Biblio

We present True2F, a system for second-factor authentication that provides the benefits of conventional authentication tokens in the face of phishing and software compromise, while also providing strong protection against token faults and backdoors. To do so, we develop new lightweight two-party protocols for generating cryptographic keys and ECDSA signatures, and we implement new privacy defenses to prevent cross-origin token-fingerprinting attacks. To facilitate real-world deployment, our system is backwards-compatible with today's U2F-enabled web services and runs on commodity hardware tokens after a firmware modification. A True2F-protected authentication takes just 57ms to complete on the token, compared with 23ms for unprotected U2F.

Two-factor authentication (2FA) defends against password compromise by a remote attacker. We surveyed 4,275 students, faculty, and staff at Brigham Young University to measure user sentiment about Duo 2FA one year after the university adopted it. The results were mixed. A majority of the participants felt more secure using Duo and felt it was easy to use. About half of all participants reported at least one instance of being locked out of their university account because of an inability to authenticate with Duo. We found that students and faculty generally had more negative perceptions of Duo than staff. The survey responses reveal some pain points for Duo users. In response, we offer recommendations that reduce the frequency of 2FA for users. We also suggest UI changes that draw more attention to 2FA methods that do not require WiFi, the "Remember Me" setting, and the help utility.

Authorizing access to the public cloud has evolved over the last few years, from simple user authentication and password authentication to two-factor authentication (TOTP), with the addition of an additional field for entering a unique code. Today it is used by almost all major websites such as Facebook, Microsoft, Apple and is a frequently used solution for banking websites. On the other side, the private cloud solutions like OpenStack, CloudStack or Eucalyptus doesn't offer this security improvement. This article is presenting the advantages of this new type of authentication and synthetizes the TOTP authentication forms used by major cloud providers. Furthermore, the article is proposing to solve this challenge by presenting a practical solution for adding two-factor authentication for OpenStack cloud. For this purpose, the web authentication form has been modified and a new authentication module has been developed. The present document covers as well the entire process of adding a TOTP user, generating and sending the secret code in QR form to the user. The study concludes with OpenStack tools used for simplifying the entire process presented above.

A method to increase the resiliency of in-cone logic locking against the SAT attack is described in this paper. Current logic locking techniques provide protection through the addition of circuitry outside of the original logic cone. While the additional circuitry provides provable security against the SAT attack, other attacks, such as the removal attack, limit the efficacy of such techniques. Traditional in-cone logic locking is not prone to removal attacks, but is less secure against the SAT attack. The focus of this paper is, therefore, the analysis of in-cone logic locking to increase the security against the SAT attack, which provides a comparison between in-cone techniques and newly developed methodologies. A novel algorithm is developed that utilizes maximum fanout free cones (MFFC). The application of the algorithm limits the fanout of incorrect key information. The MFFC based algorithm resulted in an average increase of 61.8% in the minimum number of iterations required to complete the SAT attack across 1,000 different variable orderings of the circuit netlist while restricted to a 5% overhead in area.

Emerging intelligent systems have stringent constraints including cost and power consumption. When they are used in critical applications, resiliency becomes another key requirement. Much research into techniques for fault tolerance and dependability has been successfully applied to highly critical systems, such as those used in space, where cost is not an overriding constraint. Further, most resiliency techniques were focused on dealing with failures in the hardware and bugs in the software. The next generation of systems used in critical applications will also have to be tolerant to test escapes after manufacturing, soft errors and transients in the electronics, hardware bugs, hardware and software Trojans and viruses, as well as intrusions and other security attacks during operation. This paper will assess the impact of these threats on the results produced by a critical system, and proposed solutions to each of them. It is argued that run-time checks at the application-level are necessary to deal with errors in the results.

Keeping Internet users safe from attacks and other threats is one of the biggest security challenges nowadays. Distributed Denial of Service (DDoS) [1] is one of the most common attacks. DDoS makes the system stop working by resource overload. Software Define Networking (SDN) [2] has recently emerged as a new networking technology offering an unprecedented programmability that allows network operators to dynamically configure and manage their infrastructures. The flexible processing and centralized management of SDN controller allow flexibly deploying complex security algorithms and mitigation methods. In this paper, we propose a new TCP-SYN flood attack mitigation in SDN networks using machine learning. By using a testbed, we implement the proposed algorithms, evaluate their accuracy and address the trade-off between the accuracy and capacity of the security device. The results show that the algorithms can mitigate TCP-SYN Flood attack over 96.

