Biblio

In this paper, we focus on developing a novel mechanism to preserve differential privacy in deep neural networks, such that: (1) The privacy budget consumption is totally independent of the number of training steps; (2) It has the ability to adaptively inject noise into features based on the contribution of each to the output; and (3) It could be applied in a variety of different deep neural networks. To achieve this, we figure out a way to perturb affine transformations of neurons, and loss functions used in deep neural networks. In addition, our mechanism intentionally adds "more noise" into features which are "less relevant" to the model output, and vice-versa. Our theoretical analysis further derives the sensitivities and error bounds of our mechanism. Rigorous experiments conducted on MNIST and CIFAR-10 datasets show that our mechanism is highly effective and outperforms existing solutions.

Open Science Big Data is emerging as an important area of research and software development. Although there are several high quality frameworks for Big Data, additional capabilities are needed for Open Science Big Data. These include data provenance, citable reusable data, data sources providing links to research literature, relationships to other data and theories, transparent analysis/reproducibility, data privacy, new optimizations/advanced algorithms, data curation, data storage and transfer. An important part of science is explanation of results, ideally leading to theory formation. In this paper, we examine means for supporting the use of theory in big data analytics as well as using big data to assist in theory formation. One approach is to fit data in a way that is compatible with some theory, existing or new. Functional Data Analysis allows precise fitting of data as well as penalties for lack of smoothness or even departure from theoretical expectations. This paper discusses principal differential analysis and related techniques for fitting data where, for example, a time-based process is governed by an ordinary differential equation. Automation in theory formation is also considered. Case studies in the fields of computational economics and finance are considered.

In context of Industry 4.0 Augmented Reality (AR) is frequently mentioned as the upcoming interface technology for human-machine communication and collaboration. Many prototypes have already arisen in both the consumer market and in the industrial sector. According to numerous experts it will take only few years until AR will reach the maturity level to be deployed in productive applications. Especially for industrial usage it is required to assess security risks and challenges this new technology implicates. Thereby we focus on plant operators, Original Equipment Manufacturers (OEMs) and component vendors as stakeholders. Starting from several industrial AR use cases and the structure of contemporary AR applications, in this paper we identify security assets worthy of protection and derive the corresponding security goals. Afterwards we elaborate the threats industrial AR applications are exposed to and develop an edge computing architecture for future AR applications which encompasses various measures to reduce security risks for our stakeholders.

In today's growing concern for home security, we have developed an advanced security system using integrated digital signature and DNA cryptography. The digital signature is formed using multi-feature biometric traits which includes both fingerprint as well as iris image. We further increase the security by using DNA cryptography which is embedded on a smart card. In order to prevent unauthorized access manually or digitally, we use geo-detection which compares the unregistered devices location with the user's location using any of their personal devices such as smart phone or tab.

The Named-Data Networking (NDN) has emerged as a clean-slate Internet proposal on the wave of Information-Centric Networking. Although the NDN's data-plane seems to offer many advantages, e.g., native support for multicast communications and flow balance, it also makes the network infrastructure vulnerable to a specific DDoS attack, the Interest Flooding Attack (IFA). In IFAs, a botnet issuing unsatisfiable content requests can be set up effortlessly to exhaust routers' resources and cause a severe performance drop to legitimate users. So far several countermeasures have addressed this security threat, however, their efficacy was proved by means of simplistic assumptions on the attack model. Therefore, we propose a more complete attack model and design an advanced IFA. We show the efficiency of our novel attack scheme by extensively assessing some of the state-of-the-art countermeasures. Further, we release the software to perform this attack as open source tool to help design future more robust defense mechanisms.

Phishing is referred as an attempt to obtain sensitive information, such as usernames, passwords, and credit card details (and, indirectly, money), for malicious reasons, by disguising as a trustworthy entity in an electronic communication [1]. Hackers and malicious users, often use Emails as phishing tools to obtain the personal data of legitimate users, by sending Emails with authentic identities, legitimate content, but also with malicious URL, which help them to steal consumer's data. The high dimensional data in phishing context contains large number of redundant features that significantly elevate the classification error. Additionally, the time required to perform classification increases with the number of features. So extracting complex Features from phishing Emails requires us to determine which Features are relevant and fundamental in phishing detection. The dominant approaches in phishing are based on machine learning techniques; these rely on manual feature engineering, which is time consuming. On the other hand, deep learning is a promising alternative to traditional methods. The main idea of deep learning techniques is to learn complex features extracted from data with minimum external contribution [2]. In this paper, we propose new phishing detection and prevention approach, based first on our previous spam filter [3] to classify textual content of Email. Secondly it's based on Autoencoder and on Denoising Autoencoder (DAE), to extract relevant and robust features set of URL (to which the website is actually directed), therefore the features space could be reduced considerably, and thus decreasing the phishing detection time.

