Mankind has entered a new age that is characterized by the automation of many processes. Gone are the days when human labor was required for the completion of certain tasks. Today, it is possible to eliminate human effort in the performance of these tasks. Machine learning is among the numerous elements of technology that have facilitated automation. Essentially, machine learning is a process that allows automated tools to improve how they perform certain tasks. These tools possess human-like capacities that allow them to learn from their environments and experiences. Virtual reality is one of the technological marvels that benefits from machine learning (Papernot et al., 2016). It is employed in a wide range of situations which include providing pilots with a simulated environment for training. While impressive progress has been made in machine learning, there are loopholes that can be exploited by malicious individuals. These loopholes expose systems to the threats of cyber attacks. The issue of cybersecurity as it relates to machine learning is the subject of this paper.
How threats and attacks occur
In the introduction above, virtual reality has been identified as among the technological innovations that are transforming how humans perform certain tasks. Thanks to these innovations, it has become possible to accelerate processes. While caution is exercised in the construction of such technologies as virtual reality, there are weaknesses within systems that allow security breaches to occur (Chung, 2016). Poor design of the systems is mostly to blame for the breaches. When cyber attacks occur, the attackers may gain access to sensitive information. They may choose to doctor the information or profit from it. The costs of security breaches are huge and for this reason, organizations need to exhaust all their resources to ensure that their systems are completely insulated from cyber attacks.
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Another issue that allows breaches and attacks to occur in machine learning is the fact that there lacks complete awareness regarding cybersecurity (Papernot et al., 2016). Many organizations and individuals are simply uninformed about the threat of cybersecurity and the devastation that it can cause. They are therefore unable to put in place measures to ensure that the threats are contained or prevented before they cause damage. The lack of information and awareness combines with poor security protocols to facilitate cyber attacks. There are systems that are not sufficiently secured to ensure that unauthorized access of information does not occur. Machine learning is particularly vulnerable to the effects of cyber attacks. It is important for machine learning to guarantee the confidentiality of information and the privacy of users. When attacks occur, unauthorized individuals gain access to sensitive information and compromise the privacy of users.
While machine learning shares similarities with other technologies as regards cyber attacks and security, it possesses some unique qualities. The attack model of explaining cybersecurity in machine learning offers insights on how the qualities of machine learning allow for cyber attacks to occur. One of these qualities is the attack surface (Papernot et al., 2016). The attack surface of machine learning plays an important role in determining its vulnerability. Poorly designed surfaces that possess security flaws allow unscrupulous individuals to easily launch attacks. On the other hand, properly-developed surfaces are much harder to penetrate and offer additional layers of security to the machine learning (Papernot et al., 2016). The capabilities of the adversary also determine the level of threat of cyber attack that machine learning is exposed to. Adversaries who are weak may lack the skills and tools needed to launch an attack (Papernot et al., 2016). A stronger adversary, on the other hand, poses a real threat to machine learning. This is because they may exploit their expertise and resources to carry out an attack. The aims that the adversary wishes to attain are the other factor that shape cybersecurity in machine learning (Papernot et al., 2016). There are some adversaries who seek to gain access to data and others who set out to render machine learning facilities unusable. Given that their aims are different, these adversaries will employ different techniques and target different elements of machine learning as they seek to achieve their goals.
Guidelines for cybersecurity in machine learning
Machine learning is gaining prominence as it is used in a wide range of situations. While its extended use presents opportunities for human advancement, it also exposes individuals, systems and entire organizations to the threat of cyber attacks. The devastation that can result from cyber attacks cannot be overstated. In the discussion that follows, guidelines that organizations need to adopt to shield their systems from cyber attacks are offered. The implementation of these guidelines will ensure that all sensitive information remains secure and the privacy of users is never compromised.
The best defense against cyber attacks in machine learning is sealing all loopholes that can be exploited by attackers. It is recommended that all limitations that are found in simple hypotheses that are used for making predictions should be addressed (Papernot et al., 2016). This creates a situation where machine learning technologies are completely sealed and insulated from the threat of attacks. The limitations can be addressed by making the underlying algorithms more complex (Papernot et al., 2016). The complexity of the algorithm will make it much difficult for attackers to gain access. It has been mentioned previously that the poor design of security systems in machine learning expose them to attacks. The complex algorithm addresses the poor designs and makes machine learning more secure.
One of the factors that make cyber attacks possible is the failure to minimize access to the system. To ensure that machine learning systems are never attacked, it is advised that access should be severely limited (Hu & Scott, 2014). Access to important and sensitive segments of the system should be particularly monitored closely and limited. For instance, access to interfaces and classes should be limited to safeguard the entire machine learning system from attacks (Hu & Scott, 2014). By limiting access, organizations will be able to ensure that only individuals who have authorization can access the system. Even these individuals should be strictly monitored to ensure that they do only limit themselves to the parts of the system that their level of authorization allows them.
Resource monitoring is another recommendation that is issued for safeguarding machine learning systems against cyber attacks (Hu & Scott, 2014). It is also advised that the resource should be closed. This ensures that the confidentiality of data is never compromised and that only authorized individuals enjoy access to the data. Data confidentiality can also be preserved by avoiding the manipulation of sensitive segments of the machine learning systems (Hu & Scott, 2014). For example, individuals are advised to stay clear of inner classes so as to preserve data integrity. When all these measures are adopted, organizations will be able to secure their systems from cyber attacks.
In conclusion, cybersecurity is an issue that continues to shape the development of machine learning. Despite the gains that machine learning presents, individuals seem unable to keep themselves from worrying about the threat of cyber attacks. Machine learning plays an important role in the functioning of many computer systems. Should the basic components of machine learning become compromised, the very health of organizations would suffer devastation. The organizations could lose their data and the attackers could gain access to confidential information. No effort should be spared by organizations as they seek to secure their computer systems. They should seal all loopholes and adopt the best practices. This will go a long way in insulating their computer systems.
References
Chung, D. (2016). We are Living in a Computer Simulation . Journal of Modern Physics, 7, 1210-1227.
Hu, Y., & Scott, C. (2014). A Case Study of Adopting Security Guidelines in Undergraduate Software Engineering Education. Journal of Computer and Communications, 2, 25-36.
Papernot, N., McDaniel, P., Sinha, A., & Wellman, M. (2016). SoK: Towards the Science ofSecurity and Privacy in Machine Learning. arXiv:1611.03814
