Network-based Intrusion Detection System (NIDS) forms the frontline defence against network attacks that compromise the security of the data, systems, and networks. In recent years, Deep Neural ...Networks (DNNs) have been increasingly used in NIDS to detect malicious traffic due to their high detection accuracy. However, DNNs are vulnerable to adversarial attacks that modify an input example with imperceivable perturbation, which causes a misclassification by the DNN. In security-sensitive domains, such as NIDS, adversarial attacks pose a severe threat to network security. However, existing studies in adversarial learning against NIDS directly implement adversarial attacks designed for Computer Vision (CV) tasks, ignoring the fundamental differences in the detection pipeline and feature spaces between CV and NIDS. It remains a major research challenge to launch and detect adversarial attacks against NIDS. This article surveys the recent literature on NIDS, adversarial attacks, and network defences since 2015 to examine the differences in adversarial learning against deep neural networks in CV and NIDS. It provides the reader with a thorough understanding of DL-based NIDS, adversarial attacks and defences, and research trends in this field. We first present a taxonomy of DL-based NIDS and discuss the impact of taxonomy on adversarial learning. Next, we review existing white-box and black-box adversarial attacks on DNNs and their applicability in the NIDS domain. Finally, we review existing defence mechanisms against adversarial examples and their characteristics.
Smart Meter Data Privacy: A Survey Asghar, Muhammad Rizwan; Dán, György; Miorandi, Daniele ...
IEEE Communications surveys and tutorials,
01/2017, Letnik:
19, Številka:
4
Journal Article
Recenzirano
Automated and smart meters are devices that are able to monitor the energy consumption of electricity consumers in near real-time. They are considered key technological enablers of the smart grid, as ...the real-time consumption data that they can collect could enable new sophisticated billing schemes, could facilitate more efficient power distribution system operation and could give rise to a variety of value-added services. At the same time, the energy consumption data that the meters collect are sensitive consumer information; thus, privacy is a key concern and is a major inhibitor of real-time data collection in practice. In this paper, we review the different uses of metering data in the smart grid and the related privacy legislation. We then provide a structured overview, shortcomings, recommendations, and research directions of security solutions that are needed for privacy-preserving meter data delivery and management. We finally survey recent work on privacy-preserving technologies for meter data collection for the three application areas: 1) billing; 2) operations; and 3) value-added services including demand response.
Much attention has been paid to the recognition of human emotions with the help of electroencephalogram (EEG) signals based on machine learning technology. Recognizing emotions is a challenging task ...due to the non-linear property of the EEG signal. This paper presents an advanced signal processing method using the deep neural network (DNN) for emotion recognition based on EEG signals. The spectral and temporal components of the raw EEG signal are first retained in the 2D Spectrogram before the extraction of features. The pre-trained AlexNet model is used to extract the raw features from the 2D Spectrogram for each channel. To reduce the feature dimensionality, spatial, and temporal based, bag of deep features (BoDF) model is proposed. A series of vocabularies consisting of 10 cluster centers of each class is calculated using the k-means cluster algorithm. Lastly, the emotion of each subject is represented using the histogram of the vocabulary set collected from the raw-feature of a single channel. Features extracted from the proposed BoDF model have considerably smaller dimensions. The proposed model achieves better classification accuracy compared to the recently reported work when validated on SJTU SEED and DEAP data sets. For optimal classification performance, we use a support vector machine (SVM) and k-nearest neighbor (k-NN) to classify the extracted features for the different emotional states of the two data sets. The BoDF model achieves 93.8% accuracy in the SEED data set and 77.4% accuracy in the DEAP data set, which is more accurate compared to other state-of-the-art methods of human emotion recognition.
A Content Delivery Network (CDN) is a distributed system composed of a large number of nodes that allows users to request objects from nearby nodes. CDN not only reduces end-to-end latency on the ...user side but also offloads Content Providers (CPs), providing resilience against Distributed Denial of Service (DDoS) attacks. However, by caching objects and processing user requests, CDN providers could infer user preferences and the popularity of objects, thus resulting in information leakage. Unfortunately, such information leakage may result in loss of user privacy and reveal business-specific information to untrusted or compromised CDN providers. State-of-the-art solutions can protect the content of sensitive objects but cannot prevent CDN providers from inferring user preferences and the popularity of objects. In this work, we present a privacy-preserving encrypted CDN system to hide not only the content of objects and user requests, but also protect user preferences and the popularity of objects from curious CDN providers. We employ encryption to protect the objects and user requests in a way that both the CDNs and CPs can perform the search operations without accessing objects and requests in cleartext. Our proposed system is based on a scalable key management approach for multi-user access, where no key regeneration and data re-encryption are needed for user revocation. We have implemented a prototype of the system and show its practical efficiency.
