Three Top Trends: Artificial Intelligence, Sandbox Evading Malware, and Ransomware

Introduction

The rate of technological advances in on the rise across the globe. The advances have brought about various trends. Some of these trends include Artificial Intelligence, Sandbox Evading Malware, and Ransomware. Artificial Intelligence explores the creation of intelligent machines with the abilities to work and react like human beings. Ransomware is a malware that takes over the computer, threaten the owner with harm while denying him, or her access to the data and the attacker requires ransom from the victims while promising to restore the access to data after payment. While the sandbox evading malware make use evasion techniques such as wear and tear to exploit the targets. The malware makes use of the architecture of the system and its past use to evade the sandboxes without exposing the instrumentation indicators effectively. This paper focuses on these three top trends. The paper provides a summary of these trends and discusses the effects of artificial intelligence in shaping the political landscape across the world.

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Artificial Intelligence

Artificial Intelligence is a field that explores the creation of intelligent machines with the abilities to work and react like human beings. Artificial intelligence has enabled the computers to perform tasks such as the speech recognition, plan, learn, and solve problems (Nilsson, 2014; Wenger, 2014). As a branch of computer science, artificial intelligence has become an essential part of the computer industry. The field comprises of very technical and specialized research with core problems revolving around reasoning, knowledge, perception, problem solving, learning, planning, and the ability to manipulate objects. The artificially intelligent machines exhibit the abilities to act and react like the humans. The abilities of these machines are enhanced by the plethora of information from the big data that is now available via various channels. Implementation of artificial intelligence in knowledge engineering requires the access to the objects, its categories, its properties, and other relations (Russell & Norvig, 2016). Access to these key components will help initiate common sense, problem-solving power, and reasoning of the machines.

Thus, artificial intelligence hugely relies on the machine learning to for pattern recognition of the streamed inputs from the data provided (Cohen & Feigenbaum, 2014). Artificial intelligence in these machines requires them to learn without supervision which is vital in the pattern identification from the provided inputs. In order to reach this level, the machines are first subjected to supervision and classification where categories of the key objects are identified and the corresponding sets of numerical inputs, as well as outputs, obtained. The supervision phase of machine learning involves the input of the learning algorithm and the monitoring of the corresponding performance (Nilsson, 2014; Russell & Norvig, 2016). The machine perception of the artificial intelligence relies on the capabilities of the sensory inputs in deducing the various aspects of the world. These aspects are then analyzed using the computer vision. Artificial intelligence also includes the field of robotics. The robots require artificial intelligence to handle different tasks that may be presented to them such as the manipulation of the objects, navigation, mapping, and motion planning.

Ransomware

Ransomware is a malware that takes over the computer, threaten the owner with harm while denying him, or her access to the data and the attacker requires ransom from the victims while promising to restore the access to data after payment (Scaife et al., 2016). However, these promises do not always turn out to be true. In cases of a ransomware attack, the users are always shown how to pay a given amount of fee via the blockchain technologies such as bitcoin as a fee to get a decryption key.

The ransomware often exploits various vectors to access the computer of the victim. The most common channel is the phishing scam. The attacker delivers the attachments via the victim's mail, which he or she downloads and opens permitting the attacker to take control over the computer of the victim. Most of these attachment comes with built-in social engineering tools that deceive the user into giving the attacker administrative privileges over his or her computer (Scaife et al., 2016). Other ransomware such as the NotPetva exploits the existing vulnerabilities of the computer system without tricking the user into giving them the administrative privileges.

After taking over, the malware encrypts the users' files which can only be decrypted using a mathematical key known by the creator. The malware then displays a message that the files are inaccessible and can only be accessed after decryption. They always direct the victim to send the attacker the Bitcoin payment which is untraceable. Some forms of ransomware depict the attacker as a law enforcement agency and shut down the computer of the victim due to the presence of pirated software or pornographic materials on it then demands payment in the form of fines for the victim not to be reported to the authorities (Kharraz et al., 2016). Some ransomware also comes to inform of leakware or doxware where the attacker threatens to publicize the sensitive information that may be on the hard drive of the victim unless he or she pays a ransom.

Sandbox Evading Malware

The various existing forms of malware such as the ransomware, worms, and viruses continue to be popular among the cybercriminals. Nonetheless, firewalls, intrusion detection and prevention devices, as well as the antivirus software, have been vital in the recognition and blockage of these malicious programs. However, these security measures in place only capture what they know from their libraries. For this reason, some malware has been programmed to be environmentally aware of the ability to alter its behaviour on detecting it is being executed in the analysis environment. Various attempts have been put in place by the security companies to ensure that the well-known properties of the analysis environments are replaced with real values while hiding the instrumental artifacts from the malware to prevent them from detecting the operations of the security software. Such steps require sandboxes to be implemented using the virtual machines (Bulazel & Yener, 2017). Using the virtual machines enables the security team to scrub the vendor-specific drivers, BIOS versions, processes, and the VM-revealing indicators.

However, the modern sandboxes implemented are moving away from the visualization and emulation-based systems to the cloud-based systems such as a bare metal hosts. The sophistication of technology is making the fidelity and the transparency of the analysis of the malware systems open and exploitable by the security experts. Thus, malware authors often resort to modification of their programs to target other system characteristics which are indicative of the artificial environment. The sandbox evading malware make use evasion techniques such as wear and tear to exploit the targets. The malware makes use of the architecture of the system and its past use to evade the sandboxes without exposing the instrumentation indicators effectively (Miramirkhani et al., 2017). The sandbox evading malware make use of the decision trees to determine the artificial environment of the target system and not the user device to gain control. Nonetheless, the security firms have developed various strategies to help avoid the attack of this malware. Various statistical models have been developed aimed at capturing the model of the system and the degree of use to try and predict the moves of the sandbox evading software (Bulazel & Yener, 2017). The data collected have been instrumental to the sandbox operators in creating the images of the systems to exhibit their wear and tear state thus preventing the attacks from this malware.

Discussion

Effects of Artificial Intelligence in Shaping the Political Landscape

In the past few years, AI and social media platforms such as Facebook have been used together to shape the democratic processes as well as in changing the nature of public debate in various countries such as the role played by Cambridge Analytica in the most recent US elections. Through the use of AI, the company was able to give unexpected outcomes in the election. The integration of Artificial Intelligence with the data provided by the social media made it possible for the company to make tailored messaged that were delivered to voters in personalized ways (Biddle, 2018). The ability to personalize these messages tremendously changed the public view, truth, and opinion resulting in changes in the political debates. The role played by AI during the recent elections made it difficult to trust news from single authoritative sources as it brought about the issues of fake news (Lyons, 2018). The AI and social media led to the spread of fake news due to lack of content control on the social media platforms.

Conclusion

The three top trends discussed in this paper, especially artificial intelligence have brought drastic changes in the technological world. Artificial intelligence has changed the ways people interact with the machines. It has also been used with other existing platforms such as social media to change the way individuals interact in the democratic processes such as elections as it has been used as a major tool to shape people's opinion. Ransomeware has also been exploited since the introduction of untraceable Blockchain modes of payment. The various forms of malware have also become more sophisticated with time to evade the sandbox.

References

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