In recent days, Enterprises are expanding their business efficiently through web applications which has paved the way for building good consumer relationship with its customers. The major threat faced by these enterprises is their inability to provide secure environments as the web applications are prone to severe vulnerabilities. As a result of this, many security standards and tools have been evolving to handle the vulnerabilities. Though there are many vulnerability detection tools available in the present, they do not provide sufficient information on the attack. For the long-term functioning of an organization, data along with efficient analytics on the vulnerabilities is required to enhance its reliability. The proposed model thus aims to make use of Machine Learning with Analytics to solve the problem in hand. Hence, the sequence of the attack is detected through the pattern using PAA and further the detected vulnerabilities are classified using Machine Learning technique such as SVM. Probabilistic results are provided in order to obtain numerical data sets which could be used for obtaining a report on user and application behavior. Dynamic and Reconfigurable PAA with SVM Classifier is a challenging task to analyze the vulnerabilities and impact of these vulnerabilities in heterogeneous web environment. This will enhance the former processing by analysis of the origin and the pattern of the attack in a more effective manner. Hence, the proposed system is designed to perform detection of attacks. The system works on the mitigation and prevention as part of the attack prediction.

Widespread use of Wireless Sensor Networks (WSNs) introduced many security threats due to the nature of such networks, particularly limited hardware resources and infrastructure less nature. Denial of Service attack is one of the most common types of attacks that face such type of networks. Building an Intrusion Detection and Prevention System to mitigate the effect of Denial of Service attack is not an easy task. This paper proposes the use of two machine learning techniques, namely decision trees and Support Vector Machines, to detect attack signature on a specialized dataset. The used dataset contains regular profiles and several Denial of Service attack scenarios in WSNs. The experimental results show that decision trees technique achieved better (higher) true positive rate and better (lower) false positive rate than Support Vector Machines, 99.86% vs 99.62%, and 0.05% vs. 0.09%, respectively.

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.

Nowadays due to economic reasons most of the semiconductor companies prefer to outsource the manufacturing part of their designs to third fabrication foundries, the so-called fabs. Untrustworthy fabs can extract circuit blocks, the called intellectual properties (IPs), from the layouts and then pirate them. Such fabs are suspected of hardware Trojan (HT) threat in which malicious circuits are added to the layouts for sabotage objectives. HTs lead up to increase power consumption in HT-infected circuits. However, due to process variations, the power of HTs including few gates in million-gate circuits is not detectable in power consumption analysis (PCA). Thus, such circuits should be considered as a collection of small sub-circuits, and PCA must be individually performed for each one of them. In this article, we introduce an approach facilitating PCA-based HT detection methods. Concerning this approach, we propose a new logic locking method and algorithm. Logic locking methods and algorithm are usually employed against IP piracy. They modify circuits such that they do not correctly work without applying a correct key to. Our experiments at the gate level and post-synthesis show that the proposed locking method and algorithm increase the proportion of HT activity and consequently HT power to circuit power.

With the globalization of integrated circuit (IC) design and manufacturing, malicious third-party vendors can easily insert hardware Trojans into their intellect property (IP) cores during IC design phase, threatening the security of IC systems. It is strongly required to develop hardware-Trojan detection methods especially for the IC design phase. As the particularity of Trigger nets in Trojan circuits, in this paper, we propose an ensemble-learning-based hardware-Trojan detection method by detecting the Trigger nets at the gate level. We extract the Trigger-net features for each net from known netlists and use the ensemble learning method to train two detection models according to the Trojan types. The detection models are used to identify suspicious Trigger nets in an unknown detected netlist and give results of suspiciousness values for each detected net. By flagging the top n% suspicious nets of each detection model as the suspicious Trigger nets based on the suspiciousness values, the proposed method can achieve, on average, 88% true positive rate, 90% true negative rate, and 90% Accuracy.

