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Nowadays, the Internet of Things (IoT) concept plays a pivotal role in society and brings new capabilities to different industries. The number of IoT solutions in areas such as transportation and healthcare is increasing and new services are under development. In the last decade, society has experienced a drastic increase in IoT connections. In fact, IoT connections will increase in the next few years across different areas. Conversely, several challenges still need to be faced to enable efficient and secure operations (e.g., interoperability, security, and standards). Furthermore, although efforts have been made to produce datasets composed of attacks against IoT devices, several possible attacks are not considered. Most existing efforts do not consider an extensive network topology with real IoT devices. The main goal of this research is to propose a novel and extensive IoT attack dataset to foster the development of security analytics applications in real IoT operations. To accomplish this, 33 attacks are executed in an IoT topology composed of 105 devices. These attacks are classified into seven categories, namely DDoS, DoS, Recon, Web-based, brute force, spoofing, and Mirai. Finally, all attacks are executed by malicious IoT devices targeting other IoT devices. The dataset is available on the CIC Dataset website. [ABSTRACT FROM AUTHOR]

Malicious online advertisement detection has attracted increasing attention in recent years in both academia and industry. The existing advertising blocking systems are vulnerable to the evolution of new attacks and can cause time latency issues by analyzing web content or querying remote servers. This article proposes a lightweight detection system for advertisement Uniform resource locators (URLs) detection, depending only on lexical‐based features. Deep learning algorithms are used for online advertising classification. After optimizing the deep neural network architecture, our proposed approach can achieve satisfactory results with false negative rate as low as 1.31%. We also design a novel unsupervised method for data clustering. With the implementation of AutoEncoder for feature preprocessing and t‐distributed stochastic neighbor embedding for clustering and visualization, our model outperforms other dimensionality reduction algorithms by generating clear clusterings for different URL families. [ABSTRACT FROM AUTHOR]

Cutting edge Deep Learning (DL) techniques have been widely applied to areas like image processing and speech recognition so far. Likewise, some DL work has been done in the area of cybersecurity. In this survey, we focus on recent DL approaches that have been proposed in the area of cybersecurity, namely intrusion detection, malware detection, phishing/spam detection, and website defacement detection. First, preliminary definitions of popular DL models and algorithms are described. Then, a general DL framework for cybersecurity applications is proposed and explained based on the four major modules it consists of. Afterward, related papers are summarized and analyzed with regard to the focus area, methodology, model applicability, and feature granularity. Finally, concluding remarks and future work are discussed including the possible research topics that can be taken into consideration to enhance various cybersecurity applications using DL models. [ABSTRACT FROM AUTHOR]

Intrusion detection systems are an essential part of any cybersecurity architecture. These systems are critical in defending networks against a variety of security threats. In recent years, deep neural networks have proved their performance and efficiency in various machine learning tasks, including intrusion detection. However, it is shown that deep learning models are highly vulnerable to adversarial attacks. This paper proposes a new approach for performing an adversarial attack against deep learning-based malicious network activity classification. We use the Jacobian Saliency Map to find the best group of features, with different features and perturbation magnitude, to generate adversarial examples. We evaluate our method on three CIC-IDS2017, CIC-IDS2018, and CIC-DDoS2019 datasets. Our experiments show that our proposed method can achieve better performance while using fewer features in adversarial sample generation than other attacks that depend on a higher number of features. Our technique can generate adversarial samples for more than 18% of samples in CIC-IDS2017, 15% of samples in CIC-IDS2018, and 14% of samples in CIC-DDoS2019, using only three features and 0.1 as the perturbation magnitude. We do a deeper analysis of the attack based on its parameters, distance metrics, and the target model performance. Also, an evaluation model with three criteria, including success rates of the best feature sets, average confidence of the adversarial class, and adversarial samples transferability, is used in our analysis. • We propose a new approach for performing an adversarial attack against deep learning-based malicious network activity classification. • We use the Jacobian Saliency Map to find the best group of features, with different features and magnitude of perturbations, to generate adversarial examples. • We evaluate our method on three CIC-IDS2017, CIC-IDS2018, and CIC-DDoS2019 datasets. • Our experiments show that our method can achieve better performance while using fewer features in adversarial sample generation than other attacks that depend on a higher number of features. [ABSTRACT FROM AUTHOR]

Over recent years, the development of online social media has dramatically changed the way people connect and share information. It is undeniable that social platform has promoted the quickest type of spread for fake stories. Almost all the current online fact-checking sources and researches are concentrating on the validating political content and context. The proposed system in this paper provides a complete visual data analytics methods to assist users in achieving a comprehensive understanding of malicious activities at multiple levels such as adversary's behavior, victim's behavior, content, and context level. In this paper, we investigate a variety of datasets from different aspects such as role, vulnerabilities, influential level, and distribution pattern. The proposed method in this paper focuses on automatic fake/hostile activity detection by utilizing a variety of machine learning (ML) techniques, deep learning models, natural language processes (NLP), and social network analysis (SNA) techniques. Different auxiliary models, such as bot detection, user credibility, and text readability, are deployed to generate additional influential features. The classification performance of ten different machine learning algorithms using a variety of well-known datasets is evaluated by utilizing 10-fold cross-validation. [Display omitted] • This research investigates and evaluates a variety of fake news-related datasets. • This study offers hostile activity detection using various ML, NLP, and SNA models. • A detection scheme based on adversary, victim, content, and context is provided. [ABSTRACT FROM AUTHOR]

A network intrusion detection system is an essential part of network security research. It detects intrusion behaviors through active defense technology and takes emergency measures such as alerting and terminating intrusions. To this end, with the rapid development of learning technology, various machine-learning-based and deep-learning-based approaches have been developed, but there is a limitation in the detection accuracy. We believe that dealing with network traffic as if they are vibrations, waves, or sounds would allow us to detect intruders better. In this work, we envisioned a novel Network Intrusion Detection System called "the sound of intrusion". The proposed system transforms the traffic flow features into waves and utilizes advanced audio/speech recognition deep-learning-based techniques to detect intruders. We used several deep-learning-based techniques including long short-term memory, deep belief networks, and convolutional neural networks. The proposed approach has been validated using two well-known and recent benchmark datasets namely NSLKDD and CIC-IDS2017. It achieves the highest detection accuracy, 84.82%, and 99.41%, with the lowest false alarm rate of 0.12% and 0.004% on two common network intrusion detection systems datasets, namely NSL-KDD and CICIDS2017, respectively. It demonstrates improvements over existing approaches, and shows a strong potential for use as a modern Network Intrusion Detection System. [Display omitted] • Deep-learning-based NIDS has been proven to be successful and effective. • Network flow features have been considered as sounds. • Highest detection accuracy with the lowest false alarm have been achieved. [ABSTRACT FROM AUTHOR]