Low-Complexity Content Popularity Prediction Mechanism
Overview of the Paper
Self-driving car technology has developed a lot in recent times, allowing people to relieve the burden of manual navigation. This would give them more time while travelling, and it is no longer a distant dream. The development of an On-Board Unit or OBU is being done, which would be placed in the car to better use its existing infotainment abilities. Alongside this, there must be a Road-Side Unit or RSU that would help the OBU and an MBS obtain the popularity concept of other nearby devices. This research has proposed the production of a “Low-Complexity Content Popularity Prediction Mechanism” that would help extract the popular patterns of local contents present in self-driving cars that are using Long Short-Term Memory or LSTM-based prediction mechanism. But, to provide an even better onboard experience, there is a need for the popular content of other self-driving cars [1]. This would have privacy issues, and hence a Hierarchical Federal Averaging Algorithm is implemented on local models that would be useful in getting the “Regional and Global Content Popularity Prediction Model” in the RSU and macro cell base station (MBS), respectively. This was extensively researched and experimented upon using real-world data, which revealed that the proposed algorithm could be very helpful in making good use of the cache space by maximum use of the local cache hit ratio and also for the minimisation of the content retrieval cost of various self-driving cars when compared to any other method.
Proactive content catching is, thus, a very reliable and effective strategy to deal with the rapid rise in the requests for content obtained from diverse passengers of self-driving cars. But, due to the limitations in the cache of the OBU, it is important to have a self-attention technique that would help in proactive content caching. However, this concept is still very effective in combining the self-attention mechanism and the LSTM model of the OBU for allowing good proactive content caching of the infotainment contents. The Hierarchical Federal Learning Architecture is very useful for developing regional and global strategy models, and the extensive experimentation would allow for a better cache hit ratio.
The advent of Artificial Intelligence has created a revolution in technologies. Self-driving cars are one such application or implementation of the technologies. The rise in the usage of self-driving cars requires privacy and security concerns in the working environment. Proactive Content Caching is one such technology that helps in improving the quality of experience of self-driving cars and better decision-making phenomena. The research is conducted so that the users of such cars can be protected from any privacy and security threats while driving these technologies and get an overall good experience while using the technologies.
Research methodology helps the researchers gather information regarding how data has been collected and analysed in the research. In this research, the primary experimental data analysis technique has been considered. Here machine learning algorithms are taken into consideration for performing data analysis. Hierarchical Federated Averaging is used for determining proactive caching of data. Here, the result of the research has been obtained with the help of the statistical analysis of the data obtained with the help of the algorithms.
Hierarchical Federal Averaging Algorithm
From the research findings, it is evident that the technique that the researcher proposes shall help manage data in self-driving cars such that correct decisions can be made for a better experience. The authors have suggested using the self-attention technique for the research with the help of the machine learning algorithm. This has helped increase the hit ratio of cache data alongside reducing the cost of retrieval. The technology helps in cache optimisation as well. All of them shall help in providing a better experience to the users. The self-attention technique helps in gathering and predicting the local contents. This can also help measure the user satisfaction level, which is again one of the most important parts of the organisations developing self-driving cars.
In the age of technology software, security solutions are increasingly falling short of the security mark due to malware and hackers bypassing them. Malware or malicious software is hacking into every system and network and bypassing high-class security in hardware and software. Malware is nothing but software, a set of files or code that can be delivered in a network or to a device and has certain harmful contents within them which can cause damage to the target. There are several variants of malware which is why there are several ways in which malware can cause damage to a computer. Computer viruses are also a type of malware and many others like Trojans, worms, bots, spyware, ransomware, etc. Hence, to protect sensitive information from these malicious contents, it is essential to have proper security. This can be hardware security or software security. This research paper deals with implementing a machine learning algorithm called the LSTM algorithm, which helps detect malware approaches. This has increased the requirement for hardware-based solutions that can be very costly and with complex designs and dynamic power consumption. The current hardware solutions are created on statistical learning blocks with uncharacteristic features of processor behaviour, network traffic, and system cell. The learning technique performance in the hardware depends upon the training data and its quality. But, in a processor, there are a very limited number of PMC or performance monitoring counters which means that simultaneously, they cannot monitor more than a few behavioural events. This has become a major issue in the PMC based hardware solutions, working with incompetent quantity and quality of data. This research helps detect the architectural features that would help in malware detection. It also reveals the difference between the workloads of malware and benign based upon their performance counter information. Most of the malware is easily detectable with the help of the performance characteristics, but a few behave similarly to benign workloads and must be dealt with in-depth. This research paper also emphasises multiple steps which can investigate the critical issues associated with the PMC-based malware detection solution. These steps include distribution-based feature selection, statistical characterisation of malware, provision of architectural design alternatives for detecting hardware-based malware, and trade-off analysis for the detection of accuracy and time [2]. The results of this research have revealed that with the current technologies and existing number of PMCs present, it is impossible to achieve the anticipated accuracy in malware detection. To be more accurate in malware detection in real-time, improvement in the implementation accuracy is important, along with hardware acceleration schemes. Both of these would increase the chances of malware detection by nearly five to ten per cent and increase the speed of malware detection by nearly ten per cent with a very little additional cost.