Hardware Trojan Horses and active fault attacks are a threat to the safety and security of electronic systems. By such manipulations, an attacker can extract sensitive information or disturb the functionality of a device. Therefore, several protections against malicious inclusions have been devised in recent years. A prominent technique to detect abnormal behavior in the field is run-time verification. It relies on dedicated monitoring circuits and on verification rules generated from a set of temporal properties. An important question when dealing with such protections is the effectiveness of the protection against unknown attacks. In this paper, we present a methodology based on automatic generation of monitoring and formal verification techniques that can be used to validate and analyze the quality of a set of temporal properties when used as protection against generic attackers of variable strengths.

Present security study involving analysis of manipulation of individual droplets of samples and reagents by digital microfluidic biochip has remarked that the biochip design flow is vulnerable to piracy attacks, hardware Trojans attacks, overproduction, Denial-of-Service attacks, and counterfeiting. Attackers can introduce bioprotocol manipulation attacks against biochips used for medical diagnosis, biochemical analysis, and frequent diseases detection in healthcare industry. Among these attacks, hardware Trojans have created a major threatening issue in its security concern with multiple ways to crack the sensitive data or alter original functionality by doing malicious operations in biochips. In this paper, we present a systematic algorithm for the assignment of checkpoints required for error-recovery of available bioprotocols in case of hardware Trojans attacks in performing operations by biochip. Moreover, it can guide the placement and timing of checkpoints so that the result of an attack is reduced, and hence enhance the security concerns of digital microfluidic biochips. Comparative study with traditional checkpoint schemes demonstrate the superiority of the proposed algorithm without overhead of the bioprotocol completion time with higher error detection accuracy.

Military communities have come to rely heavily on commercial off the shelf (COTS) standards and technologies for Internet of Things (IoT) operations. One of the major obstacles to military use of COTS IoT devices is the security of data transfer. In this paper, we successfully design and develop a lightweight, trust-based security architecture to support routing in a mobile IoT network. Specifically, we modify the RPL IoT routing algorithm using common security techniques, including a nonce identity value, timestamp, and network whitelist. Our approach allows RPL to select a routing path over a mobile IoT wireless network based on a computed node trust value and average received signal strength indicator (ARSSI) value across network members. We conducted simulations using the Cooja network simulator and Wireshark to validate the algorithm against stipulated threat models. We demonstrate that our algorithm can protect the network against Denial of Service (DoS) and Sybil based identity attacks. We also show that the control overhead required for our algorithm is less than 5% and that the packet delivery rate improves by nearly 10%.

The Internet of Things (IoT) is an emerging technology that plays a vital role in interconnecting various objects into a network to provide desired services within its resource constrained characteristics. In IoT, the Routing Protocol for Low power and Lossy network (RPL) is the standardized proactive routing protocol that achieves satisfying resource consumption, but it does not consider the node's routing behavior for forwarding data packets. The malicious intruders exploit these loopholes for launching various forms of routing attacks. Different security mechanisms have been introduced for detecting these attacks singly. However, the launch of multiple attacks such as Rank attack and Sybil attacks simultaneously in the IoT network is one of the devastating and destructive situations. This problem can be solved by establishing secure routing with trustworthy nodes. The trustworthiness of the nodes is determined using trust evaluation methods, where the parameters considered are based on the factors that influence in detecting the attacks. In this work, Providing Routing Security using the Technique of Collective Trust (PROTECT) mechanism is introduced, and it aims to provide a secure RPL routing by simultaneously detecting both Rank and Sybil attacks in the network. The advantage of the proposed scheme is highlighted by comparing its performance with the performance of the Sec-Trust protocol in terms of detection accuracy, energy consumption, and throughput.

With the recent proliferation of Internet of Things (IoT) and embedded devices, there is a growing need to develop a security framework to protect such devices. RISC-V is a promising open source architecture that targets low-power embedded devices and SoCs. However, there is a dearth of practical and low-overhead security solutions in the RISC-V architecture. Programs compiled using RISC-V toolchains are still vulnerable to code injection and code reuse attacks such as buffer overflow and return-oriented programming (ROP). In this paper, we propose FIXER, a hardware implemented security extension to RISC-V that provides a defense mechanism against such attacks. FIXER enforces fine-grained control-flow integrity (CFI) of running programs on backward edges (returns) and forward edges (calls) without requiring any architectural modifications to the RISC-V processor core. We implement FIXER on RocketChip, a RISC-V SoC platform, by leveraging the integrated Rocket Custom Coprocessor (RoCC) to detect and prevent attacks. Compared to existing software based solutions, FIXER reduces energy overhead by 60% at minimal execution time (1.5%) and area (2.9%) overheads.

