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Given how smart today’s malware authors have become through employing highly sophisticated techniques, it is only logical that methods be developed to combat the most potent threats, particularly where the malware is stealthy and makes indicators of compromise (IOC) difficult to detect. After the analysis is completed, the output can be employed to detect and then counteract the attack. The goal of this work is to propose a machine learning approach to improve malware detection by combining the strengths of both supervised and unsupervised machine learning techniques. This study is essential as malware has certainly become ubiquitous as cyber-criminals use it to attack systems in cyberspace. Malware analysis is required to reveal hidden IOC, to comprehend the attacker’s goal and the severity of the damage and to find vulnerabilities within the system.

This research proposes a hybrid approach for dynamic and static malware analysis that combines unsupervised and supervised machine learning algorithms and goes on to show how Malware exploiting steganography can be exposed.

The tactics used by malware developers to circumvent detection are becoming more advanced with steganography becoming a popular technique applied in obfuscation to evade mechanisms for detection. Malware analysis continues to call for continuous improvement of existing techniques. State-of-the-art approaches applying machine learning have become increasingly popular with highly promising results.

Cyber security researchers globally are grappling with devising innovative strategies to identify and defend against the threat of extremely sophisticated malware attacks on key infrastructure containing sensitive data. The process of detecting the presence of malware requires expertise in malware analysis. Applying intelligent methods to this process can aid practitioners in identifying malware’s behaviour and features. This is especially expedient where the malware is stealthy, hiding IOC.

A proper understanding of malware characteristics is necessary to protect massive data generated because of the advances in Internet of Things (IoT), big data and the cloud. Because of the encryption techniques used by the attackers, network security experts struggle to develop an efficient malware detection technique. Though few machine learning-based techniques are used by researchers for malware detection, large amounts of data must be processed and detection accuracy needs to be improved for efficient malware detection. Deep learning-based methods have gained significant momentum in recent years for the accurate detection of malware. The purpose of this paper is to create an efficient malware detection system for the IoT using Siamese deep neural networks.

In this work, a novel Siamese deep neural network system with an embedding vector is proposed. Siamese systems have generated significant interest because of their capacity to pick up a significant portion of the input. The proposed method is efficient in malware detection in the IoT because it learns from a few records to improve forecasts. The goal is to determine the evolution of malware similarity in emerging domains of technology.

The cloud platform is used to perform experiments on the Malimg data set. ResNet50 was pretrained as a component of the subsystem that established embedding. Each system reviews a set of input documents to determine whether they belong to the same family. The results of the experiments show that the proposed method outperforms existing techniques in terms of accuracy and efficiency.

The proposed work generates an embedding for each input. Each system examined a collection of data files to determine whether they belonged to the same family. Cosine proximity is also used to estimate the vector similarity in a high-dimensional area.

The purpose of this research is to show that companies world‐wide are being placed under increasing pressure by an onslaught of cyber risks and malware is one of the most common sources of security failures at present. The position in South Africa is no exception and malware presents a very real danger to corporate South Africa's information assets, resources and systems, as it has the capacity to undermine firewalls, hijack Virtual Private Networks (VPN's) and defeat digital signatures. The threats associated with malware have several salient legal issues embedded in it and these are elaborated in the paper. Unfortunately, corporate South Africa is still largely ignorant of the range of tools available to the “Darkside” and the potential legal consequences which may ensue if this cyber risk materialises. The article helps in the understanding of the problem.

This paper examines malware, and more specifically legal liability for malware from a South African perspective. The account contained in this contribution deals with the question whether or not a company who falls victim to a malware attack or unwillingly facilitates such an attack, may be held legally liable. This is done by giving a brief overview of the nature of the cyber risk malware, before moving on to observe the consequences which may ensue if a malware attack occurs. Corporations who fall victim to malware attacks or unwittingly facilitates such an attack may suffer: direct damage; indirect damage; and physiological damage.

It will be pointed out that malware attacks may result in legal liability in civil law for the “victim” company because of its failure to take reasonable steps to secure the information assets, resources and systems of the company.

It will furthermore be observed that companies who unwillingly facilitate malware attacks, where for instance the company's own employee uses company resources to launch a virus attack, may be faced with legal liability in the form of vicarious liability.

Suggestions are made on how to avoid legal liability for failed information security.