Self-Attention Technique
Malware is one of the notorious issues that highly affect the work process to such an extent that system security has to be updated. This is inclusive of the fact that the hackers develop malware so that their behaviour can remain unrecognised. The rationale behind the development of this paper is to understand and classify malware behaviours such that the people working with information systems and information technologies are well aware of the malware behaviour. In this manner, they can make the people aware of the events, leading to this kind of attack on the organisation.
There are two methods used for the process of conducting the research. The first one is the secondary data. This has been used to gather necessary details regarding the background research conducted. The other process which has been used for the research is primary data analysis. Here, experiments have been conducted with the help of machine learning algorithms, which are analysed statistically.
From the above discussion, it is evident that the technologies in use often attract issues like malware and ransomware. Making use of hardware solutions to detect and prevent malware is becoming some of the most important parts of the current society. To obtain high-quality data, Degree of Distribution (DoD) metrics are put to use, which shall help statistically analyse the data and determine benign samples from malware. This technique was highly successful as the system could detect and differentiate malware differently. The technology successfully helped detect information about the behaviour of the malware attacks, which is good as this can help the real-time development of the software and process that can benefit the entire business sector or any other sector in the future for determining the malware activities.
From the overall discussion of the paper, it is evident that both the paper portrays the benefits and usage of machine learning algorithms. Both the work has been conducted using experimental data analysis techniques and has made use of the LSTM algorithm to obtain results of the data analysis. However, for paper 1, Proactive Content Caching is used for Hierarchical Federated Learning, using the Self-Attention mechanism within the organisations. The aim is to improve the users’ quality of experience by using those self-driving cars. However, for the second paper, although the same algorithm has been used to develop a technique that can help detect and differentiate the methods that can lead to a malware attack in an organisation. The papers are done with the help of experimental practices, where the researchers have shown how algorithms can be used to detect issues and prevent the same. The paper is well aligned with the requirement such that the people can read them and identify the steps which have been taken up by the researchers for the development of the data analysis section of the research.
Each research has kept the end of the research open where future work can be initiated from where the subject has been left. The aim is to deliver a hassle-free, secure environment to the users of the technologies. They can use the same to determine how systems can be secured from any external aspects. The researchers have also kept the layout of the papers simple and illustrative such that detailed experimentation results can be portrayed properly. This is inclusive of the fact that the researchers are well aware that the diagrammatic representation helps perform the analysis of the data better.
Information security and the effectiveness of systems are two of the most important and happening aspects of contemporary society. With the large-scale increase in the data flow in the information systems, the process of decision making in self-driving cars has become more complicated. The paper that the researchers have conducted has given an insight into how machine learning algorithms can be used in an organisation to better the decision-making process. This can help improve the behaviour of the systems and help in dealing with the requirements in the local region better. The other paper helps in dealing with the security issues of the systems, including the issues like malware. The results of this research have revealed that with the current technologies and existing number of PMCs present, it is not likely to achieve the accuracy desired in the process of malware detection. To be more accurate in malware detection in real-time, it is important to implement accuracy improvement schemes as well as hardware acceleration schemes. The paper has discussed two innovative measures that can help improve people’s lives in the future with the benefit of technology.
Conclusion
After conducting the research, it can be said that information technologies like Machine Learning and AI have immense benefits in the workplace and even in the lives of individuals. However, one must be aware of the loopholes of the technologies to avoid issues like Malware and Ransomware. Not only is that, but these algorithms and modern technologies can also help determine the ways of detecting issues in the systems, especially the machine learning algorithms. Thus, they can be used for various purposes of learning and analysis in the future.
References
- Khanal, K. Thar, and E.N. Huh, Route-Based Proactive Content Caching Using Self-Attention in Hierarchical Federated Learning. IEEE Access, 10, pp.29514-29527, 2022.
- Pattee, S.M. Anik, and B.K. Lee, Performance Monitoring Counter Based Intelligent Malware Detection and Design Alternatives, IEEE Access, 10, pp.28685-28692, 2022.
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