Biblio

The deployment of smart devices in IoT applications are increasing with tremendous pace causing severe security concerns, as it trade most of private information. To counter that security issues in low resource applications, lightweight cryptographic algorithms have been introduced in recent past. In this paper we propose efficient hardware architecture of piccolo lightweight algorithm uses 64 bits block size with variable key size of length 80 and 128 bits. This paper introduces novel hardware architecture of piccolo-80, to supports high speed RFID security applications. Different design strategies are there to optimize the hardware metrics trade-off for particular application. The algorithm is implemented on different family of FPGAs with different devices to analyze the performance of design in 4 input LUTs and 6 input LUTs implementations. In addition, the results of hardware design are evaluated and compared with the most relevant lightweight block ciphers, shows the proposed architecture finds its utilization in terms of speed and area optimization from the hardware resources. The increment in throughput with optimized area of this architecture suggests that piccolo can applicable to implement for ultra-lightweight applications also.

This paper explains the work-in-progress on a vehicle safety and security evaluation platform. Since the testing of cyber-attacks on an actual may be costly or dangerous, the platform enables us to evaluate the threat and the risk associated with cyber-attacks in a safe virtual environment. The goal is to integrate vehicle and powertrain models, mobility and network simulators to actual hardware running the control algorithms using CAN communication. Hardware is selected so as to allows expandability and application of wireless modules which will act as additional attack surfaces. In the current paper, the framework and ideology behind is testbed is described and current progress is shown. A simple GPS spoofing attack on a virtual test vehicle is done and some initial results are discussed.

The growing trend toward information technology increases the amount of data travelling over the network links. The problem of detecting anomalies in data streams has increased with the growth of internet connectivity. Software-Defined Networking (SDN) is a new concept of computer networking that can adapt and support these growing trends. However, the centralized nature of the SDN design is challenged by the need for an efficient method for traffic monitoring against traffic anomalies caused by misconfigured devices or ongoing attacks. In this paper, we propose a new model for traffic behavior monitoring that aims to ensure trusted communication links between the network devices. The main objective of this model is to confirm that the behavior of the traffic streams matches the instructions provided by the SDN controller, which can help to increase the trust between the SDN controller and its covered infrastructure components. According to our preliminary implementation, the behavior monitoring unit is able to read all traffic information and perform a validation process that reports any mismatching traffic to the controller.

Pervasive systems over the years continuous to grow exponentially. Engagement of IoT in fields such as Agriculture, Home automation, industrial applications etc is on the rise. Self organizing networks within the IoT field give rise to engagement of various nodes for data communication. The rise in Cyber-attacks within IoT pose a lot of threat to these connected nodes and hence there is a need for data passing through nodes to be verified during communication. In this paper we proposed a nodal authentication approach in IoT using blockchain in securing the integrity of data passing through the nodes in IoT. In our work, we engaged the GOST algorithm in our approach. At the end, we achieved a nodal authentication and verification of the transmitted data. This makes it very difficult for an attacker to fake a node in the communication chain of the connected nodes. Data integrity was achieved in the nodes during the communication.

A Unique Object (UNO) is a physical object with unique characteristics that can be measured externally. The usually analogue measurement can be converted into a digital representation - a fingerprint - which uniquely identifies the object. For practical applications it is necessary that measurements can be performed without the need of specialist equipment or complex measurement setup. Furthermore, a UNO should be able to defeat simulation attacks; an attacker may replace the UNO with a device or system that produces the expected measurement. Recently a novel type of UNOs based on Quantum Dots (QDs) and exhibiting unique photo-luminescence properties has been proposed. The uniqueness of these UNOs is based on quantum effects that can be interrogated using a light source and a camera. The so called Quantum Confinement UNO (QCUNO) responds uniquely to different light excitation levels which is exploited for simulation attack protection, as opposed to focusing on features too small to reproduce and therefore difficult to measure. In this paper we describe methods for extraction of fingerprints from the QCUNO. We evaluate our proposed methods using 46 UNOs in a controlled setup. Focus of the evaluation are entropy, error resilience and the ability to detect simulation attacks.