No such a study has yet been undertaken in South Africa as most view the law and technology as strange and perhaps dangerous bedfellows. The study will also be of use, value and interest to the library and information community outside South Africa since it raises an issue of real significance.

The purpose of this paper is to point out, to non‐technical library users, the hazards of using the library's public computers.

The purpose of this paper is to articulate the dangers faced by library users, whether they be students, faculty, or staff. This paper is geared towards education and enlightenment of non‐technical users of public computers.

There are many hazards to computing, both online and off. Most public computers are not well prepared to cope with worms, Trojans and other malware. There are remedies for a lot of the malware, but as in an arms race it is a matter of catching up with the latest weapon to be presented.

Since public computers are so vulnerable to attack, library IT must be constantly vigilant for the menaces that are present. The paper shows how computers can be protected.

If this can help save one computer's workload by making people aware of the dangers involved, the information is well worthwhile.

This paper aims to investigate the impact of perceived price level and information security awareness on computer users’ attitude. Moreover, this study aims to investigate the effect of attitude, subjective norms and perceived behavioral control (PBC) on intention to use anti-malware software.

Data were collected using a structured questionnaire from 225 students of five public universities in Malaysia. Purposive sampling technique was used in this study. AMOS 24 was used to test the research framework using a two-step approach.

Findings give support to some of the hypotheses developed with R2 values of 0.521 for attitude and 0.740 for intention. Perceived price level had a negative effect on attitude while information security awareness had a positive effect on attitude and intention. Attitude, subjective norms and PBC were all positively related to intention, but perceived price level did not affect intention. This suggests that benefits of using anti-malware are more than its price value. Therefore, the price has no direct effect on intention to use.

University computer networks are as open and inviting as their campuses. Therefore, this research can be helpful to the universities to safeguard their networks and encourage the students to use anti-malware. However, using anti-malware software will enable an individual to identify and prioritize security risks, quickly detect and mitigate security breaches, improve the understanding of security gaps and safeguard the sensitive data by minimizing the risks related to malware.

This study ventured to model the information security behavior of anti-malware usage by individual users by using the theory of planned behavior with the addition of two new variables, perceived price level and information security awareness to explain the behavior better.

This paper proposes an approach to deal with malware and botnets, which in recent years have become one of the major threats in the cyber world. These malicious pieces of software can cause harm not only to the infected victims but also to actors at a much larger scale. For this reason, defenders, namely, security researchers and analysts, and law enforcement have fought back and contained the spreading infections. However, the fight is fundamentally asymmetric.

In this paper, the authors argue the need to equip defenders with more powerful active defence tools such as malware and botnets, called antidotes, which must be used as last resort to mitigate malware epidemics. Additionally, the authors argue the validity of this approach by considering the ethical and legal concerns of leveraging sane and compromised hosts to mitigate malware epidemics. Finally, the authors further provide evidence of the possible success of these practices by applying their approach to Hlux, Sality and Zeus malware families.

Although attackers have neither ethical nor legal constraints, defenders are required to follow much stricter rules and develop significantly more intricate tools. Additionally, attackers have been improving their malware to make them more resilient to takeovers.

By combining existing research, the authors provide an analysis and possible implication of a more intrusive yet effective solution for fighting the spreading of malware.

This article aims to provide an interactive model for hands on training of malware analysis. Cyberwar games are the highly stylized representation of cyber conflicts in a simulation model. Game models are helpful in understanding the phenomenon of cyber attacks as well as to evolve new techniques of detection. Cyber security trainings are generally very challenging. Cyber test beds make such trainings easy both for trainees and trainers. However, it is not feasible for each organization to build a network for the sole purpose of hands-on training for employees. Therefore, it is desirable to build an interactive environment that is interesting and free of cost as well.

After exploring existing cyberwar games and their techniques, limitation and strengths, this paper presents a design to merge the cyber attacks into a unique model of war game for detection and analysis of malware. This research designs a malware analysis testbed using online free resources. The authors have used the platform of Cyber Defense Technology Experimental Research (DETER). This study proposed model of a testbed that supports malware reverse engineering scenarios, exercise logs and analysis to develop reverse engineering tactics.

The proposed cyber testbed is an automated system that can be used as a platform to train cyber warriors. A few features of the proposed testbed are as follows: testbed provides real or seemingly real malware communication with the real world. It includes automated decisions for the detection of malicious behavior without human intervention. The author gives a design to develop free of cost mechanism for remote learning of highly technical cyber security areas, and this simulation is for malware analysis.

