Biblio

As recently studied, network-on-chip (NoC) suffers growing threats from hardware trojans (HTs), leading to performance degradation or information leakage when it provides communication service in many/multi-core systems. Therefore, defense techniques against NoC HTs experience rapid development in recent years. However, to the best of our knowledge, there are few standard benchmarks developed for the defense techniques evaluation. To address this issue, in this paper, we design a suite of benchmarks which involves multiple NoCs with different HTs, so that researchers can compare various HT defense methods fairly by making use of them. We first briefly introduce the features of target NoC and its infected modules in our benchmarks, and then, detail the design of our NoC HTs in a one-by-one manner. Finally, we evaluate our benchmarks through extensive simulations and report the circuit cost of NoC HTs in terms of area and power consumption, as well as their effects on NoC performance. Besides, comprehensive experiments, including functional testing and side channel analysis are performed to assess the stealthiness of our HTs.

In recent years, various benchmark suites have been developed to evaluate the efficacy of Android security analysis tools. Tool developers often choose such suites based on the availability and popularity of suites and not on their characteristics and relevance due to the lack of information about them. In this context, based on a recent effort, we empirically evaluated four Android-specific benchmark suites: DroidBench, Ghera, ICCBench, and UBCBench. For each benchmark suite, we identified the APIs used by the suite that were discussed on Stack Overflow in the context of Android app development and measured the usage of these APIs in a sample of 227K real-world apps (coverage). We also identified security-related APIs used in real-world apps but not in any of the above benchmark suites to assess the opportunities to extend benchmark suites (gaps).

The presence of software vulnerabilities is a serious threat to any software project. Exploiting them can compromise system availability, data integrity, and confidentiality. Unfortunately, many open source projects go for years with undetected ready-to-exploit critical vulnerabilities. In this study, we investigate the presence of software vulnerabilities in open source projects and the factors that influence this presence. We analyzed the top 100 open source PHP applications in GitHub using a static analysis vulnerability scanner to examine how common software vulnerabilities are. We also discussed which vulnerabilities are most present and what factors contribute to their presence. We found that 27% of these projects are insecure, with a median number of 3 vulnerabilities per vulnerable project. We found that the most common type is injection vulnerabilities, which made 58% of all detected vulnerabilities. Out of these, cross-site scripting (XSS) was the most common and made 43.5% of all vulnerabilities found. Statistical analysis revealed that project activities like branching, pulling, and committing have a moderate positive correlation with the number of vulnerabilities in the project. Other factors like project popularity, number of releases, and number of issues had almost no influence on the number of vulnerabilities. We recommend that open source project owners should set secure code development guidelines for their project members and establish secure code reviews as part of the project's development process.

Creative works, whether paintings or memes, follow unique journeys that result in their final form. Understanding these journeys, a process known as "provenance analysis," provides rich insights into the use, motivation, and authenticity underlying any given work. The application of this type of study to the expanse of unregulated content on the Internet is what we consider in this paper. Provenance analysis provides a snapshot of the chronology and validity of content as it is uploaded, re-uploaded, and modified over time. Although still in its infancy, automated provenance analysis for online multimedia is already being applied to different types of content. Most current works seek to build provenance graphs based on the shared content between images or videos. This can be a computationally expensive task, especially when considering the vast influx of content that the Internet sees every day. Utilizing non-content-based information, such as timestamps, geotags, and camera IDs can help provide important insights into the path a particular image or video has traveled during its time on the Internet without large computational overhead. This paper tests the scope and applicability of metadata-based inferences for provenance graph construction in two different scenarios: digital image forensics and cultural analytics.

Many technological cases exploiting data science have been realized in recent years; machine learning, Internet of Things, and stream data processing are examples of this trend. Other advanced applications have focused on capturing the value from streaming data of different objects of transport and traffic management in an Intelligent Transportation System (ITS). In this context, security control and trust level play a decisive role in the sustainable adoption of this trend. However, conceptual work integrating the security approaches of different disciplines into one coherent reference architecture is limited. The contribution of this paper is a reference architecture for ITS security (called SITS). In addition, a classification of Big Data technologies, products, and services to address the ITS trust challenges is presented. We also proposed a novel multi-tier ITS security framework for validating the usability of SITS with business intelligence development in the enterprise domain.

Whenever we talk about big data, the concern is always about the security of the data. In recent days the most heard about technology is the Natural Language Processing. This new and trending technology helps in solving the ever ending security problems which are not completely solved using big data. Starting with the big data security issues, this paper deals with addressing the topics related to cyber security and information security using the Natural Language Processing technology. Including the well-known cyber-attacks such as phishing identification and spam detection, this paper also addresses issues on information assurance and security such as detection of Advanced Persistent Threat (APT) in DNS and vulnerability analysis. The goal of this paper is to provide the overview of how natural language processing can be used to address cyber security issues.