HTTPS refers to an application-specific implementation that runs HyperText Transfer Protocol (HTTP) on top of Secure Socket Layer (SSL) or Transport Layer Security (TLS). HTTPS is used to provide ...encrypted communication and secure identification of web servers and clients, for different purposes such as online banking and e-commerce. However, many HTTPS vulnerabilities have been disclosed in recent years. Although many studies have pointed out that these vulnerabilities can lead to serious consequences, domain administrators seem to ignore them. In this study, we evaluate the HTTPS security level of Alexa’s top 1 million domains from two perspectives. First, we explore which popular sites are still affected by those well-known security issues. Our results show that less than 0.1% of HTTPS-enabled servers in the measured domains are still vulnerable to known attacks including Rivest Cipher 4 (RC4), Compression Ratio Info-Leak Mass Exploitation (CRIME), Padding Oracle On Downgraded Legacy Encryption (POODLE), Factoring RSA Export Keys (FREAK), Logjam, and Decrypting Rivest–Shamir–Adleman (RSA) using Obsolete and Weakened eNcryption (DROWN). Second, we assess the security level of the digital certificates used by each measured HTTPS domain. Our results highlight that less than 0.52% domains use the expired certificate, 0.42% HTTPS certificates contain different hostnames, and 2.59% HTTPS domains use a self-signed certificate. The domains we investigate in our study cover 5 regions (including ARIN, RIPE NCC, APNIC, LACNIC, and AFRINIC) and 61 different categories such as online shopping websites, banking websites, educational websites, and government websites. Although our results show that the problem still exists, we find that changes have been taking place when HTTPS vulnerabilities were discovered. Through this three-year study, we found that more attention has been paid to the use and configuration of HTTPS. For example, more and more domains begin to enable the HTTPS protocol to ensure a secure communication channel between users and websites. From the first measurement, we observed that many domains are still using TLS 1.0 and 1.1, SSL 2.0, and SSL 3.0 protocols to support user clients that use outdated systems. As the previous studies revealed security risks of using these protocols, in the subsequent studies, we found that the majority of domains updated their TLS protocol on time. Our 2020 results suggest that most HTTPS domains use the TLS 1.2 protocol and show that some HTTPS domains are still vulnerable to the existing known attacks. As academics and industry professionals continue to disclose attacks against HTTPS and recommend the secure configuration of HTTPS, we found that the number of vulnerable domain is gradually decreasing every year.
Classification of human emotions based on electroencephalography (EEG) is a very popular topic nowadays in the provision of human health care and well-being. Fast and effective emotion recognition ...can play an important role in understanding a patient’s emotions and in monitoring stress levels in real-time. Due to the noisy and non-linear nature of the EEG signal, it is still difficult to understand emotions and can generate large feature vectors. In this article, we have proposed an efficient spatial feature extraction and feature selection method with a short processing time. The raw EEG signal is first divided into a smaller set of eigenmode functions called (IMF) using the empirical model-based decomposition proposed in our work, known as intensive multivariate empirical mode decomposition (iMEMD). The Spatio-temporal analysis is performed with Complex Continuous Wavelet Transform (CCWT) to collect all the information in the time and frequency domains. The multiple model extraction method uses three deep neural networks (DNNs) to extract features and dissect them together to have a combined feature vector. To overcome the computational curse, we propose a method of differential entropy and mutual information, which further reduces feature size by selecting high-quality features and pooling the k-means results to produce less dimensional qualitative feature vectors. The system seems complex, but once the network is trained with this model, real-time application testing and validation with good classification performance is fast. The proposed method for selecting attributes for benchmarking is validated with two publicly available data sets, SEED, and DEAP. This method is less expensive to calculate than more modern sentiment recognition methods, provides real-time sentiment analysis, and offers good classification accuracy.
