Biblio

In recent years, there has been a significant increase in network intrusion attacks which raises a great concern from the privacy and security aspects. Due to the advancement of the technology, cyber-security attacks are becoming very complex such that the current detection systems are not sufficient enough to address this issue. Therefore, an implementation of an intelligent and effective network intrusion detection system would be crucial to solve this problem. In this paper, we use deep learning techniques, namely, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to design an intelligent detection system which is able to detect different network intrusions. Additionally, we evaluate the performance of the proposed solution using different evaluation matrices and we present a comparison between the results of our proposed solution to find the best model for the network intrusion detection system.

An acoustic fingerprint is a condensed and powerful digital signature of an audio signal which is used for audio sample identification. A fingerprint is the pattern of a voice or audio sample. A large number of algorithms have been developed for generating such acoustic fingerprints. These algorithms facilitate systems that perform song searching, song identification, and song duplication detection. In this study, a comprehensive and powerful survey of already developed algorithms is conducted. Four major music fingerprinting algorithms are evaluated for identifying and analyzing the potential hurdles that can affect their results. Since the background and environmental noise reduces the efficiency of music fingerprinting algorithms, behavioral analysis of fingerprinting algorithms is performed using audio samples of different languages and under different environmental conditions. The results of music fingerprint classification are more successful when deep learning techniques for classification are used. The testing of the acoustic feature modeling and music fingerprinting algorithms is performed using the standard dataset of iKala, MusicBrainz and MIR-1K.

In recent years, unmanned aerial vehicles (UAVs) have become increasingly accessible to the public due to their high availability with affordable prices while being equipped with better technology. However, this raises a great concern from both the cyber and physical security perspectives since UAVs can be utilized for malicious activities in order to exploit vulnerabilities by spying on private properties, critical areas or to carry dangerous objects such as explosives which makes them a great threat to the society. Drone identification is considered the first step in a multi-procedural process in securing physical infrastructure against this threat. In this paper, we present drone detection and identification methods using deep learning techniques such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Convolutional Recurrent Neural Network (CRNN). These algorithms will be utilized to exploit the unique acoustic fingerprints of the flying drones in order to detect and identify them. We propose a comparison between the performance of different neural networks based on our dataset which features audio recorded samples of drone activities. The major contribution of our work is to validate the usage of these methodologies of drone detection and identification in real life scenarios and to provide a robust comparison of the performance between different deep neural network algorithms for this application. In addition, we are releasing the dataset of drone audio clips for the research community for further analysis.

Deep learning techniques have been widely used in intrusion detection, but their application on convolutional neural networks (CNN) is still immature. The main challenge is how to represent the network traffic to improve performance of the CNN model. In this paper, we propose a network intrusion detection algorithm based on representation learning using Fast Fourier Transformation (FFT), which is first exploration that converts traffic to image by FFT to the best of our knowledge. Each traffic is converted to an image and then the intrusion detection problem is turned to image classification. The experiment results on NSL-KDD dataset show that the classification performence of the algorithm in the CNN model has obvious advantages compared with other algorithms.

In this paper we present techniques based on machine learning techniques on monitoring data for analysis of cybersecurity threats in cloud environments that incorporate enterprise applications from the fields of telecommunications and IoT. Cybersecurity is a term describing techniques for protecting computers, telecommunications equipment, applications, environments and data. In modern networks enormous volume of generated traffic can be observed. We propose several techniques such as Support Vector Machines, Neural networks and Deep Neural Networks in combination for analysis of monitoring data. An approach for combining classifier results based on performance weights is proposed. The proposed approach delivers promising results comparable to existing algorithms and is suitable for enterprise grade security applications.

In recent years, there is a surge of interest in approaches pertaining to security issues of Internet of Things deployments and applications that leverage machine learning and deep learning techniques. A key prerequisite for enabling such approaches is the development of scalable infrastructures for collecting and processing security-related datasets from IoT systems and devices. This paper introduces such a scalable and configurable data collection infrastructure for data-driven IoT security. It emphasizes the collection of (security) data from different elements of IoT systems, including individual devices and smart objects, edge nodes, IoT platforms, and entire clouds. The scalability of the introduced infrastructure stems from the integration of state of the art technologies for large scale data collection, streaming and storage, while its configurability relies on an extensible approach to modelling security data from a variety of IoT systems and devices. The approach enables the instantiation and deployment of security data collection systems over complex IoT deployments, which is a foundation for applying effective security analytics algorithms towards identifying threats, vulnerabilities and related attack patterns.

In recent years, deep convolution neural networks (DCNNs) have won many contests in machine learning, object detection, and pattern recognition. Furthermore, deep learning techniques achieved exceptional performance in image classification, reaching accuracy levels beyond human capability. Malware variants from similar categories often contain similarities due to code reuse. Converting malware samples into images can cause these patterns to manifest as image features, which can be exploited for DCNN classification. Techniques for converting malware binaries into images for visualization and classification have been reported in the literature, and while these methods do reach a high level of classification accuracy on training datasets, they tend to be vulnerable to overfitting and perform poorly on previously unseen samples. In this paper, we explore and document a variety of techniques for representing malware binaries as images with the goal of discovering a format best suited for deep learning. We implement a database for malware binaries from several families, stored in hexadecimal format. These malware samples are converted into images using various approaches and are used to train a neural network to recognize visual patterns in the input and classify malware based on the feature vectors. Each image type is assessed using a variety of learning models, such as transfer learning with existing DCNN architectures and feature extraction for support vector machine classifier training. Each technique is evaluated in terms of classification accuracy, result consistency, and time per trial. Our preliminary results indicate that improved image representation has the potential to enable more effective classification of new malware.

Deep learning techniques have demonstrated the ability to perform a variety of object recognition tasks using visible imager data; however, deep learning has not been implemented as a means to autonomously detect and assess targets of interest in a physical security system. We demonstrate the use of transfer learning on a convolutional neural network (CNN) to significantly reduce training time while keeping detection accuracy of physical security relevant targets high. Unlike many detection algorithms employed by video analytics within physical security systems, this method does not rely on temporal data to construct a background scene; targets of interest can halt motion indefinitely and still be detected by the implemented CNN. A key advantage of using deep learning is the ability for a network to improve over time. Periodic retraining can lead to better detection and higher confidence rates. We investigate training data size versus CNN test accuracy using physical security video data. Due to the large number of visible imagers, significant volume of data collected daily, and currently deployed human in the loop ground truth data, physical security systems present a unique environment that is well suited for analysis via CNNs. This could lead to the creation of algorithmic element that reduces human burden and decreases human analyzed nuisance alarms.