Bitcoin is a cryptocurrency which acts as an application protocol that works on top of the IP protocol. This paper focuses on distinct Bitcoin security features, including security services, mechanisms, and algorithms. Further, we propose a well-defined security functional architecture to minimize security risks. The security features and requirements of Bitcoin have been structured in layers.

Adversarial attacks have been the hot research field in artificial intelligence security. Decision-based black-box adversarial attacks are much more appropriate in the real-world scenarios, where only the final decisions of the targeted deep neural networks are accessible. However, since there is no available guidance for searching the imperceptive adversarial perturbation, boundary attack, one of the best performing decision-based black-box attacks, carries out computationally expensive search. For improving attack efficiency, we propose a novel black box explanation guided decision-based black-box adversarial attack. Firstly, the problem of decision-based adversarial attacks is modeled as a derivative-free and constraint optimization problem. To solve this optimization problem, the black box explanation guided constrained random search method is proposed to more quickly find the imperceptible adversarial example. The insights into the targeted deep neural networks explored by the black box explanation are fully used to accelerate the computationally expensive random search. Experimental results demonstrate that our proposed attack improves the attack efficiency by 64% compared with boundary attack.

We explore how to build a blind certificate authority (CA). Unlike conventional CAs, which learn the exact identity of those registering a public key, a blind CA can simultaneously validate an identity and provide a certificate binding a public key to it, without ever learning the identity. Blind CAs would therefore allow bootstrapping truly anonymous systems in which no party ever learns who participates. In this work we focus on constructing blind CAs that can bind an email address to a public key. To do so, we first introduce secure channel injection (SCI) protocols. These allow one party (in our setting, the blind CA) to insert a private message into another party's encrypted communications. We construct an efficient SCI protocol for communications delivered over TLS, and use it to realize anonymous proofs of account ownership for SMTP servers. Combined with a zero-knowledge certificate signing protocol, we build the first blind CA that allows Alice to obtain a X.509 certificate binding her email address alice@domain.com to a public key of her choosing without ever revealing “alice” to the CA. We show experimentally that our system works with standard email server implementations as well as Gmail.

The cost and complexity of conducting multi-site clinical trials have significantly increased over time, with site monitoring, data management, and Institutional Review Board (IRB) amendments being key drivers. Trial sponsors, such as pharmaceutical companies, are also increasingly outsourcing trial management to multiple organizations. Enforcing compliance with standard operating procedures, such as preserving data privacy for human subject protection, is crucial for upholding the integrity of a study and its findings. Current efforts to ensure quality of data collected at multiple sites and by multiple organizations lack a secure, trusted, and efficient framework for fragmented data capture. To address this challenge, we propose a novel data management infrastructure based on a permissioned blockchain with private channels, smart contracts, and distributed ledgers. We use an example multi-organizational clinical trial to design and implement a blockchain network: generate activity-specific private channels to segregate data flow for confidentiality, write channel-specific smart contracts to enforce regulatory guidelines, monitor the immutable transaction log to detect protocol breach, and auto-generate audit trail. Through comprehensive experimental study, we demonstrate that our system handles high-throughput transactions, exhibits low-latency, and constitutes a trusted, scalable solution.

With the transformation of connected vehicles into the Internet of Vehicles (IoV), the time is now ripe for paving the way for the next generation of connected vehicles with novel applications and innovative security measures. The connected vehicles are experiencing prenominal growth in the auto industry, but are still studded with many security and privacy vulnerabilities. Today's IoV applications are part of cyber physical communication systems that collect useful information from thousands of smart sensors associated with the connected vehicles. The technology advancement has paved the way for connected vehicles to share significant information among drivers, auto manufacturers, auto insurance companies and operational and maintenance service providers for various applications. The critical issues in engineering the IoV applications are effective to use of the available spectrum and effective allocation of good channels an opportunistic manner to establish connectivity among vehicles, and the effective utilization of the infrastructure under various traffic conditions. Security and privacy in information sharing are the main concerns in a connected vehicle communication network. Blockchain technology facilitates secured communication among users in a connected vehicles network. Originally, blockchain technology was developed and employed with the cryptocurrency. Bitcoin, to provide increased trust, reliability, and security among users based on peer-to-peer networks for transaction sharing. In this paper, we propose to integrate blockchain technology into ad hoc vehicular networking so that the vehicles can share network resources with increased trust, reliability, and security using distributed access control system and can benefit a wider scope of scalable IoV applications scenarios for decision making. The proposed architecture is the faithful environment for information sharing among connected vehicles. Blockchain technology allows multiple copies of data storage at the distribution cloud. Distributed access control system is significantly more secure than a traditional centralized system. This paper also describes how important of ad hoc vehicular networking in human life, possibilities in real-world implementation and its future trends. The ad hoc vehicular networking may become one of the most trendy networking concepts in the future that has the perspective to bring out much ease human beneficial and secured applications.