Emotional awareness perception is a largely growing field that allows for more natural interactions between people and machines. Electroencephalography (EEG) has emerged as a convenient way to ...measure and track a user’s emotional state. The non-linear characteristic of the EEG signal produces a high-dimensional feature vector resulting in high computational cost. In this paper, characteristics of multiple neural networks are combined using Deep Feature Clustering (DFC) to select high-quality attributes as opposed to traditional feature selection methods. The DFC method shortens the training time on the network by omitting unusable attributes. First, Empirical Mode Decomposition (EMD) is applied as a series of frequencies to decompose the raw EEG signal. The spatiotemporal component of the decomposed EEG signal is expressed as a two-dimensional spectrogram before the feature extraction process using Analytic Wavelet Transform (AWT). Four pre-trained Deep Neural Networks (DNN) are used to extract deep features. Dimensional reduction and feature selection are achieved utilising the differential entropy-based EEG channel selection and the DFC technique, which calculates a range of vocabularies using k-means clustering. The histogram characteristic is then determined from a series of visual vocabulary items. The classification performance of the SEED, DEAP and MAHNOB datasets combined with the capabilities of DFC show that the proposed method improves the performance of emotion recognition in short processing time and is more competitive than the latest emotion recognition methods.
Over the years, software applications have captured a big market ranging from smart devices (smartphones, smart wearable devices) to enterprise resource management including Enterprise Resource ...Planning, office applications, and the entertainment industry (video games and graphics design applications). Protecting the copyright of software applications and protection from malicious software (malware) have been topics of utmost interest for academia and industry for many years. The standard solutions use the software license key or rely on the Operating System (OS) protection mechanisms, such as Google Play Protect. However, some end users have broken these protections to bypass payments for applications that are not free. They have done so by downloading the software from an unauthorised website or by jailbreaking the OS protection mechanisms. As a result, they cannot determine whether the software they download is malicious or not. Further, if the software is uploaded to a third party platform by malicious users, the software developer has no way of knowing about it. In such cases, the authenticity or integrity of the software cannot be guaranteed. There is also a problem of information transparency among software platforms. In this study, we propose an architecture that is based on blockchain technology for providing data transparency, release traceability, and auditability. Our goal is to provide an open framework to allow users, software vendors, and security practitioners to monitor misbehaviour and assess software vulnerabilities for preventing malicious software downloads. Specifically, the proposed solution makes it possible to identify software developers who have gone rogue and are potentially developing malicious software. Furthermore, we introduce an incentive policy for encouraging security engineers, victims and software owners to participate in collaborative works. The outcomes will ensure the wide adoption of a software auditing ecosystem in software markets, specifically for some mobile device manufacturers that have been banned from using the open-source OS such as Android. Consequently, there is a demand for them to verify the application security without completely relying on the OS-specific security mechanisms.
The Publish and Subscribe (pub/sub) system is an established paradigm to disseminate the data from publishers to subscribers in a loosely coupled manner using a network of dedicated brokers. However, ...sensitive data could be exposed to malicious entities if brokers get compromised or hacked; or even worse, if brokers themselves are curious to learn about the data. A viable mechanism to protect sensitive publications and subscriptions is to encrypt the data before it is disseminated through the brokers. State-of-the-art approaches allow brokers to perform encrypted matching without revealing publications and subscriptions. However, if malicious brokers collude with malicious subscribers or publishers, they can learn the interests of innocent subscribers, even when the interests are encrypted. In this article, we present a pub/sub system that ensures confidentiality of publications and subscriptions in the presence of untrusted brokers. Furthermore, our solution resists collusion attacks between untrusted brokers and malicious subscribers (or publishers). Finally, we have implemented a prototype of our solution to show its feasibility and efficiency.
Wireless body area networks (WBANs) play a vital role in shaping today's healthcare systems. Given the critical nature of a WBAN in one's health to automatically monitor and diagnose health issues, ...security and privacy of these healthcare systems need a special attention. In this paper, we first propose a novel four-tier architecture of remote health monitoring system and then identify the security requirements and challenges at each tier. We provide a concise survey of the literature aimed at improving the security and privacy of WBANs and then present a comprehensive overview of the problem. In particular, we stress that the inclusion of in vivo nano-networks in a remote healthcare monitoring system is imperative for its completeness. To this end, we elaborate on security threats and concerns in nano-networks and medical implants as well as we emphasize on presenting a holistic framework of an overall ecosystem for WBANs, which is essential to ensure end-to-end security. Lastly, we discuss some limitations of current WBANs.
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