Adoption of Machine Learning With Adaptive Approach for Securing CPS

The cyber physical system safety and security is the major concern on the incorporated components with interface standards, communication protocols, physical operational characteristics, and real-time sensing. The seamless integration of computational and distributed physical components with intelligent mechanisms increases the adaptability, autonomy, efficiency, functionality, reliability, safety, and usability of cyber-physical systems. In IoT-enabled cyber physical systems, cyber security is an essential challenge due to IoT devices in industrial control systems. Computational intelligence algorithms have been proposed to detect and mitigate the cyber-attacks in cyber physical systems, smart grids, power systems. The various machine learning approaches towards securing CPS is observed based on the performance metrics like detection accuracy, average classification rate, false negative rate, false positive rate, processing time per packet. A unique feature of CPS is considered through structural adaptation which facilitates a self-healing CPS.

A Review of Machine Learning Methods Applied for Handling Zero-Day Attacks in the Cloud Environment

Cloud computing is an emerging technological paradigm that provides a flexible, scalable, and reliable infrastructure and services for organizations. Services of cloud computing is based on sharing; thus, it is open for attacker to attack on its security. The main thing that grabs the organizations to adapt the cloud computing technology is cost reduction through optimized and efficient computing, but there are various vulnerabilities and threats in cloud computing that affect its security. Providing security in such a system is a major concern as it uses public network to transmit data to a remote server. Therefore, the biggest problem of cloud computing system is its security. The objective of the chapter is to review Machine learning methods that are applied to handle zero-day attacks in a cloud environment.

Big Data Analytics for Intrusion Detection

Intrusion detection has become a problem of big data, with a semantic gap between vast security data sources and real knowledge about threats. The use of machine learning (ML) algorithms on big data has already been successfully applied in other domains. Hence, this approach is promising for dealing with cyber security's big data problem. Rather than relying on human analysts to create signatures or classify huge volumes of data, ML can be used. ML allows the implementation of advanced algorithms to extract information from data using behavioral analysis or to find hidden correlations. However, the adversarial setting and the dynamism of the cyber threat landscape stand as difficult challenges when applying ML. The next generation security information and event management (SIEM) systems should provide security monitoring with the means for automation, orchestration and real-time contextual threat awareness. However, recent research shows that further work is needed to fulfill these requirements. This chapter presents a survey on recent work on big data analytics for intrusion detection.

Hybridization of Machine Learning Algorithm in Intrusion Detection System

In recent years, the field of machine learning grows very fast both on the development of techniques and its application in intrusion detection. The computational complexity of the machine learning algorithms increases rapidly as the number of features in the datasets increases. By choosing the significant features, the number of features in the dataset can be reduced, which is critical to progress the classification accuracy and speed of algorithms. Also, achieving high accuracy and detection rate and lowering false alarm rates are the major challenges in designing an intrusion detection system. The major motivation of this work is to address these issues by hybridizing machine learning and swarm intelligence algorithms for enhancing the performance of intrusion detection system. It also emphasizes applying principal component analysis as feature selection technique on intrusion detection dataset for identifying the most suitable feature subsets which may provide high-quality results in a fast and efficient manner.

Applications of Machine Learning in Cyber Security

With the exponential rise in technological awareness in the recent decades, technology has taken over our lives for good, but with the application of computer-aided technological systems in various domains of our day-to-day lives, the potential risks and threats have also come to the fore, aiming at the various security features that include confidentiality, integrity, authentication, authorization, and so on. Computer scientists the world over have tried to come up, time and again, with solutions to these impending problems. With time, attackers have played out complicated attacks on systems that are hard to comprehend and even harder to mitigate. The very fact that a huge amount of data is processed each second in organizations gave birth to the concept of Big Data, thereby making the systems more adept and intelligent in dealing with unprecedented attacks on a real-time basis. This chapter presents a study about applications of machine learning algorithms in cyber security.