We are currently witnessing the development of increasingly effective author identification systems (AISs) that have the potential to track users across the internet based on their writing style. In this paper, we discuss two methods for providing user anonymity with respect to writing style: Adversarial Stylometry and Adversarial Authorship. With Adversarial Stylometry, a user attempts to obfuscate their writing style by consciously altering it. With Adversarial Authorship, a user can select an author cluster target (ACT) and write toward this target with the intention of subverting an AIS so that the user's writing sample will be misclassified Our results show that Adversarial Authorship via interactive evolutionary hill-climbing outperforms Adversarial Stylometry.

Distributed Denial of Service (DDoS) attacks are some of the most persistent threats on the Internet today. The evolution of DDoS attacks calls for an in-depth analysis of those attacks. A better understanding of the attackers' behavior can provide insights to unveil patterns and strategies utilized by attackers. The prior art on the attackers' behavior analysis often falls in two aspects: it assumes that adversaries are static, and makes certain simplifying assumptions on their behavior, which often are not supported by real attack data. In this paper, we take a data-driven approach to designing and validating three DDoS attack models from temporal (e.g., attack magnitudes), spatial (e.g., attacker origin), and spatiotemporal (e.g., attack inter-launching time) perspectives. We design these models based on the analysis of traces consisting of more than 50,000 verified DDoS attacks from industrial mitigation operations. Each model is also validated by testing its effectiveness in accurately predicting future DDoS attacks. Comparisons against simple intuitive models further show that our models can more accurately capture the essential features of DDoS attacks.

We provide an agent based simulation model of the Swedish payment system. The simulation model is to be used to analyze the consequences of loss of functionality, or disruptions of the payment system for the food and fuel supply chains as well as the bank sector. We propose a gaming simulation approach, using a computer based role playing game, to explore the collaborative responses from the key actors, in order to evoke and facilitate collective resilience.

Advanced Metering Infrastructure (AMI) have rapidly become a topic of international interest as governments have sponsored their deployment for the purposes of utility service reliability and efficiency, e.g., water and electricity conservation. Two problems plague such deployments. First is the protection of consumer privacy. Second is the problem of huge amounts of data from such deployments. A new architecture is proposed to address these problems through the use of Aggregators, which incorporate temporary data buffering and the modularization of utility grid analysis. These Aggregators are used to deliver anonymized summary data to the central utility while preserving billing and automated connection services.

Multi-hop Wireless Mesh Networks (WMNs) is a promising new technique for communication with routing protocol designs being critical to the effective and efficient of these WMNs. A common approach for routing traffic in these networks is to select a minimal distance from source to destination as in wire-line networks. Opportunistic Routing(OR) makes use of the broadcasting ability of wireless network and is especially very helpful for WMN because all nodes are static. Our proposed scheme of Multicast Opportunistic Routing(MOR) in WMNs is based on the broadcast transmissions and Learning Au-tomata (LA) to expand the potential candidate nodes that can aid in the process of retransmission of the data. The receivers are required to be in sync with one another in order to avoid duplicated broadcasting of data which is generally achieved by formulating the forwarding candidates according to some LA based metric. The most adorable aspect of this protocol is that it intelligently "learns" from the past experience and improves its performance. The results obtained via this approach of MOR, shows that the proposed scheme outperforms with some existing sachems and is an improved and more effective version of opportunistic routing in mesh network.

We study the notion of stability and perturbation resilience introduced by Bilu and Linial (2010) and Awasthi, Blum, and Sheffet (2012). A combinatorial optimization problem is α-stable or α-perturbation-resilient if the optimal solution does not change when we perturb all parameters of the problem by a factor of at most α. In this paper, we give improved algorithms for stable instances of various clustering and combinatorial optimization problems. We also prove several hardness results. We first give an exact algorithm for 2-perturbation resilient instances of clustering problems with natural center-based objectives. The class of clustering problems with natural center-based objectives includes such problems as k-means, k-median, and k-center. Our result improves upon the result of Balcan and Liang (2016), who gave an algorithm for clustering 1+√2≈2.41 perturbation-resilient instances. Our result is tight in the sense that no polynomial-time algorithm can solve (2−ε)-perturbation resilient instances of k-center unless NP = RP, as was shown by Balcan, Haghtalab, and White (2016). We then give an exact algorithm for (2−2/k)-stable instances of Minimum Multiway Cut with k terminals, improving the previous result of Makarychev, Makarychev, and Vijayaraghavan (2014), who gave an algorithm for 4-stable instances. We also give an algorithm for (2−2/k+δ)-weakly stable instances of Minimum Multiway Cut. Finally, we show that there are no robust polynomial-time algorithms for n1−ε-stable instances of Set Cover, Minimum Vertex Cover, and Min 2-Horn Deletion (unless P = NP).

