It also provides brief descriptions of each ML method, frequently used security datasets, essential ML tools, and evaluation metrics to evaluate a classification model. 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. There are incentivized malicious adversaries present in the cyberspace that are willing to game and exploit such ML vulnerabilities. However, despite the ongoing success, there are significant challenges in ensuring the trustworthiness of ML systems.

Machine learning (ML) techniques are playing a vital role in numerous applications of cyber security. Conventional security systems lack efficiency in detecting previously unseen and polymorphic security attacks. The previously used security systems are no longer sufficient because cybercriminals are smart enough to evade conventional security systems. Cyber security techniques provide enhancements in security measures to detect and react against cyberattacks. The cyberspace has become more vulnerable to automated and prolonged cyberattacks. Pervasive growth and usage of the Internet and mobile applications have expanded cyberspace. The obtained accuracy was 99.36% and 91% on benchmark and the proposed dataset, respectively. The first dataset is a real and benchmark dataset while the other is a real-like, modern, and more challenging dataset collected from social media and many public available image spam datasets. The results confirm the effectiveness of the proposed schema as it is evaluated over two datasets. Weighted k-nearest neighbor, which is a simple, yet powerful, machine learning algorithm, was used as a classifier. Suitable image processing techniques were used to capture the image features that can differentiate spam images from non-spam ones. In this paper, an image spam detection schema is presented. Image spam detection based on image visual features has the advantage of efficiency in terms of reducing the computational cost and improving the performance. Obfuscated image spamming is one of the new tricks to bypass text-based and Optical Character Recognition (OCR)-based spam filters.

Spams are undesired e-mail messages which are randomly sent in bulk usually for commercial aims. Finally, to highlight the general nature of the generated noise, we show that our attacks are transferable even against supervised algorithms such as SVMs, random forests and neural networks.Į-mail is an efficient and reliable data exchange service. Furthermore, we perform a comparison of our algorithm with a state-of-the-art approach showing that we are able to reach or even outperform its performance. In the experimental part, we demonstrate the sensibility of different single and ensemble clustering algorithms against our crafted adversarial samples on different scenarios. In the absence of any derivative information, we perform the optimization with a custom approach inspired by the Abstract Genetic Algorithm (AGA). We do not assume any information about the internal structure of the victim clustering algorithm, and we allow the attacker to query it as a service only. We formulate the problem as a constrained minimization program, general in its structure and customizable by the attacker according to her capability constraints. In an attempt to fill this gap, in this work, we propose a black-box adversarial attack for crafting adversarial samples to test the robustness of clustering algorithms. To the best of our knowledge, however, only a few works have currently addressed this problem. Due to the widespread use of these applications, a robustness analysis of this family of algorithms against adversarial noise has become imperative. Clustering algorithms play a fundamental role as tools in decision-making and sensible automation processes.

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