Agent-based Vs Agent-less Sandbox for Dynamic Behavioral Analysis

Malicious software is detected and classified by either static analysis or dynamic analysis. In static analysis, malware samples are reverse engineered and analyzed so that signatures of malware can be constructed. These techniques can be easily thwarted through polymorphic, metamorphic malware, obfuscation and packing techniques, whereas in dynamic analysis malware samples are executed in a controlled environment using the sandboxing technique, in order to model the behavior of malware. In this paper, we have analyzed Petya, Spyeye, VolatileCedar, PAFISH etc. through Agent-based and Agentless dynamic sandbox systems in order to investigate and benchmark their efficiency in advanced malware detection

Malware Resistant Data Protection in Hyper-connected Networks: A survey

Data protection is the process of securing sensitive information from being corrupted, compromised, or lost. A hyperconnected network, on the other hand, is a computer networking trend in which communication occurs over a network. However, what about malware. Malware is malicious software meant to penetrate private data, threaten a computer system, or gain unauthorised network access without the users consent. Due to the increasing applications of computers and dependency on electronically saved private data, malware attacks on sensitive information have become a dangerous issue for individuals and organizations across the world. Hence, malware defense is critical for keeping our computer systems and data protected. Many recent survey articles have focused on either malware detection systems or single attacking strategies variously. To the best of our knowledge, no survey paper demonstrates malware attack patterns and defense strategies combinedly. Through this survey, this paper aims to address this issue by merging diverse malicious attack patterns and machine learning (ML) based detection models for modern and sophisticated malware. In doing so, we focus on the taxonomy of malware attack patterns based on four fundamental dimensions the primary goal of the attack, method of attack, targeted exposure and execution process, and types of malware that perform each attack. Detailed information on malware analysis approaches is also investigated. In addition, existing malware detection techniques employing feature extraction and ML algorithms are discussed extensively. Finally, it discusses research difficulties and unsolved problems, including future research directions.Comment: 30 pages, 9 figures, 7 tables, no where submitted ye

Fisher exact Boschloo and polynomial vector learning for malware detection

Computer technology shows swift progress that has infiltrated people’s lives with the candidness and pliability of computers to work ease shows security breaches. Thus, malware detection methods perform modifications in running the malware based on behavioral and content factors. The factors are taken into consideration compromises of convergence rate and speed. This research paper proposed a method called fisher exact Boschloo and polynomial vector learning (FEB-PVL) to perform both content and behavioral-based malware detection with early convergence to speed up the process. First, the input dataset is provided as input then fisher exact Boschloo’s test Bernoulli feature extraction model is applied to obtain independent observations of two binary variables. Next, the extracted network features form input to polynomial regression support vector learning to different malware classes from benign classes. The proposed method validates the results with respect to the malware and the benign files. The present research aimed to develop the behaviors to detect the accuracy process of the features that have minimum time speeds the overall performances. The proposed FEB-PVL increases the true positive rate and reduces the false positive rate and hence increasing the precision rate using FEB-PVL by 7% compared to existing approaches

WOPR: A Dynamic Cybersecurity Detection and Response Framework

Malware authors develop software to exploit the flaws in any platform and application which suffers a vulnerability in its defenses, be it through unpatched known attack vectors or zero-day attacks for which there is no current solution. It is the responsibility of cybersecurity personnel to monitor, detect, respond to and protect against such incidents that could affect their organization. Unfortunately, the low number of skilled, available cybersecurity professionals in the job market means that many positions go unfilled and cybersecurity threats are unknowingly allowed to negatively affect many enterprises.The demand for a greater cybersecurity posture has led several organizations to de- velop automated threat analysis tools which can be operated by less-skilled infor- mation security analysts and response teams. However, the diverse needs and organizational factors of most businesses presents a challenge for a “one size fits all” cybersecurity solution. Organizations in different industries may not have the same regulatory and standards compliance concerns due to processing different forms and classifications of data. As a result, many common security solutions are ill equipped to accurately model cybersecurity threats as they relate to each unique organization.We propose WOPR, a framework for automated static and dynamic analysis of software to identify malware threats, classify the nature of those threats, and deliver an appropriate automated incident response. Additionally, WOPR provides the end user the ability to adjust threat models to fit the risks relevant to an organization, allowing for bespoke automated cybersecurity threat management. Finally, WOPR presents a departure from traditional signature-based detection found in anti-virus and intrusion detection systems through learning system-level behavior and matching system calls with malicious behavior