Diversity with AntiVirus products: Additional empirical studies

In this paper we describe the design of a new set of empirical studies we will run to test the gains in detection capabilities from using diverse AntiVirus products. This new work builds on previous work on this topic reported in [1, 2, 3]. We describe the motivation for this work, how it extends the previous work and what studies we will conduct

A Study of the Relationship Between Antivirus Regressions and Label Changes

AntiVirus (AV) products use multiple components to detect malware. A component which is found in virtually all AVs is the signature-based detection engine: this component assigns a particular signature label to a malware that the AV detects. In previous analysis [1-3], we observed cases of regressions in several different AVs: i.e. cases where on a particular date a given AV detects a given malware but on a later date the same AV fails to detect the same malware. We studied this aspect further by analyzing the only externally observable behaviors from these AVs, namely whether AV engines detect a malware and what labels they assign to the detected malware. In this paper we present the results of the analysis about the relationship between the changing of the labels with which AV vendors recognize malware and the AV regressions

Vulnerability Prediction Capability: A Comparison between Vulnerability Discovery Models and Neural Network Models

In this paper, we introduce an approach for predicting the cumulative number of software vulnerabilities that is in most cases more accurate than vulnerability discovery models (VDMs). Our approach uses a neural network model (NNM) to model the nonlinearities associated with vulnerability disclosure. Nine common VDMs were used to compare their prediction capability with our approach. The different models were applied to vulnerabilities associated with eight well-known software (four operating systems and four web browsers). The models were assessed in terms of prediction accuracy and prediction bias. Out of eight software we analyzed, the NNM outperformed the VDMs in all the cases in terms of prediction accuracy, and provided smaller values of absolute average bias in seven cases. This study shows that NNMs are promising for accurate predictions of software vulnerabilities disclosures

Predicting the Discovery Pattern of Publically Known Exploited Vulnerabilities

Vulnerabilities with publically known exploits typically form 2-7% of all vulnerabilities reported for a given software version. With a smaller number of known exploited vulnerabilities compared with the total number of vulnerabilities, it is more difficult to model and predict when a vulnerability with a known exploit will be reported. In this paper, we introduce an approach for predicting the discovery pattern of publically known exploited vulnerabilities using all publically known vulnerabilities reported for a given software. Eight commonly used vulnerability discovery models (VDMs) and one neural network model (NNM) were utilized to evaluate the prediction capability of our approach. We compared their predictions results with the scenario when only exploited vulnerabilities were used for prediction. Our results show that, in terms of prediction accuracy, out of eight software we analyzed, our approach led to more accurate results in seven cases. Only in one case, the accuracy of our approach was worse by 1.6%

Cluster-based Vulnerability Assessment Applied to Operating Systems

Organizations face the issue of how to best allocate their security resources. Thus, they need an accurate method for assessing how many new vulnerabilities will be reported for the operating systems (OSs) they use in a given time period. Our approach consists of clustering vulnerabilities by leveraging the text information within vulnerability records, and then simulating the mean value function of vulnerabilities by relaxing the monotonic intensity function assumption, which is prevalent among the studies that use software reliability models (SRMs) and nonhomogeneous Poisson process (NHPP) in modeling. We applied our approach to the vulnerabilities of four OSs: Windows, Mac, IOS, and Linux. For the OSs analyzed in terms of curve fitting and prediction capability, our results, compared to a power-law model without clustering issued from a family of SRMs, are more accurate in all cases we analyzed

Cluster-based Vulnerability Assessment Applied to Operating Systems

Organizations face the issue of how to best allocate their security resources. Thus, they need an accurate method for assessing how many new vulnerabilities will be reported for the operating systems (OSs) they use in a given time period. Our approach consists of clustering vulnerabilities by leveraging the text information within vulnerability records, and then simulating the mean value function of vulnerabilities by relaxing the monotonic intensity function assumption, which is prevalent among the studies that use software reliability models (SRMs) and nonhomogeneous Poisson process (NHPP) in modeling. We applied our approach to the vulnerabilities of four OSs: Windows, Mac, IOS, and Linux. For the OSs analyzed in terms of curve fitting and prediction capability, our results, compared to a power-law model without clustering issued from a family of SRMs, are more accurate in all cases we analyzed

Cluster-based Vulnerability Assessment of Operating Systems and Web Browsers

Organizations face the issue of how to best allocate their security resources. Thus, they need an accurate method for assessing how many new vulnerabilities will be reported for the operating systems (OSs) and web browsers they use in a given time period. Our approach consists of clustering vulnerabilities by leveraging the text information within vulnerability records, and then simulating the mean value function of vulnerabilities by relaxing the monotonic intensity function assumption, which is prevalent among the studies that use software reliability models (SRMs) and nonhomogeneous Poisson process (NHPP) in modeling. We applied our approach to the vulnerabilities of four OSs (Windows, Mac, IOS, and Linux) and four web browsers (Internet Explorer, Safari, Firefox, and Chrome). Out of the total eight OSs and web browsers we analyzed using a power-law model issued from a family of SRMs, the model was statistically adequate for modeling in six cases. For these cases, in terms of estimation and forecasting capability, our results, compared to a power-law model without clustering, are more accurate in all cases but one

Strong coupling theory for driven tunneling and vibrational relaxation

We investigate on a unified basis tunneling and vibrational relaxation in driven dissipative multistable systems described by their N lowest lying unperturbed levels. By use of the discrete variable representation we derive a set of coupled non-Markovian master equations. We present analytical treatments that describe the dynamics in the regime of strong system-bath coupling. Our findings are corroborated by ``ab-initio'' real-time path integral calculations.Comment: 4 LaTeX pages including 3 figure

AntiVirus and Malware Analysis Tool

We present AVAMAT: AntiVirus and Malware Analysis Tool - a tool for analysing the malware detection capabilities of AntiVirus (AV) products running on different operating system (OS) platforms. Even though similar tools are available, such as VirusTotal and MetaDefender, they have several limitations, which motivated the creation of our own tool. With AVAMAT we are able to analyse not only whether an AV detects a malware, but also at what stage of inspection does it detect it and on what OS. AVAMAT enables experimental campaigns to answer various research questions, ranging from the detection capabilities of AVs on OSs, to optimal ways in which AVs could be combined to improve malware detection capabilities