In recent years, the application of Bluetooth has always been the highly debated topic among the researches. Through the Bluetooth protocol, Bluetooth can implement the data switching in short distance between various devices. Nevertheless, BlueBorne Attack makes the seemingly stable Bluetooth protocols full of vulnerabilities. Our research will concentrate on predicting the BlueBorne Attack with the following directions: the working mechanism, the working methods and effective range of BlueBorne. Based on the comprehensive review of recent peer-reviewed researches, this project provides a new model based on application layer to solve the security problem of BlueBorne. The paper asserts that compared with the previous research, the unique model has better consequence with highly stability.

We present data from detailed observations of a “controlled in-the-wild” study of Brain-Computer Interface (BCI) system. During 10 days of demonstration at seven nonspecialized public events, 1563 people learned about the system in various social configurations. Observations of audience behavior revealed recurring behavioral patterns. From these observations a framework of interaction with BCI systems was deduced. It describes the phases of passing by an installation, viewing and reacting, passive and active interaction, group interactions, and follow-up actions. We also conducted semi-structured interviews with the people who interacted with the system. The interviews revealed the barriers and several directions for further research on BCIs. Our findings can be useful for designing the BCIs foxr everyday adoption by a wide range of people.

IoT devices introduce unprecedented threats into home and professional networks. As they fail to adhere to security best practices, they are broadly exploited by malicious actors to build botnets or steal sensitive information. Their adoption challenges established security standard as classic security measures are often inappropriate to secure them. This is even more problematic in sensitive environments where the presence of insecure IoTs can be exploited to bypass strict security policies. In this paper, we demonstrate an attack against a highly secured network using a Bluetooth smart bulb. This attack allows a malicious actor to take advantage of a smart bulb to exfiltrate data from an air gapped network.

Researchers develop bioassays by rigorously experimenting in the lab. This involves significant fiscal and skilled person-hour investment. A competitor can reverse engineer a bioassay implementation by imaging or taking a video of a biochip when in use. Thus, there is a need to protect the intellectual property (IP) rights of the bioassay developer. We introduce a novel 3D multilayer-based obfuscation to protect a biochip against reverse engineering.

The presence of security vulnerabilities in automotive networks has already been shown by various publications in recent years. Due to the specification of the Controller Area Network (CAN) as a broadcast medium without security mechanisms, attackers are able to read transmitted messages without being noticed and to inject malicious messages. In order to detect potential attackers within a network or software system as early as possible, Intrusion Detection Systems (IDSs) are prevalent. Many approaches for vehicles are based on techniques which are able to detect deviations from specified CAN network behaviour regarding protocol or payload properties. However, it is challenging to detect attackers who secretly connect to CAN networks and do not actively participate in bus traffic. In this paper, we present an approach that is capable of successfully detecting unknown CAN devices and determining the distance (cable length) between the attacker device and our sensing unit based on Time Domain Reflectometry (TDR) technique. We evaluated our approach on a real vehicle network.

The Supervisory Control and Data Acquisition (SCADA) system is the most commonly used industrial control system but is subject to a wide range of serious threats. Intrusion detection systems are deployed to promote the security of SCADA systems, but they continuously generate tremendous number of alerts without further comprehending them. There is a need for an efficient system to correlate alerts and discover attack strategies to provide explainable situational awareness to SCADA operators. In this paper, we present a causal-polytree-based anomaly reasoning framework for SCADA networks, named CAPTAR. CAPTAR takes the meta-alerts from our previous anomaly detection framework EDMAND, correlates the them using a naive Bayes classifier, and matches them to predefined causal polytrees. Utilizing Bayesian inference on the causal polytrees, CAPTAR can produces a high-level view of the security state of the protected SCADA network. Experiments on a prototype of CAPTAR proves its anomaly reasoning ability and its capabilities of satisfying the real-time reasoning requirement.

A popular countermeasure against IP piracy relies on obfuscating the Finite State Machine (FSM), which is assumed to be the heart of a digital system. In this paper, we propose to use a special class of non-group additive cellular automata (CA) called D1 * CA, and it's counterpart D1 * CAdual to obfuscate each state-transition of an FSM. The synthesized FSM exhibits correct state-transitions only for a correct key, which is a designer's secret. The proposed easily testable key-controlled FSM synthesis scheme can thwart reverse engineering attacks, thus offers IP protection.