Applications of Machine Learning in Cyber Security Domain

Knowledge revolution is transforming the globe from traditional society to a technology-driven society. Online transactions have compounded, exposing the world to a new demon called cybercrime. Human beings are being replaced by devices and robots, leading to artificial intelligence. Robotics, image processing, machine vision, and machine learning are changing the lifestyle of citizens. Machine learning contains algorithms which are capable of learning from historical occurrences. This chapter discusses the concept of machine learning, cyber security, cybercrime, and applications of machine learning in cyber security domain. Malware detection and network intrusion are a few areas where machine learning and deep learning can be applied. The authors have also elaborated on the research advancements and challenges in machine learning related to cyber security. The last section of this chapter lists the future trends and directions in machine learning and cyber security.

A Review on Cyber Security Mechanisms Using Machine and Deep Learning Algorithms

Digitalization is the buzz word today by which every walk of our life has been computerized, and it has made our life more sophisticated. On one side, we are enjoying the privilege of digitalization. On the other side, security of our information in the internet is the most concerning element. A variety of security mechanisms, namely cryptography, algorithms which provide access to protected information, and authentication including biometric and steganography, provide security to our information in the Internet. In spite of the above mechanisms, recently artificial intelligence (AI) also contributes towards strengthening information security by providing machine learning and deep learning-based security mechanisms. The artificial intelligence (AI) contribution to cyber security is important as it serves as a provoked reaction and a response to hackers' malicious actions. The purpose of this chapter is to survey recent papers which are contributing to information security by using machine learning and deep learning techniques.

Variable Selection Method for Regression Models Using Computational Intelligence Techniques

Regression model (RM) is an important tool for modeling and analyzing data. It is one of the popular predictive modeling techniques which explore the relationship between a dependent (target) and independent (predictor) variables. The variable selection method is used to form a good and effective regression model. Many variable selection methods existing for regression model such as filter method, wrapper method, embedded methods, forward selection method, Backward Elimination methods, stepwise methods, and so on. In this chapter, computational intelligence-based variable selection method is discussed with respect to the regression model in cybersecurity. Generally, these regression models depend on the set of (predictor) variables. Therefore, variable selection methods are used to select the best subset of predictors from the entire set of variables. Genetic algorithm-based quick-reduct method is proposed to extract optimal predictor subset from the given data to form an optimal regression model.

A Secure Protocol for High-Dimensional Big Data Providing Data Privacy

Due to recent technological development, a huge amount of data generated by social networking, sensor networks, internet, etc., adds more challenges when performing data storage and processing tasks. During PPDP, the collected data may contain sensitive information about the data owner. Directly releasing this for further processing may violate the privacy of the data owner, hence data modification is needed so that it does not disclose any personal information. The existing techniques of data anonymization have a fixed scheme with a small number of dimensions. There are various types of attacks on the privacy of data like linkage attack, homogeneity attack, and background knowledge attack. To provide an effective technique in big data to maintain data privacy and prevent linkage attacks, this paper proposes a privacy preserving protocol, UNION, for a multi-party data provider. Experiments show that this technique provides a better data utility to handle high dimensional data, and scalability with respect to the data size compared with existing anonymization techniques.

A Novel Biometric Image Enhancement Approach With the Hybridization of Undecimated Wavelet Transform and Deep Autoencoder

For a long time, image enhancement techniques have been widely used to improve the image quality in many image processing applications. Recently, deep learning models have been applied to image enhancement problems with great success. In the domain of biometric, fingerprint and face play a vital role to authenticate a person in the right way. Hence, the enhancement of these images significantly improves the recognition rate. In this chapter, undecimated wavelet transform (UDWT) and deep autoencoder are hydridized to enhance the quality of images. Initially, the images are decomposed with Daubechies wavelet filter. Then, deep autoencoder is trained to minimize the error between reconstructed and actual input. The experiments have been conducted on real-time fingerprint and face images collected from 150 subjects, each with 10 orientations. The signal to noise ratio (SNR), peak signal to noise ratio (PSNR), mean square error (MSE), and root mean square error (RMSE) have been computed and compared. It was observed that the proposed model produced a biometric image with high quality.