Self-describing the content of a video is an elementary problem in artificial intelligence that joins computer vision and natural language processing. Through this paper, we propose a single system which could carry out video analysis (Object Detection and Captioning) at a reduced time and memory complexity. This single system uses YOLO (You Look Only Once) as its base model. Moreover, to highlight the importance of using transfer learning in development of the proposed system, two more approaches have been discussed. The rest one uses two discrete models, one to extract continuous bag of words from the frames and other to generate captions from those words i.e. Language Model. VGG-16 (Visual Geometry Group) is used as the base image decoder model to compare the two approaches, while LSTM is the base Language Model used. The Dataset used is Microsoft Research Video Description Corpus. The dataset was manually modified to serve the purpose of training the proposed system. Second approach which uses transfer learning proves to be the better approach for development of the proposed system.

This paper presents an integrated Analog Delay Line (ADL) for analog RF signal processing. The design is inspired by a Bucket Brigade Device (BBD) structure. It transfers charges from a sampled input signal stage after stage. It belongs to the Charge Coupled Devices (CCD). This ADL is fully differential with Common Mode (CM) control. The 28nm Fully Depleted Silicon on Insulator (FDSOI) Technology from ST Microelectronics is used for the design. Further results come from simulations using Spectre Cadence.

The analysis of multiple Android malware families indicates malware instances within a common malware family always have similar call graph structures. Based on the isomorphism of sensitive API call graph, we propose a method which is used to construct malware family features via combining static analysis approach with graph similarity metric. The experiment is performed on a malware dataset which contains 1326 malware samples from 16 different malware families. The result shows that the method can differentiate distinct malware family features and divide suspect malware samples into corresponding families with a high accuracy of 96.77% overall and even defend a certain extent of obfuscation.

Security protection is a concern for the Internet of Things (IoT) which performs data exchange autonomously over the internet for remote monitoring, automation and other applications. IoT implementations has raised concerns over its security and various research has been conducted to find an effective solution for this. Thus, this work focus on the analysis of an asymmetric encryption scheme, AA-Beta (AAβ) on a platform constrained in terms of processor capability, storage and random access Memory (RAM). For this work, the platform focused is ARM Cortex-M7 microcontroller. The encryption and decryption's performance on the embedded microcontroller is realized and time executed is measured. By enabled the I-Cache (Instruction cache) and D-Cache (Data Cache), the performances are 50% faster compared to disabled the D-Cache and I-Cache. The performance is then compared to our previous work on System on Chip (SoC). This is to analyze the gap of the SoC that has utilized the full GNU Multiple Precision Arithmetic Library (GMP) package versus ARM Cortex-M7 that using the mini-gmp package in term of the footprint and the actual performance.

Machine learning algorithms have been proven to be vulnerable to a special type of attack in which an active adversary manipulates the training data of the algorithm in order to reach some desired goal. Although this type of attack has been proven in previous work, it has not been examined in the context of a data stream, and no work has been done to study a targeted version of the attack. Furthermore, current literature does not provide any metrics that allow a system to detect these attack while they are happening. In this work, we examine the targeted version of this attack on a Support Vector Machine(SVM) that is learning from a data stream, and examine the impact that this attack has on current metrics that are used to evaluate a models performance. We then propose a new metric for detecting these attacks, and compare its performance against current metrics.

The collection of high frequency metering data in the emerging smart grid gives rise to the concern of consumer privacy. Anonymization of metering data is one of the proposed approaches in the literature, which enables transmission of unmasked data while preserving the privacy of the sender. Distributed anonymization methods can reduce the dependency on service providers, thus promising more privacy for the consumers. However, the distributed communication among the end-users introduces overhead and requires methods to prevent external attacks. In this paper, we propose four variants of a distributed anonymization method for smart metering data privacy, referred to as the Collaborative Anonymity Set Formation (CASF) method. The performance overhead analysis and security analysis of the variants are done using NS-3 simulator and the Scyther tool, respectively. It is shown that the proposed scheme enhances the privacy preservation functionality of an existing anonymization scheme, while being robust against external attacks.

In this paper, cyber physical system is analyzed from security perspective. A double closed-loop security control structure and algorithm with defense functions is proposed. From this structure, the features of several cyber attacks are considered respectively. By this structure, the models of information disclosure, denial-of-service (DoS) and Man-in-the-Middle Attack (MITM) are proposed. According to each kind attack, different models are obtained and analyzed, then reduce to the unified models. Based on this, system security conditions are obtained, and a defense scenario with detail algorithm is design to illustrate the implementation of this program.