In this contemporary world, working on internet is a crucial task owing to the security threats in the network like intrusions, injections etc. To recognize and reduce these system attacks, analysts and academicians have introduced Intrusion Detection Systems (IDSs) with the various standards and applications. There are different types of Intrusion Detection Systems (IDS) arise to solve the attacks in various environments. Though IDS is more powerful, it produces the results on the abnormal behaviours said to be attacks with false positive and false negative rates which leads to inaccurate detection rate. The other problem is that, there are more number of attacks arising simultaneously with different behaviour being detected by the IDS with high false positive rates which spoils the strength and lifetime of the system, system's efficiency and fault tolerance. Complex Event Processing (CEP) plays a vital role in handling the alerts as events in real time environment which mainly helps to recognize and reduce the redundant alerts.CEP identifies and analyses relationships between events in real time, allowing the system to proactively take efficient actions to respond to specific alerts.In this study, the tendency of Complex Event Processing (CEP) over Intrusion Detection System (IDS) which offers effective handling of the alerts received from IDS in real time and the promotion of the better detection of the attacks are discussed. The merits and challenges of CEP over IDS described in this paper helps to understand and educate the IDS systems to focus on how to tackle the dynamic attacks and its alerts in real time.

New IoT applications are demanding for more and more performance in embedded devices while their deployment and operation poses strict power constraints. We present the security concept for a customizable Internet of Things (IoT) platform based on the RISC-V ISA and developed by several Fraunhofer Institutes. It integrates a range of peripherals with a scalable computing subsystem as a three dimensional System-in-Package (3D-SiP). The security features aim for a medium security level and target the requirements of the IoT market. Our security architecture extends given implementations to enable secure deployment, operation, and update. Core security features are secure boot, an authenticated watchdog timer, and key management. The Universal Sensor Platform (USeP) SoC is developed for GLOBALFOUNDRIES' 22FDX technology and aims to provide a platform for Small and Medium-sized Enterprises (SMEs) that typically do not have access to advanced microelectronics and integration know-how, and are therefore limited to Commercial Off-The-Shelf (COTS) products.

There are many challenges when it comes to deploying robots remotely including lack of operator situation awareness and decreased trust. Here, we present a conversational agent embodied in a Furhat robot that can help with the deployment of such remote robots by facilitating teaming with varying levels of operator control.

The paper presents RoboScape, a collaborative, networked robotics environment that makes key ideas in computer science accessible to groups of learners in informal learning spaces and K-12 classrooms. RoboScape is built on top of NetsBlox, an open-source, networked, visual programming environment based on Snap! that is specifically designed to introduce students to distributed computation and computer networking. RoboScape provides a twist on the state of the art of robotics learning platforms. First, a user's program controlling the robot runs in the browser and not on the robot. There is no need to download the program to the robot and hence, development and debugging become much easier. Second, the wireless communication between a student's program and the robot can be overheard by the programs of the other students. This makes cybersecurity an immediate need that students realize and can work to address. We have designed and delivered a cybersecurity summer camp to 24 students in grades between 7 and 12. The paper summarizes the technology behind RoboScape, the hands-on curriculum of the camp and the lessons learned.

Crowdsensing, driven by the proliferation of sensor-rich mobile devices, has emerged as a promising data sensing and aggregation paradigm. Despite useful, traditional crowdsensing systems typically rely on a centralized third-party platform for data collection and processing, which leads to concerns like single point of failure and lack of operation transparency. Such centralization hinders the wide adoption of crowdsensing by wary participants. We therefore explore an alternative design space of building crowdsensing systems atop the emerging decentralized blockchain technology. While enjoying the benefits brought by the public blockchain, we endeavor to achieve a consolidated set of desirable security properties with a proper choreography of latest techniques and our customized designs. We allow data providers to safely contribute data to the transparent blockchain with the confidentiality guarantee on individual data and differential privacy on the aggregation result. Meanwhile, we ensure the service correctness of data aggregation and sanitization by delicately employing hardware-assisted transparent enclave. Furthermore, we maintain the robustness of our system against faulty data providers that submit invalid data, with a customized zero-knowledge range proof scheme. The experiment results demonstrate the high efficiency of our designs on both mobile client and SGX-enabled server, as well as reasonable on-chain monetary cost of running our task contract on Ethereum.

