Cybersecurity

The internet has revolutionized the way people communicate, how they manage their business, and even how they conduct their studies. Organizations can conduct meetings virtually and store all their data online. With this convenience, however, comes the risk of cybercrime (CC). Some of the world's most renowned organizations have found themselves having to incur huge recovery costs after falling prey to CC. Higher learning institutions' databases are increasingly falling victim to CCs, owing to the vast amounts of personal and research data they harbor. Despite this, the area of CCs in learning institutions remains understudied. This chapter seeks to identify how CC is manifested in such institutions and the specific cybersecurity measures that stakeholders could use to minimize their exposure to the same. The qualitative case study was designed to explore the research questions, and collected data through semistructured interviews. The findings showed hacking, phishing, and spoofing as the most common manifestations of cybercrime in higher learning institutions.

A Review of Machine Learning Techniques for Anomaly Detection in Static Graphs

Detection of anomalies in a given data set is a vital step in several applications in cybersecurity; including intrusion detection, fraud, and social network analysis. Many of these techniques detect anomalies by examining graph-based data. Analyzing graphs makes it possible to capture relationships, communities, as well as anomalies. The advantage of using graphs is that many real-life situations can be easily modeled by a graph that captures their structure and inter-dependencies. Although anomaly detection in graphs dates back to the 1990s, recent advances in research utilized machine learning methods for anomaly detection over graphs. This chapter will concentrate on static graphs (both labeled and unlabeled), and the chapter summarizes some of these recent studies in machine learning for anomaly detection in graphs. This includes methods such as support vector machines, neural networks, generative neural networks, and deep learning methods. The chapter will reflect the success and challenges of using these methods in the context of graph-based anomaly detection.

Building New Relationships

Given the current widespread popularity of social media, such as Twitter, Instagram, Snapchat, and many other applications, understanding users' attitudes and usage behavior of social media applications becomes a necessity in order to develop future placements of such technologies and increase the level of trust among the users. Therefore, the aim of this chapter is to shed light on the impact of trustworthiness of social media on the intention to use it. Data is gathered through a quantitative method, in which a questionnaire is used as a primary data. A convenient sampling is applied, in which the most easily accessible managers and employees in Ministry of Interior in Bahrain are chosen. The results demonstrate that there is a significant positive relationship between trustworthiness and intention to use social media. The study recommends future works to study the impact of security awareness on the usage of social media in public sector in Bahrain.

Critical Cybersecurity Threats

One common reason for cybercrime is the goal of damaging a business by hacking or destroying important information. Another such reason is the criminal's goal of gaining financially from the hack. This chapter analyzes Bahraini organizations' vulnerability to digital security threats. It has used qualitative research to analyze industry performance. Moreover, with the support of secondary research, it has also explored cybersecurity threats faced by such organizations. The discussion based on secondary data analysis has explored two major aspects of Bahraini organizations and the cybersecurity threats they face. Firstly, the data and finances of both sectors are at huge risk in Bahraini organizations. Secondly, one important aspect of exploration has been to identify the most frequently encountered forms of cybercrime. Its analysis reveals that the kind of cybersecurity threat that a business is most likely to face is cyberwarfare. This may affect two rival businesses while they are competing with each other. Competitors' data may be destroyed or hacked—leading to long-term losses.

Characteristic Analysis of Side Channel Attacks and Various Power Analysis Attack Techniques

Embedded systems have a plethora of security solutions and encryption protocols that can protect them against a multitude of attacks. Hardware engineers infuse lot of time and effort in implementing cryptographic algorithms, keeping the analysis of design constraints into rumination. When it comes to designs in potential hostile environment, engineers face a challenge for building resistance-free embedded systems against attacks called side channel attacks. Therefore, there is a strong need to address issues related to side channel attacks. This chapter will provide an insight into the field of hardware security, and will provide a deep investigation of various types of side channel attacks and better understanding of various power analysis tools, which will further give researchers a vision to build efficient and secure systems in order to thwart attacks. This chapter mainly focuses on passive attacks as compared to active attacks since passive attacks are easy to perform and lot of research is going on these attacks.

Improving Spam Email Filtering Systems Using Data Mining Techniques

Email is the most popular choice of communication due to its low-cost and easy accessibility, which makes email spam a major issue. Emails can be incorrectly marked by a spam filter and legitimate emails can get lost in the spam folder or the spam emails can deluge the users' inboxes. Therefore, various methods based on statistics and machine learning have been developed to classify emails accurately. In this chapter, the existing spam filtering methods were studied comprehensively, and a spam email classifier based on the genetic algorithm was proposed. The proposed algorithm was successful in achieving high accuracy by reducing the rate of false positives, but at the same time, it also maintained an acceptable rate of false negatives. The proposed algorithm was tested on 2000 emails from the two popular spam datasets, Enron and LingSpam, and the accuracy was found to be nearly 90%. The results showed that the genetic algorithm is an effective method for spam classification and with further enhancements that will provide a more robust spam filter.

A Hybrid Connectionist/ Substitution Approach for Data Encryption

The authors believe that the hybridization of two different approaches results in more complex encryption outcomes. The proposed method combines a symbolic approach, which is a table substitution method, with another paradigm that models real-life neurons (connectionist approach). This hybrid model is compact, nonlinear, and parallel. The neural network approach focuses on generating keys (weights) based on a feedforward neural network architecture that works as a mirror. The weights are used as an input for the substitution method. The hybrid model is verified and validated as a successful encryption method.

Cyber Security, IT Governance, and Performance

Cybersecurity is an emerging field with a growing body of literature and publications. It is fundamentally based in computer science and computer engineering but has recently gained popularity in business management. Despite the explosion of cybersecurity, there is a scarcity of literature on the definition of the term ‘Cybers Security' and how it is situated within different contexts. Henceforth, this chapter presents a review of the work related to cybersecurity, within different contexts, mainly IT governance and firm performance context. The work reviewed is separated into four main categories: the importance of cybersecurity and how it is measured, corporate governance and IT governance, IT governance mechanisms, and financial performance measures.

Composite Discrete Logarithm Problem and a Reconstituted ElGamal Cryptosystem Based on the Problem

In this chapter, the authors have defined a new ElGamal cryptosystem by using the power Fibonacci sequence module m. Then they have defined a new sequence module m and the other ElGamal cryptosystem by using the new sequence. In addition, they have compared that the new ElGamal cryptosystems and ElGamal cryptosystem in terms of cryptography. Then the authors have defined the third ElGamal cryptosystem. They have, particularly, called the new system as composite ElGamal cryptosystem. The authors made an application of composite ElGamal cryptosystem. Finally, the authors have compared that composite ElGamal cryptosystem and ElGamal cryptosystem in terms of cryptography and they have obtained that composite ElGamal cryptosystem is more advantageous than ElGamal cryptosystem.

Bahrain Government Information Security Framework

Information technology is perceived as an important enabler for government entities to accomplish their goals. The proliferation of electronic government services that can provide value for citizens and residents have pushed governments all over the world to adopt and deploy these services. However, governments have realized that it is critical to build proper defense to protect the information. Implementing information security by using international or national information security frameworks helps organizations to ensure the safeguard of information assets. This chapter reviews useful information security frameworks. Also, this chapter provides a proposed information security framework implemented in the Government of Bahrain, which is called CyberTrust Program. This framework was developed based on best practices and local resources and culture.