A survey on data leakage prevention systems

The formation of new malwares every day poses a significant challenge to anti-virus vendors since antivirus tools, using manually crafted signatures, are only capable of identifying known malware instances and their relatively similar variants. To identify new and unknown malwares for updating their anti-virus signature repository, anti-virus vendors must daily collect new, suspicious files that need to be analyzed manually by information security experts who then label them as malware or benign. Analyzing suspected files is a time-consuming task and it is impossible to manually analyze all of them. Consequently, anti-virus vendors use machine learning algorithms and heuristics in order to reduce the number of suspect files that must be inspected manually. These techniques, however, lack an essential element – they cannot be daily updated. In this work we introduce a solution for this updatability gap. We present an active learning (AL) framework and introduce two new AL methods that will assist anti-virus vendors to focus their analytical efforts by acquiring those files that are most probably malicious. Those new AL methods are designed and oriented towards new malware acquisition. To test the capability of our methods for acquiring new malwares from a stream of unknown files, we conducted a series of experiments over a ten-day period. A comparison of our methods to existing high performance AL methods and to random selection, which is the naive method, indicates that the AL methods outperformed random selection for all performance measures. Our AL methods outperformed existing AL method in two respects, both related to the number of new malwares acquired daily, the core measure in this study. First, our best performing AL method, termed “Exploitation”, acquired on the 9th day of the experiment about 2.6 times more malwares than the existing AL method and 7.8 more times than the random selection. Secondly, while the existing AL method showed a decrease in the number of new malwares acquired over 10 days, our AL methods showed an increase and a daily improvement in the number of new malwares acquired. Both results point towards increased efficiency that can possibly assist anti-virus vendors.

Novel active learning methods for enhanced PC malware detection in windows OS

In this survey, we give an extensive overview on honeypots. This includes not only honeypot software but also methodologies to analyse honeypot data.

A Survey on Honeypot Software and Data Analysis.

In this paper we review the recent advances in honeypot. Some notable proposals and there analysis have been discussed. The aspects of using honeypot in education and in hybrid environment with IDS have been explained. In this paper we also defines the use of signature technique in honeypot for traffic analysis. In the end we summarizes all these aspects.

Honeypot in network security: a survey

Honeytokens are artificial digital data items planted deliberately into a genuine system resource in order to detect unauthorized attempts to use information. The honeytokens are characterized by properties which make them appear as genuine data items. Honeytokens are also accessible to potential attackers who intend to violate an organization's security in an attempt to mine information in a malicious manner. One of the main challenges in generating honeytokens is creating data items that appear as real and that are difficult to distinguish from real tokens. In this paper we present “HoneyGen” - a novel method for generating honeytokens automatically. HoneyGen creates honeytokens that are similar to the real data by extrapolating the characteristics and properties of real data items. The honeytoken generation process consists of three main phases: rule mining in which various types of rules that characterize the real data are extracted from the production database; honeytoken generation in which an artificial relational database is generated based on the extracted rules; and the likelihood rating in which a score is calculated for each honeytoken based on its similarity to the real data. A Turing-like test was performed in order to evaluate the ability of the method to generate honeytokens that cannot be detected by humans as honeytokens. The results indicate that participants were unable to distinguish honeytokens having a high likelihood score from real tokens.

HoneyGen: An automated honeytokens generator

Information security is a growing concern today for organizations and individuals alike. This has led to growing interest in more aggressive forms of defense to supplement the existing methods. One of these methods involves the use of honeypots. A honeypot is a security resource whose value lies in being probed, attacked or compromised. In this paper we present an overview of honeypots and provide a starting point for persons who are interested in this technology. We examine different kinds of honeypots, honeypot concepts, and approaches to their implementation.

Michele Adams

Papers

Honeypots: concepts, approaches, and challenges