Article Review: Analysis of Malware Detection Techniques

This report provides an article review focusing on malware detection and prevention techniques. It analyzes two research papers: one exploring automated URL classification to identify malicious websites, and the other examining the effectiveness of reputation-based blacklists in blocking potentially harmful hosts and network blocks. The first article discusses the development of systems that use statistical methods to discover lexical and host-based properties of malicious URLs, achieving high accuracy in detecting threats. The second paper investigates the accuracy of blacklists used to block unsolicited email and reveals non-trivial false positives and negatives, highlighting the complexities of these security measures. The review provides a comparative analysis of these approaches, their strengths, and limitations in the context of the evolving landscape of cyber threats, emphasizing the need for robust and adaptive malware detection strategies.

Running head: ARTICLE REVIEW
Article Review
Name of the Student
Name of the University
Author Note

1ARTICLE REVIEW
Table of Contents
Introduction................................................................................................................................2
Brief report of Article 1..........................................................................................................2
Brief report of Article 2..........................................................................................................3
References..................................................................................................................................4

2ARTICLE REVIEW
Introduction
The rate of Malware Attacks has increased at an exponential rate in the latest times.
The more the attacks are being tried to be mitigated, the more the cyber criminals are creating
much enhanced malicious software to gain access of the personal information in an
unauthorized way. It is a known fact that the malware attacks are mostly done to perform
theft of personal data and information for gathering monetary gains. The malware attacks are
not just only damaging the devices of the victims but are also halting financial transactions
and also are capable of cyber lockdowns over an area or an entire city. However, there has
been developed mitigations strategies as well, that simply detects and prevents the malware
attacks to occur. However, there are evidences about the existences of some fractions of
malware infected machines even after the detection and mitigation systems that are used for
developing undetectable betraying and etymological automated URLs for continuing the
works of a malware. There have been several researches conducted on the mitigation factors
of these URLs and following would be about a briefing of two of these researches by two
different authors.
Brief report of Article 1
According to the author (), this report aims at finding the solution to detect the
malicious websites, which are potential malware threats with suspicious URLs. According to
the author, this research topic is extremely necessary for having a precaution before a
malicious URL is clicked by a user unknowingly. If there was a system to detect these, then
the problem regarding the attacks could be much easier to control. The learning of a
mechanism is thus necessary to understand how these URLs need to be detected and how a
user can easily understand if any URL is suspicious enough before clicking onto them. There
have been several approaches developed for this reason which are able to support the

3ARTICLE REVIEW
classification of problem by judging the website reputation. They would be detecting the
suspicion from lexical features, IP address properties, WHOIS properties, Domain name
properties and geographic properties. The developed features would then be having
classification models to chalk out the suspicious ones.
Brief report of Article 2
The author of this paper has the opinion on the blacklisted websites, that would have a
defending mechanism against these website URLs with the help of blackhole lists. This is
why, they would be able to reputation-based websites that would effectively list the potential
reputation based blacklist and them study the network. This mechanism would find out if
these blocks are unsolicited or if there is a potential threat that has false positives or false
negatives. Based on these mechanisms, the derivation of suspicious URLs would be classified
so that there would not be a malicious attack on users through potentially harmful websites.
Otherwise, the malware attacks would not be occurring through spam mails, but just by
clicking onto some undetected websites.

4ARTICLE REVIEW
References
Ma, Justin & Saul, Lawrence & Savage, Stefan & Voelker, Geoffrey. (2009). Beyond
blacklists: learning to detect malicious Web sites from suspicious URLs. 1245-1254.
10.1145/1557019.1557153.
S. Sinha, M. Bailey and F. Jahanian, "Shades of grey: On the effectiveness of reputation-
based “blacklists”," 2008 3rd International Conference on Malicious and Unwanted
Software (MALWARE), Fairfax, VI, 2008, pp. 57-64.