This paper deals with a new indoor localization scheme called “CEPHEID” by using ceiling lighting fixtures. It is based on the fact that each lighting fixture has its own characteristic flickering pattern. Then, the author proposes a technique to identify individual light by using simple instruments and DNN classifier. Thanks to the less requirements for hardware, CEPHEID can be implemented by a few simple discrete electronic components and an ordinary smartphone. A prototype “CEPHEID dongle” is also introduced in this paper. Finally, the validity of the author's method is examined by indoor positioning experiments.

In VANET, vehicles periodically transmit beacon messages to the neighboring vehicles and the RSU. To establish the authenticity of these messages, a number of digital signature schemes have been proposed in literature. Many of these schemes enable an RSU to perform aggregate verification of the signatures to deal with high vehicle density scenarios. These schemes are either based on traditional PKC concept involving certificate management overhead or identity based cryptography having key escrow problem. Further, these schemes require the existence of OBU device which is resistant to side channel attacks. In this paper, we propose a hierarchical trusted authority privacy preserving certificateless aggregate signature scheme for VANET. In addition to providing message authentication, integrity and non-repudiation, our scheme is resistant to message forgeability attack. The proposed scheme assumes hierarchical organization of network such that vehicles operate under multiple trusted authorities (TA) which in turn are controlled by single root TA. Using our scheme, the entity could verify messages received from vehicles which operate under multiple TAs. The proposed scheme is free from key escrow problem and resistant to side channel attacks on OBU. It also possesses conditional linkability such that originator of a message could be revealed whenever required. Simulations confirm the efficient nature in terms of verification delay as compared to other well known schemes proposed in literature.

Due to the rise of huge population in mankind and the large variety of upcoming utilization of power, the energy requirement has substantially increased. Smart Grid is a very important part of the Smart Cities initiative and is one of the crucial components in distribution and reconciliation of energy. Security of the smart grid infrastructure, which is an integral part of the smart grid framework, intended at transitioning the conventional power grid system into a robust, reliable, adaptable and intelligent energy utility, is an impending problem that needs to be arrested quickly. With the increasingly intensifying integration of smart devices in the smart grid infrastructure with other interconnected applications and the communication backbone is compelling both the energy users and the energy utilities to thoroughly look into the privacy and security issues of the smart grid. In this paper, we present challenges of the existing security mechanisms deployed in the smart grid framework and we tried to bring forward the unresolved problems that would highlight the security aspects of Smart Grid as a challenging area of research and development in the future.

To efficiently provide the ancient ceramic composition analysis and testing services, it is necessary to efficiently classify the ancient ceramics in ceramic cloud service platform. In this paper, we get the 8 kinds of major chemical contents of the body and glaze in each sample according to analyze 35 samples. After establishing of the classification model of two samples, the results indicate: as long as choosing SVM algorithm correctly, the classification results of body and glaze samples will be quite ideal, and the support vector machine is a very valuable new method which can process ancient porcelains data.

Cross site scripting (XSS) attack is one of the attacks on the web. It brings session hijack with HTTP cookies, information collection with fake HTML input form and phishing with dummy sites. As a countermeasure of XSS attack, machine learning has attracted a lot of attention. There are existing researches in which SVM, Random Forest and SCW are used for the detection of the attack. However, in the researches, there are problems that the size of data set is too small or unbalanced, and that preprocessing method for vectorization of strings causes misclassification. The highest accuracy of the classification was 98% in existing researches. Therefore, in this paper, we improved the preprocessing method for vectorization by using word2vec to find the frequency of appearance and co-occurrence of the words in XSS attack scripts. Moreover, we also used a large data set to decrease the deviation of the data. Furthermore, we evaluated the classification results with two procedures. One is an inappropriate procedure which some researchers tend to select by mistake. The other is an appropriate procedure which can be applied to an attack detection filter in the real environment.

Internet of things is a network formed between two or more devices through internet which helps in sharing data and resources. IoT is present everywhere and lot of applications in our day-to-day life such as smart homes, smart grid system which helps in reducing energy consumption, smart garbage collection to make cities clean, smart cities etc. It has some limitations too such as concerns of security of the network and the cost of installations of the devices. There have been many researches proposed various method in improving the IoT systems. In this paper, we have discussed about the scope and limitations of IoT in various fields and we have also proposed a technique to secure offline architecture of IoT.

The heart of research pumps for analyzing risks in today's competitive business environment where big, massive computations are performed on interconnected devices pervasively. Advanced computing environments i.e. Cloud, big data and Internet of things are taken under consideration for finding and analyzing business risks developed from evolutionary, interoperable and digital devices communications with massive volume of data generated. Various risks in advanced computational environment have been identified in this research and are provided with risks mitigation strategies. We have also focused on how risk management affects these environments and how that effect can be mitigated for software and business quality improvement.