The proliferation of IoT devices that can be more easily compromised than desktop computers has led to an increase in IoT-based botnet attacks. To mitigate this threat, there is a need for new methods detect attacks launched from and differentiate between hours- milliseconds-long In article, we propose novel network-based anomaly detection method the called N-BaIoT extracts behavior snapshots network uses deep autoencoders anomalous traffic devices. evaluate our method, infected nine...

Neural networks have become an increasingly popular solution for network intrusion detection systems (NIDS).Their capability of learning complex patterns and behaviors make them a suitable differentiating between normal traffic attacks.However, drawback neural is the amount resources needed to train them.Many gateways routers devices, which could potentially host NIDS, simply do not memory or processing power sometimes even execute such models.More importantly, existing solutions are trained...

In this work we apply machine learning algorithms on network traffic data for accurate identification of IoT devices connected to a network. To train and evaluate the classifier, collected labeled from nine distinct devices, PCs smartphones. Using supervised learning, trained multi-stage meta classifier; in first stage, classifier can distinguish between generated by non-IoT devices. second each device is associated specific class. The overall classification accuracy our model 99.281+.

This research provides a security assessment of the Android framework-Google's software stack for mobile devices. The authors identify high-risk threats to framework and suggest several solutions mitigating them.

This paper presents a study on detecting cyber attacks industrial control systems (ICS) using convolutional neural networks. The was performed Secure Water Treatment testbed (SWaT) dataset, which represents scaled-down version of real-world water treatment plant. We suggest method for anomaly detection based measuring the statistical deviation predicted value from observed value. applied proposed by variety deep network architectures including different variants and recurrent test dataset...

In previous studies classification algorithms were employed successfully for the detection of unknown malicious code. Most these extracted features based on byte n-gram patterns in order to represent inspected files. this study we files using OpCode which are from after disassembly. The used as process. process main goal is detect malware within a set suspected will later be included antivirus software signatures. A rigorous evaluation was performed test collection comprising more than...

Security experts have demonstrated numerous risks imposed by Internet of Things (IoT) devices on organizations. Due to the widespread adoption such devices, their diversity, standardization obstacles, and inherent mobility, organizations require an intelligent mechanism capable automatically detecting suspicious IoT connected networks. In particular, not included in a white list trustworthy device types (allowed be used within organizational premises) should detected. this research, Random...

Industrial control systems (ICSs) are widely used and vital to industry society. Their failure can have severe impact on both the economy human life. Hence, these become an attractive target for physical cyber attacks alike. In this article, we examine attack detection method based simple lightweight neural networks, namely, 1D convolutional networks autoencoders. We apply time frequency domains of data discuss pros cons each representation approach. The suggested is evaluated three popular...

In this paper we apply Machine Learning (ML) techniques on static features that are extracted from Android's application files for the classification of files. Features Java byte-code (i.e.,.dex files) and other file types such as XML-files. Our evaluation focused classifying two Android applications: tools games. Successful differentiation between games is expected to provide positive indication about ability methods learn model benign applications potentially detect malware The results an...

The Internet of Things (IoT) is a global ecosystem information and communication technologies aimed at connecting any type object (thing), time, in place, to each other the Internet. One major problems associated with IoT heterogeneous nature such deployments; this heterogeneity poses many challenges, particularly, areas security privacy. Specifically, testing analysis devices considered very complex task, as different methodologies, including software hardware approaches, are needed. In...

Google's Android framework incorporates an operating system and software stack for mobile devices. Using a general-purpose such as Linux in devices has advantages but also security risks. Security-Enhanced (SELinux) can help reduce potential damage from successful attack.

State-of-the-art deep neural networks (DNNs) are highly effective in solving many complex real-world problems. However, these models vulnerable to adversarial perturbation attacks, and despite the plethora of research this domain, day, adversaries still have upper hand cat mouse game example generation methods vs. detection prevention methods. In research, we present a novel method that uses Shapley Additive Explanations (SHAP) values computed for internal layers DNN classifier discriminate...

In recent years, the emerging Internet-of-Things (IoT) has led to rising concerns about security of networked embedded devices. this work, we propose SIPHON architecture---a Scalable high-Interaction Honeypot platform for IoT Our architecture leverages devices that are physically at one location and connected Internet through so-called \emph{wormholes} distributed around world. The resulting allows exposing few physical over a large number geographically IP addresses. We demonstrate proposed...

As the number of drones increases and era in which they begin to fill skies approaches, an important question needs be answered: From a security privacy perspective, are society really prepared handle challenges that large volume flights will create? In this paper, we investigate age commercial drones. First, focus on research question: Are their ecosystems protected against attacks performed by malicious entities? We list drone's targets, present methodology for reviewing attack...

Physical adversarial attacks against object detectors have seen increasing success in recent years. However, these require direct access to the of interest order apply a physical patch. Furthermore, hide multiple objects, an patch must be applied each object. In this paper, we propose contactless translucent containing carefully constructed pattern, which is placed on camera’s lens, fool state-of-the-art detectors. The primary goal our all instances selected target class. addition,...