Image-Based malware classification using ensemble of CNN architectures (IMCEC)

This book presents the latest management ideas in knowledge creation and management in readable and non-technical chapters. Leading experts have contributed chapters in their fields of expertise. Each distils his or her subject in a chapter that is accessible to managers who want to learn what can be applied to their organizations without the distracting details of research methodology. Each chapter, however, is based on careful research. The book is organized so that readers can easily find chapters of most interest and value to them. The emphasis is on the practical applications of knowledge to a wide variety of organizations and functional areas.

Knowledge creation and management : new challenges for managers

Forensic-chain: Blockchain based digital forensics chain of custody with PoC in Hyperledger Composer

The number of applications (apps) available for smart devices or Android based IoT (Internet of Things) has surged dramatically over the past few years. Meanwhile, the volume of ill-designed or malicious apps (malapps) has been growing explosively. To ensure the quality and security of the apps in the markets, many approaches have been proposed in recent years to discriminate malapps from benign ones. Machine learning is usually utilized in classification process. Accurately characterizing apps' behaviors, or so-called features, directly affects the detection results with machine learning algorithms. Android apps evolve very fast. The size of current apps has become increasingly large and the behaviors of apps have become increasingly complicated. The extracting effective and representative features from apps is thus an ongoing challenge. Although many types of features have been extracted in existing work, to the best of our knowledge, no work has systematically surveyed the features constructed for detecting Android malapps. In this paper, we are motivated to provide a clear and comprehensive survey of the state-of-the-art work that detects malapps by characterizing behaviors of apps with various types of features. Through the designed criteria, we collect a total of 1947 papers in which 236 papers are used for the survey with four dimensions: the features extracted, the feature selection methods employed if any, the detection methods used, and the scale of evaluation performed. Based on our in-depth survey, we highlight the issues of exploring effective features from apps, provide the taxonomy of these features and indicate the future directions.

/pdf/constructing-features-for-detecting-android-malicious-22xpnwdms4.pdf

Constructing Features for Detecting Android Malicious Applications: Issues, Taxonomy and Directions

The smart home is an environment, where heterogeneous electronic devices and appliances are networked together to provide smart services in a ubiquitous manner to the individuals. organization and people are wide accepting and adopting the functionalities offered by the smart home applications. this can be because of the various advantages, in easing users’ everyday life and work, provided by the rising internet of Things (IoT) technologies and devices, equipped with sensors, cameras, or actuators, and able either to accumulate information from the environment or to perform proper tasks. the main features of smart homes embrace realtime monitoring, remote control, safety from intruders, gas/fire alarm, and so on. Since among smart homes, sensitive and personal data are managed, security and privacy solutions should be put in place, to protect users/businesses’ data against violation try still on guarantee the supply of reliable services. As IoT home devices become increasingly ubiquitous, study’s findings and recommendations contribute to the broader understanding of users’ evolving attitudes towards privacy in smart homes.

/pdf/internet-of-things-iot-of-smart-home-privacy-and-security-vbb7zcyzup.pdf

Internet of Things (IoT) of Smart Home: Privacy and Security

Malware analysis is a process to perform analysis of malware and how to study the components and behavior of malware. On this paper it will use two methods of malware analysis, static analysis and dynamic analysis. Static analysis is a method of malware analysis which done without running the malware. While dynamic analysis is a method of malware analysis which the malware is running in a secure system [7]. Malware analysis is important, since many malware at this day which is not detectable by antivirus. Now viruses are made with special ability to avoid detection from antivirus [9]. On this research we will focus on implementation of malware analysis using static analysis and dynamic analysis method. General Terms Computer Security

/pdf/implementation-of-malware-analysis-using-static-and-dynamic-3hcwbbapbc.pdf

Implementation of Malware Analysis using Static and Dynamic Analysis Method

Digital forensics starts to show its role and contribution in the society as a solution in disclosure of cybercrime. The essential in digital forensics is chain of custody, which is an attempt to preserve the integrity of digital evidence as well as a procedure for performing documentation chronologically toward evidence. The characteristics of digital evidence have caused the handling chain of custody is becoming more complicated and complex. A number of researchers have contributed to provide solutions for the digital chain custody through a different point of views. This paper gives an overview of the extent to which the problem and challenges are faced in the digital chain of custody issue as well as the scope of researches that can be done to contribute in the issue of the digital chain of custody.