Many countries around the world have realized the benefits of the e-government platform in peoples' daily life, and accordingly have already made partial implementations of the key e-government processes. However, before full implementation of all potential services can be made, governments demand the deployment of effective information security measures to ensure secrecy and privacy of their citizens. In this paper, a robust watermarking algorithm is proposed to provide copyright protection for e-government document images. The proposed algorithm utilizes two transforms: the Discrete Wavelet Transformation (DWT) and the Singular Value Decomposition (SVD). Experimental results demonstrate that the proposed e-government document images watermarking algorithm performs considerably well compared to existing relevant algorithms.

In the current society, people pay more and more attention to identity security, especially in the case of some highly confidential or personal privacy, one-to-one identification is particularly important. The iris recognition just has the characteristics of high efficiency, not easy to be counterfeited, etc., which has been promoted as an identity technology. This paper has carried out research on daugman algorithm and iris edge detection.

The enormous growth of Internet-based traffic exposes corporate networks with a wide variety of vulnerabilities. Intrusive traffics are affecting the normal functionality of network's operation by consuming corporate resources and time. Efficient ways of identifying, protecting, and mitigating from intrusive incidents enhance productivity. As Intrusion Detection System (IDS) is hosted in the network and at the user machine level to oversee the malicious traffic in the network and at the individual computer, it is one of the critical components of a network and host security. Unsupervised anomaly traffic detection techniques are improving over time. This research aims to find an efficient classifier that detects anomaly traffic from NSL-KDD dataset with high accuracy level and minimal error rate by experimenting with five machine learning techniques. Five binary classifiers: Stochastic Gradient Decent, Random Forests, Logistic Regression, Support Vector Machine, and Sequential Model are tested and validated to produce the result. The outcome demonstrates that Random Forest Classifier outperforms the other four classifiers with and without applying the normalization process to the dataset.

The Internet of Things (IoT) visualizes a massive network with billions of interaction among smart things which are capable of contributing all sorts of services. Self-configuring things (nodes) are connected dynamically with a global network in IoT scenario. The small things are widely spread in a real world paradigm with minimal processing capacity and limited storage. The recent IoT technologies have more concerns about the security, privacy and reliability. Sharing personal data over the centralized system still remains as a challenging task. If the infrastructure is able to provide the assurance for transferring the data but for now it requires special attention on security and data consistency. Because, centralized system and infrastructure is viewed as a more attractive point for hacker or cyber-attacker. To solve this we present a secured smart contract based on Blockchain to develop a secured communicative network. A Hash based secret key is used for encryption and decryption purposes. A demo attack is done for developing a better understanding on blockchain technology in terms of their comparison and calculation.

Hardware Trojans are malicious modifications on integrated circuits (IC), which pose a grave threat to the security of modern military and commercial systems. Existing methods of detecting hardware Trojans are plagued by the inability of detecting all Trojans, reliance on golden chip that might not be available, high time cost, and low accuracy. In this paper, we present Golden Gates, a novel detection method designed to achieve a comparable level of accuracy to full reverse engineering, yet paying only a fraction of its cost in time. The proposed method inserts golden gate circuits (GGC) to achieve superlative accuracy in the classification of all existing gate footprints using rapid scanning electron microscopy (SEM) and backside ultra thinning. Possible attacks against GGC as well as malicious modifications on interconnect layers are discussed and addressed with secure built-in exhaustive test infrastructure. Evaluation with real SEM images demonstrate high classification accuracy and resistance to attacks of the proposed technique.

