Pervasive growth and usage of the Internet and mobile applications have expanded cyberspace. The cyberspace has become more vulnerable to automated and prolonged cyberattacks. Cyber security techniques provide enhancements in security measures to detect and react against cyberattacks. The previously used security systems are no longer sufficient because cybercriminals are smart enough to evade conventional security systems. Conventional security systems lack efficiency in detecting previously unseen and polymorphic security attacks. Machine learning (ML) techniques are playing a vital role in numerous applications of cyber security. However, despite the ongoing success, there are significant challenges in ensuring the trustworthiness of ML systems. There are incentivized malicious adversaries present in the cyberspace that are willing to game and exploit such ML vulnerabilities. This paper aims to provide a comprehensive overview of the challenges that ML techniques face in protecting cyberspace against attacks, by presenting a literature on ML techniques for cyber security including intrusion detection, spam detection, and malware detection on computer networks and mobile networks in the last decade. It also provides brief descriptions of each ML method, frequently used security datasets, essential ML tools, and evaluation metrics to evaluate a classification model. It finally discusses the challenges of using ML techniques in cyber security. This paper provides the latest extensive bibliography and the current trends of ML in cyber security.

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A Survey on Machine Learning Techniques for Cyber Security in the Last Decade

There are a growing number of people who hold accounts on social media platforms (SMPs) but hide their identity for malicious purposes Unfortunately, very little research has been done to date to detect fake identities created by humans, especially so on SMPs In contrast, many examples exist of cases where fake accounts created by bots or computers have been detected successfully using machine learning models In the case of bots these machine learning models were dependent on employing engineered features, such as the “friend-to-followers ratio” These features were engineered from attributes, such as “friend-count” and “follower-count,” which are directly available in the account profiles on SMPs The research discussed in this paper applies these same engineered features to a set of fake human accounts in the hope of advancing the successful detection of fake identities created by humans on SMPs

Using Machine Learning to Detect Fake Identities: Bots vs Humans

Mobile phishing attacks and defence mechanisms: State of art and open research challenges

Susceptibility to phishing on social network sites: A personality information processing model.

Phishing is an identity theft, which deceives Internet users into revealing their sensitive data, e.g., login information, credit/debit card details, and so on. Researchers have developed various a...

A survey of phishing attack techniques, defence mechanisms and open research challenges

The popularity of mobile devices is increasing day by day as they provide a large variety of services by reducing the cost of services. Short Message Service (SMS) is considered one of the widely used communication service. However, this has led to an increase in mobile devices attacks like SMS Spam. In this paper, we present a novel approach that can detect and filter the spam messages using machine learning classification algorithms. We study the characteristics of spam messages in depth and then found ten features, which can efficiently filter SMS spam messages from ham messages. Our proposed approach achieved 96.5% true positive rate and 1.02% false positive rate for Random Forest classification algorithm.

Towards Filtering of SMS Spam Messages Using Machine Learning Based Technique

Smishing attack is generally performed by sending a fake short message service (SMS) that contains a link of the malicious webpage or application. Smishing messages are the subclass of spam SMS and...

A Novel Approach to Detect Spam and Smishing SMS using Machine Learning Techniques

Mobile phones have taken a crucial part in today’s transferable computer world. Mobile devices are more popular these days because of their small screen size, lower production cost, and portability. Because of their popularity, these devices seem to be a perfect target of harmful malicious attacks like mobile phishing. In this attack, the attackers usually send the fake link via emails, SMS message, messenger, WhatsApp, etc. and ask for some credential data. Mobile phishing is fooling the users to get the sensitive personal information. This paper presents a comprehensive analysis of mobile phishing attacks, their exploitation, some of the recent solutions for phishing detection. Our survey provides a good understanding of the mobile phishing problem and currently available solutions with the future scope to deal with mobile phishing attacks conveniently.

Comparative Analysis of Mobile Phishing Detection and Prevention Approaches

This research explored the factors affecting awareness regarding graduate career prospects for distance learners of Economics, studying at the Directorate of Distance Education, University of Jammu, India. Distance learners completed a questionnaire about students' career intentions and motivation for undertaking the subject. Exploratory Factor Analysis (EFA) and Cluster Analysis were used to cluster these students into groups "based upon their perceptions and awareness" of graduate careers. The distance learners were aware of the career opportunities available to MA Economics graduates. Their awareness emerged as the most significant factor. However, they also show 'Perceived Poor job prospects'. Two clusters emerged: 'highly motivated learners' and 'moderately motivated learners'. The item 'Those studying for academics and competitive exams simultaneously don't achieve anything in life' emerged as the most influential in forming the clusters. The findings highlight an urgent need for early counselling for these students. Their competitive inclination can be seen from their response to the question 'Why distance mode was chosen', as 47% responded that it was due to their preparing for the competitive exams simultaneously. Their job preferences for the banking sector (26.3%), education sector (22.5%) or acceptance of any job (16.5%) show that their pursuit of a Master's degree in Economics was not an influencer in their career orientation. The learners have not realized their potential yet, due to which they are willing to take up any job, thus underutilizing their special skills learned after completing the Master's degree in Economics.

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Segmenting Distance Learners on the Basis of their Career Awareness and Perceived Employability: A Study of PG Economics Students of University of Jammu

Neelam Choudhary

Papers

Towards Filtering of SMS Spam Messages Using Machine Learning Based Technique

A Novel Approach to Detect Spam and Smishing SMS using Machine Learning Techniques

Comparative Analysis of Mobile Phishing Detection and Prevention Approaches

Segmenting Distance Learners on the Basis of their Career Awareness and Perceived Employability: A Study of PG Economics Students of University of Jammu