/pdf/digital-chain-of-custody-state-of-the-art-4aoyc2h7uy.pdf

Digital Chain of Custody: State of the Art

Chain of custody is the procedure to do a chronological documentation of evidence, and it is an important procedure in the investigation process. Both physical and digital evidence is an important part in the process of investigation and courtroom. However, handling the chain of custody for digital evidence is more difficult than the handling of physical evidence. Nevertheless, the handling of digital evidence should still have the same procedure with the handling of physical evidence. Until now handling the chain of custody for digital evidence is still an open problem with a number of challenges, including the business model of the interaction of the parties that deal with digital evidence, recording of metadata information as well as issues of access control and security for all the handling digital chain of custody. The solution offered in this research is to build a model of Digital Evidence Cabinets as a new approach in implementing the digital evidence handling and chain of custody. The model is constructed through three approaches: Digital Evidence Management Frameworks, Digital Evidence Bags with Tag Cabinets as well as access control and secure communication. The proposed framework is expected to be a solution for the availability of an environment handling of digital evidence and to improve the integrity and credibility of digital evidence.

/pdf/digital-evidence-cabinets-a-proposed-framework-for-handling-28g4k9vbau.pdf

Digital Evidence Cabinets: A Proposed Framework for Handling Digital Chain of Custody

Digital forensics will always include at least human as the one who performs activities, digital evidence as the main object, and process as a reference for the activities followed. The existing framework has not provided a description of the interaction between human, interaction between human and digital evidence, as well as interaction between human and the process itself. A business model approach can be done to provide the idea regarding the interaction in question. In this case, what has been generated by the author in the previous study through a business model of the digital chain of custody becomes the first step in constructing a business model of a digital forensics. In principle, the proposed business model already accommodates major components of digital forensics (human, digital evidence, process) and also considers the interactions among the components. The business model suggested has contained several basic principles as described in The Regulation of Chief of Indonesian National Police (Perkap) No 10/2010. This will give support to law enforcement to deal with cybercrime cases that are more frequent and more sophisticated, and can be a reference for each institution and organization to implement digital forensics activities.

/pdf/a-proposed-digital-forensics-business-model-to-support-sq9604esfq.pdf

A Proposed Digital Forensics Business Model to Support Cybercrime Investigation in Indonesia

is one of the most important assets of the company With the development of information technology is very rapid, the possibility of ever increasing information security disorder This research was conducted to find out the level of information security in organization to give recommendations improvements in information security management at the company This research uses the ISO 27001 by involving the entire clause that exists in ISO 27001 checklist The source of the data used in this study was a detailed questionnaire and interview The respondents in this study are all the employees are in the Office of the Bureau of information technology as many as 14 peoples The results showed maturity level of information security in the Office of the Bureau of information technology is at level 2 The value of the gap between the value of the maturity level of the current and expected level of maturity value is 279 Recommendations for improvement are given requires an understanding of the company and also required coordination with the internal company

/pdf/a-maturity-level-framework-for-measurement-of-information-3mcq7qctby.pdf

Yudi Prayudi

Papers

Implementation of Malware Analysis using Static and Dynamic Analysis Method

Digital Chain of Custody: State of the Art

Digital Evidence Cabinets: A Proposed Framework for Handling Digital Chain of Custody

A Proposed Digital Forensics Business Model to Support Cybercrime Investigation in Indonesia

A Maturity Level Framework for Measurement of Information Security Performance