The hybrid microgrid is attracting great attention in recent years as it combines the main advantages of the alternating current (AC) and direct current (DC) microgrids. It is one of the best candidates to support a net-zero energy community. Thus, this paper investigates and compares different hybrid AC/DC microgrid configurations that are suitable for a net-zero energy community. Four different configurations are compared with each other in terms of their impacts on the overall system reliability, expandability, load shedding requirements, power sharing issues, net-zero energy capability, number of the required interface converters, and the requirement of costly medium-voltage components. The results of the investigations indicate that the best results are achieved when each building is enabled to supply its critical loads using an independent AC microgrid that is interfaced to the DC microgrid through a dedicated interface converter.

The botnet has been one of the most common threats to the network security since it exploits multiple malicious codes like worm, Trojans, Rootkit, etc. These botnets are used to perform the attacks, send phishing links, and/or provide malicious services. It is difficult to detect Peer-to-peer (P2P) botnets as compare to IRC (Internet Relay Chat), HTTP (HyperText Transfer Protocol) and other types of botnets because of having typical features of the centralization and distribution. To solve these problems, we propose an effective two-stage traffic classification method to detect P2P botnet traffic based on both non-P2P traffic filtering mechanism and machine learning techniques on conversation features. At the first stage, we filter non-P2P packages to reduce the amount of network traffic through well-known ports, DNS query, and flow counting. At the second stage, we extract conversation features based on data flow features and flow similarity. We detected P2P botnets successfully, by using Machine Learning Classifiers. Experimental evaluations show that our two-stage detection method has a higher accuracy than traditional P2P botnet detection methods.

Nowadays with the huge technological development, the reliance on technology has become enormous. Wireless Sensor Networks (WSN) is an example of using the Internet and communication between the patient and the hospital. Easy use of such networks helps to increase the quality of communication between patient and hospital. With the development of technology increased risk in use. Any change in this data between the patient and the hospital may cause false data that may harm the patient. In this paper, a secure protocol is designed to ensure the confidentiality, integrity, and availability of data transfer between the hospital and the patient, depending on the AES and RC4 algorithms.

The distribution of video contents generated by Content Providers (CPs) significantly contributes to increase the congestion within the networks of Internet Service Providers (ISPs). To alleviate this problem, CPs can serve a portion of their catalogues to the end users directly from servers (i.e., the caches) located inside the ISP network. Users served from caches perceive an increased QoS (e.g., average retrieval latency is reduced) and, for this reason, caching can be considered a form of traffic prioritization. Hence, since the storage of caches is limited, its subdivision among several CPs may lead to discrimination. A static subdivision that assignes to each CP the same portion of storage is a neutral but ineffective appraoch, because it does not consider the different popularities of the CPs' contents. A more effective strategy consists in dividing the cache among the CPs proportionally to the popularity of their contents. However, CPs consider this information sensitive and are reluctant to disclose it. In this work, we propose a protocol based on Shamir Secret Sharing (SSS) scheme that allows the ISP to calculate the portion of cache storage that a CP is entitled to receive while guaranteeing network neutrality and resource efficiency, but without violating its privacy. The protocol is executed by the ISP, the CPs and a Regulator Authority (RA) that guarantees the actual enforcement of a fair subdivision of the cache storage and the preservation of privacy. We perform extensive simulations and prove that our approach leads to higher hit-rates (i.e., percentage of requests served by the cache) with respect to the static one. The advantages are particularly significant when the cache storage is limited.

Quick UDP Internet Connections (QUIC) is an experimental transport protocol designed to primarily reduce connection establishment and transport latency, as well as to improve security standards with default end-to-end encryption in HTTPbased applications. QUIC is a multiplexed and secure transport protocol fostered by Google and its design emerged from the urgent need of innovation in the transport layer, mainly due to difficulties extending TCP and deploying new protocols. While still under standardisation, a non-negligble fraction of the Internet's traffic, more than 7% of a European Tier1-ISP, is already running over QUIC and it constitutes more than 30% of Google's egress traffic [1].