Cyberwar games are built for strengthening offensive and defensive capabilities in cyberspace. For this purpose, many simulations, as well as emulation platforms, can be found. Some are free and open-source, whereas others are commercial and quite expensive. Existing testbeds have limitations in respect of cyberwar games for creating innovative scenarios. Existing literature does not offer any attack and response scenario developed for malware detection through some existing open-source and online simulation or emulation environments. This research includes an analysis of the existing platforms as well as the design of a new model for malware analysis and training.

An organization’s ability to successfully manage intellectual capital is determined by the actions of its employees to prevent or minimize information security incidents. To prevent more data breaches to intellectual capital, organizations must provide regular cybersecurity awareness training for all personnel. The purpose of this paper is to investigate the effect of different evidence-based cybersecurity training methods on employees’ cybersecurity risk perception and self-reported behavior.

The study participants were randomly assigned into four groups (i.e. malware report, malware videos, both malware report and malware videos and no interventions) to assess the effects of cybersecurity training on their perceptions of vulnerability, severity, self-efficacy, security intention as well as their self-reported cybersecurity behaviors.

The results show that evidence-based malware report is a relatively better training method in affecting employees’ intentions of engaging in recommended cybersecurity behaviors comparing with the other training methods used in this study. A closer analysis suggests whether the training method contains self-relevant information could make a difference to the training effects.

This paper reports an in-depth investigation on how different evidence-based cybersecurity training methods impact employees’ perceptions of susceptibility, severity, self-efficacy, security intention as well as on their self-reported cybersecurity behaviors.

Online social network (OSN) users have a high propensity to malware threats due to the trust and persuasive factors that underpin OSN models. The escalation of social engineering malware encourages a growing demand for end-user security awareness measures. The purpose of this paper is to take the theoretical cybersecurity awareness model TTAT-MIP and test its feasibility via a Facebook app, namely social network criminal (SNC).

The research employs a mixed-methods approach to evaluate the SNC app. A system usability scale measures the usability of SNC. Paired samples t-tests were administered to 40 participants to measure security awareness – before and after the intervention. Finally, 20 semi-structured interviews were deployed to obtain qualitative data about the usefulness of the App itself.

Results validate the effectiveness of OSN apps utilising a TTAT-MIP model – specifically the mass interpersonal persuasion (MIP) attributes. Using TTAT-MIP as a guidance, practitioners can develop security awareness systems that better leverage the intra-relationship model of OSNs.

The primary limitation of this study is the experimental settings. Although the results testing the TTAT-MIP Facebook app are promising, these were set under experimental conditions.

SNC enable persuasive security behaviour amongst employees and avoid potential malware threats. SNC support consistent security awareness practices by the regular identification of new threats which may inspire the creation of new security awareness videos.

The structure of OSNs is making it easier for malicious users to carry out their activities without the possibility of detection. By building a security awareness programme using the TTAT-MIP model, organisations can proactively manage security awareness.

Many security systems are cumbersome, inconsistent and non-specific. The outcome of this research provides organisations and security practitioners with a framework for designing and developing proactive and tailored security awareness systems.

Most people are probably aware of malware, but they may not be aware of malware in what may be its most dangerous form, i.e. causing physical harm, even death, to individuals. This paper aims to document how software can cause malicious harm to individuals by attacking modern systems that appear to be neglected and under-researched.

This paper will review some of the most significant areas of concern with respect to end of days malware, i.e. malware that has a dangerous intent. The areas included are automobiles, medical devices and air traffic control systems.

The potential harmful effects of malware are often not well known by consumers and businesses around the world. These issues are not limited to just financial harm. Lives can actually be in danger. Underestimating the importance of cybersecurity and understanding the dangers that are associated with advancing technology are global issues that will continue unless there is enough awareness to force businesses and governments to address these issues. It is critical that safeguards are established.

While many papers have been written about malware and the implications of having malicious software infect a computer or a network, little attention has been paid to “end of days” malware. With advancing technology, malware now has the ability to cause serious injury or death to individuals who have minimal or no knowledge of the potential consequences of, for example, driving in an automobile, wearing or having an internal medical device or flying on an airplane. It is up to businesses and governments to address these issues.