of the important security problems in today’s cyber world. A significant number of techniques have been developed which are based on machine learning approaches. However, they are not very successful in identifying all types of intrusions. In this paper, a detailed investigation and analysis of various machine learning techniques have been carried out for finding the cause of problems associated with various machine learning techniques in detecting intrusive activities. Attack classification and mapping of the attack features is provided corresponding to each attack. Issues which are related to detecting low-frequency attacks using network attack dataset are also discussed and viable methods are suggested for improvement. Machine learning techniques have been analyzed and compared in terms of their detection capability for detecting the various category of attacks. Limitations associated with each category of them are also discussed. Various data mining tools for machine learning have also been included in the paper. At the end, future directions are provided for attack detection using machine learning techniques.
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