Medical Internet of Things (MIoT) offers innovative solutions to a healthier life, making radical changes in people's lives. Healthcare providers are enabled to continuously and remotely monitor their patients for many medial issues outside hospitals and healthcare providers' offices. MIoT systems and applications lead to increase availability, accessibility, quality and cost-effectiveness of healthcare services. On the other hand, MIoT devices generate a large amount of diverse real-time data, which is highly sensitive. Thus, securing medical data is an essential requirement when developing MIoT architectures. However, the MIoT architectures being developed in the literature have many security issues. To address the challenge of data security in MIoT, the integration of fog computing and MIoT is studied as an emerging and appropriate solution. By data security, it means that medial data is stored in fog nodes and transferred to the cloud in a secure manner to prevent any unauthorized access. In this paper, we propose a design for a secure fog-cloud based architecture for MIoT.

Network Function Virtualization (NFV) is an implementation of cloud computing that leverages virtualization technology to provide on-demand network functions such as firewalls, domain name servers, etc., as software services. One of the methods that help us understand the design and implementation process of such a new system in an abstract way is architectural modeling. Architectural modeling can be presented through UML diagrams to show the interaction between different components and its stakeholders. Also, it can be used to analyze the security threats and the possible countermeasures to mitigate the threats. In this paper, we show some of the possible threats that may jeopardize the security of NFV. We use misuse patterns to analyze misuses based on privilege escalation and VM escape threats. The misuse patterns are part of an ongoing catalog, which is the first step toward building a security reference architecture for NFV.

This paper proposes a multi-modular AC-DC converter system using wireless communication for a rapid charger of electric vehicles (EVs). The multi-modular topology, which consists of multiple modules, has an advantage on the expandability regarding voltage and power. In the proposed system, the input current and output voltage are controlled by each decentralized controller, which wirelessly communicates to the main controller, on each module. Thus, high-speed communication between the main and modules is not required. As the results in a reduced number of signal lines. The fundamental effectiveness of the proposed system is verified with a 3-kW prototype. In the experimented results, the input current imbalance rate is reduced from 49.4% to 0.1%, where total harmonic distortion is less than 3%.

Static application security testing (SAST) detects vulnerability warnings through static program analysis. Fixing the vulnerability warnings tremendously improves software quality. However, SAST has not been fully utilized by developers due to various reasons: difficulties in handling a large number of reported warnings, a high rate of false warnings, and lack of guidance in fixing the reported warnings. In this paper, we collaborated with security experts from a commercial SAST product and propose a set of approaches (Priv) to help developers better utilize SAST techniques. First, Priv identifies preferred fix locations for the detected vulnerability warnings, and group them based on the common fix locations. Priv also leverages visualization techniques so that developers can quickly investigate the warnings in groups and prioritize their quality-assurance effort. Second, Priv identifies actionable vulnerability warnings by removing SAST-specific false positives. Finally, Priv provides customized fix suggestions for vulnerability warnings. Our evaluation of Priv on six web applications highlights the accuracy and effectiveness of Priv. For 75.3% of the vulnerability warnings, the preferred fix locations found by Priv are identical to the ones annotated by security experts. The visualization based on shared preferred fix locations is useful for prioritizing quality-assurance efforts. Priv reduces the rate of SAST-specific false positives significantly. Finally, Priv is able to provide fully complete and correct fix suggestions for 75.6% of the evaluated warnings. Priv is well received by security experts and some features are already integrated into industrial practice.

Machine-learning solutions are successfully adopted in multiple contexts but the application of these techniques to the cyber security domain is complex and still immature. Among the many open issues that affect security systems based on machine learning, we concentrate on adversarial attacks that aim to affect the detection and prediction capabilities of machine-learning models. We consider realistic types of poisoning and evasion attacks targeting security solutions devoted to malware, spam and network intrusion detection. We explore the possible damages that an attacker can cause to a cyber detector and present some existing and original defensive techniques in the context of intrusion detection systems. This paper contains several performance evaluations that are based on extensive experiments using large traffic datasets. The results highlight that modern adversarial attacks are highly effective against machine-learning classifiers for cyber detection, and that existing solutions require improvements in several directions. The paper paves the way for more robust machine-learning-based techniques that can be integrated into cyber security platforms.