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

JOWMDroid: Android malware detection based on feature weighting with joint optimization of weight-mapping and classifier parameters

With the increasing use of mobile devices, malware attacks are rising, especially on Android phones, which account for 72.2% of the total market share. Hackers try to attack smartphones with various methods such as credential theft, surveillance, and malicious advertising. Among numerous countermeasures, machine learning (ML)-based methods have proven to be an effective means of detecting these attacks, as they are able to derive a classifier from a set of training examples, thus eliminating the need for an explicit definition of the signatures when developing malware detectors. This paper provides a systematic review of ML-based Android malware detection techniques. It critically evaluates 106 carefully selected articles and highlights their strengths and weaknesses as well as potential improvements. Finally, the ML-based methods for detecting source code vulnerabilities are discussed, because it might be more difficult to add security after the app is deployed. Therefore, this paper aims to enable researchers to acquire in-depth knowledge in the field and to identify potential future research and development directions.

https://www.mdpi.com/2079-9292/10/13/1606/pdf

Android Mobile Malware Detection Using Machine Learning: A Systematic Review

Smart phones are becoming essential in our lives, and Android is one of the most popular operating systems. Android OS is wide-ranging in the mobile industry today because of its opensource architecture. It is a wide variety of applications and basic features. App users tend to trust Android OS to secure data, but it has been shown that Android is more vulnerable and unstable. Identification of Android OS malware has become an emerging research subject of concern. This paper aims to analyze the various characteristics involved in malware detection. It also addresses malware detection methods. The current detection mechanism utilizes algorithms such as Bayesian algorithm, Ada grad algorithm, Naïve Bayes algorithm, Hybrid algorithm, and other algorithms for machine learning to train the sets and find the malware. Review Article Omer et al.; AJRCOS, 7(4): 59-69, 2021; Article no.AJRCOS.68044 60

/pdf/efficiency-of-malware-detection-in-android-system-a-survey-36xdn9jtj1.pdf

Efficiency of Malware Detection in Android System: A Survey

Malicious application detection in android — A systematic literature review

With the developments in mobile and wireless technology, mobile devices have become an important part of our lives. While Android is the leading operating system in market share, it is the platform most targeted by attackers. Although many solutions have been proposed in the literature for the detection of Android malware, there is still a need for attribute selection methods to be used in Android malware detection systems. In this study, a machine learning-based malware detection system is proposed to distinguish Android malware from benign applications. At the feature selection stage of the proposed malware detection system, it is aimed to remove unnecessary features by using a linear regression-based feature selection approach. In this way, the dimension of the feature vector is reduced, the training time is decreased, and the classification model can be used in real-time malware detection systems. When the results of the study are examined, the highest 0.961 is obtained according to the F-measure metric by using at least 27 features.

A novel permission-based Android malware detection system using feature selection based on linear regression

Mobile devices' hardware have been enhancing day by day. With this development, mobile phones are supporting many programs and everyone takes advantage of them. Nevertheless, malware applications are increasing more and more so that people can come across lots of problems. Android is a mobile operating system that is the most used on the smart mobile phones. Because it is the most used and open source, it has been the target of attackers. Android security related to the permissions allowed by users to the applications. There have been many studies on the permission based Android malware detection. In this study, permission based Android malware system is analyzed. Unlike other studies, we propose permission weight approach. Each of permissions is given a different score by means of this approach. Then, K-nearest Neighbor (KNN) and Naive Bayes (NB) algorithms are applied and the proposed method is compared with the previous studies. According to the experimental results, the proposed approach has better results than the other ones.

New results on permission based static analysis for Android malware

Android is the most preferred mobile operating system in the world. Applications are available from both official application repositories and other application stores. For these reasons, there has been a remarkable increase in malware for the Android operating system in recent years. In this study, a novel Android malware detection system is proposed by using filter-based feature selection methods. The proposed approach is static Android malware detection based on machine learning. Permissions extracted from application files are used as features in the developed system. Dimension reduction is carried out with eight different feature selection methods to enhance the running time and efficiency of machine learning algorithms. While four of these methods are used in Android malware detection systems, the remaining four methods are adapted from text classification studies. The adapted methods are compared in terms of both extracted features and classification results. When the results are examined, it is shown that the adapted methods improve the efficiency of the classification algorithms and can be used in this field.

A novel Android malware detection system: adaption of filter-based feature selection methods

With the widespread use of the internet, the size of the text data increases day by day. Poems can be given as an example of the growing text. In this study, we aim to classify poetry according to poet. Firstly, data set consisting of three different poetry of poets written in English have been constructed. Then, text categorization techniques are implemented on it. Chi-Square technique are used for feature selection. In addition, five different classification algorithms are tried. These algorithms are Sequential minimal optimization, Naive Bayes, C4.5 decision tree, Random Forest and k-nearest neighbors. Although each classifier showed very different results, over the 70% classification success rate was taken by sequential minimal optimization technique.

A Text Classification Application: Poet Detection from Poetry.

Short message services are one of the most widely used communication services. The increased use of mobile devices and the lowering of SMS costs by operators enable short message services to remain popular. However, this popularity causes tens of users to be exposed to spam SMS every day. The term spam can simply be referred to as unwanted messages by users. Although organizations take measures against spam SMS and there are widely used spam SMS filtering systems, the problem of spam SMS is becoming widespread. There are many studies in the literature for the detection of spam SMS, but new and efficient methods are still needed. In this study, TF-IDF and RF term weighting methods which are frequently used in text mining applications were compared in order to classify spam SMS and to use the limited content of SMSs more meaningfully. The vectors obtained from the data set were weighted by TF-IDF and RF term weighting methods and classified with 5 different classifiers popular in this field.

Oguz Emre Kural

Papers

A novel permission-based Android malware detection system using feature selection based on linear regression

New results on permission based static analysis for Android malware

A novel Android malware detection system: adaption of filter-based feature selection methods

A Text Classification Application: Poet Detection from Poetry.

Comparison of Term Weighting Techniques in Spam SMS Detection