Machine-assisted Cyber Threat Analysis using Conceptual Knowledge Discovery

Over the last years, computer networks have evolved into highly dynamic and interconnected environments, involving multiple heterogeneous devices and providing a myriad of services on top of them. This complex landscape has made it extremely difficult for security administrators to keep accurate and be effective in protecting their systems against cyber threats. In this paper, we describe our vision and scientific posture on how artificial intelligence techniques and a smart use of security knowledge may assist system administrators in better defending their networks. To that end, we put forward a research roadmap involving three complimentary axes, namely, (I) the use of FCA-based mechanisms for managing configuration vulnerabilities, (II) the exploitation of knowledge representation techniques for automated security reasoning, and (III) the design of a cyber threat intelligence mechanism as a CKDD process. Then, we describe a machine-assisted process for cyber threat analysis which provides a holistic perspective of how these three research axes are integrated together

Pattern for malware remediation – A last line of defence tool against Malware in the global communication platform

Malware is becoming a major problem to every organization that operates on the global communication platform. The malicious software programs are advancing in sophistication in many ways in order to defeat harden deployed defenses. When an organization’s defense fails to keep this malice invasion out, the organization would incur significant amount of risks and damages. Risks include data leakage, inability to operate and tarnished corporate image. Damages include compensation costs to customers and partners, service unavailability and loss of customers’ and partners’ confidence in the organization. This in turn will affect the organization’s business continuity. In order to manage the risks and damages induced by Malware incidents, incident responders are called upon to be the last line of defense against the digital onslaught assault. However incident responders are challenged too by the deep levels of knowledge, skills and experience required to contain the ever advancing and persistent Malware. This paper proposes the establishment of a Pattern template for Malware Remediation to aid incident responders to overcome their competency limitations in order to provide organizations the tool to repel Malware and to reduce the associated risks. Examples and details of the proposed patters are provided with discussions on future direction of the research work

The Malware Analysis Body of Knowledge (MABOK)

The ability to forensically analyse malicious software (malware) is becoming an increasingly important discipline in the field of Digital Forensics. This is because malware is becoming stealthier, targeted, profit driven, managed by criminal organizations, harder to detect and much harder to analyse. Malware analysis requires a considerable skill set to delve deep into malware internals when it is designed specifically to detect and hinder such attempts. This paper presents a foundation for a Malware Analysis Body of Knowledge (MABOK) that is required to successfully forensically analyse malware. This body of knowledge has been the result of several years of research into malware dissection

Comprehensive Security Framework for Global Threats Analysis

Cyber criminality activities are changing and becoming more and more professional. With the growth of financial flows through the Internet and the Information System (IS), new kinds of thread arise involving complex scenarios spread within multiple IS components. The IS information modeling and Behavioral Analysis are becoming new solutions to normalize the IS information and counter these new threads. This paper presents a framework which details the principal and necessary steps for monitoring an IS. We present the architecture of the framework, i.e. an ontology of activities carried out within an IS to model security information and User Behavioral analysis. The results of the performed experiments on real data show that the modeling is effective to reduce the amount of events by 91%. The User Behavioral Analysis on uniform modeled data is also effective, detecting more than 80% of legitimate actions of attack scenarios

Impact Analysis of Malware Based on Call Network API with Heuristic Detection Method

Malware is a program that has a negative influence on computer systems that don\u27t have user permissions. The purpose of making malware by hackers is to get profits in an illegal way. Therefore, we need a malware analysis. Malware analysis aims to determine the specifics of malware so that security can be built to protect computer devices. One method for analyzing malware is heuristic detection. Heuristic detection is an analytical method that allows finding new types of malware in a file or application. Many malwares are made to attack through the internet because of technological advancements. Based on these conditions, the malware analysis is carried out using the API call network with the heuristic detection method. This aims to identify the behavior of malware that attacks the network. The results of the analysis carried out are that most malware is spyware, which is lurking user activity and retrieving user data without the user\u27s knowledge. In addition, there is also malware that is adware, which displays advertisements through pop-up windows on computer devices that interfaces with user activity. So that with these results, it can also be identified actions that can be taken by the user to protect his computer device, such as by installing antivirus or antimalware, not downloading unauthorized applications and not accessing unsafe websites. &nbsp