With features such as decentralization, consistency, tamper resistance, non-repudiation, and pseudonym, blockchain technology has the potential to strengthen the Internet of Things (IoT) significantly, thus opening an intriguing research area in the integration of blockchain and IoT. However, most existing blockchain schemes were not dedicated to the IoT ecosystem and hence could not meet the specific requirements of IoT. This paper aims to fix the gap. Inspired by Chainspace, a blockchain platform which could be applicable in IoT, VChain is proposed, a novel blockchain scheme suitable for IoT which is more secure, concrete, and practical compared with Chainspace. Specifically, in VChain, a two-layer BFT-based consensus protocol with HoneyBadger BFT protocol is proposed and a collective signature scheme as building blocks. The designs above allow for supporting faulty-shards-tolerance and asynchronous network model, which could not be sustained in Chainspace, and keeping high efficiency as well. Moreover, the sharding strategy presented in VChain, different from that in RapidChain, which adopts the energy-consuming PoW mechanism for sharding, is environmentfriendly and thus makes VChain fit for IoT well. Last but not least, VChain also inherits the merits of Chainspace to separate the execution and verification of smart contracts for privacy.

Appending digital signatures and certificates to messages guarantee data integrity and ensure non-repudiation, but do not identify greedy authenticated nodes. Trust evolves if some reputable and trusted node verifies the node, data and evaluates the trustworthiness of the node using an accurate metric. But, even if the verifying party is a trusted centralized party, there is opacity and obscurity in computed reputation rating. The trusted party maps it with the node's identity, but how is it evaluated and what inputs derive the reputation rating remains hidden, thus concealment of transparency leads to privacy. Besides, the malevolent nodes might collude together for defamatory actions against reliable nodes, and eventually bad mouth these nodes or praise malicious nodes collaboratively. Thus, we cannot always assume the fairness of the nodes as the rating they give to any node might not be a fair one. In this paper, we propose a smart contract-based approach to update and query the reputation of nodes, stored and maintained by IPFS distributed storage. The use case particularly deals with an emergency scenario, dealing against colluding attacks. Our scheme is implemented using MATLAB simulation. The results show how smart contracts are capable of accurately identifying trustworthy nodes and record the reputation of a node transparently and immutably.

The key concerns in VANET communication are the security and privacy of the vehicles involved, but at the same time an efficient way to provide non-repudiation in the ad-hoc network is an important requirement. Most schemes proposed are using public key infrastructure (PKI) or symmetric key encryption to achieve security in VANET; both individually lack in serving the required purpose of providing privacy preservation of the involved On-Board Units (OBUs) (while still being able to offer non-repudiation) and amount to very sizeable overheads in computation. This paper proposes a privacy-preserving authentication protocol that employs hybrid cryptography, using the best features of PKI and symmetric cryptography to form a protocol that is scalable, efficient and offers services of integrity, non-repudiation, conditional privacy, and unlinkability; while still keeping the computational overhead at a reasonable level. The performance and security analysis of this scheme is provided to support the propositions.

Protection of secured data content in volatile memories (processor caches, embedded RAMs etc) is essential in networking, wireless, automotive and other embedded secure applications. It is utmost important to protect secret data, like authentication credentials, cryptographic keys etc., stored over volatile memories which can be hacked during normal device operations. Several security attacks like cold boot, disclosure attack, data remanence, physical attack, cache attack etc. can extract the cryptographic keys or secure data from volatile memories of the system. The content protection of memory is typically done by assuring data deletion in minimum possible time to minimize data remanence effects. In today's state-of-the-art SoCs, dedicated hardwares are used to functionally erase the private memory contents in case of security violations. This paper, in general, proposes a novel approach of using existing memory built-in-self-test (MBIST) hardware to zeroize (initialize memory to all zeros) on-chip memory contents before it is being hacked either through different side channels or secuirty attacks. Our results show that the proposed MBIST based content zeroization approach is substantially faster than conventional techniques. By adopting the proposed approach, functional hardware requirement for memory zeroization can be waived.

Software-defined networking (SDN) allows the smart grid to be centrally controlled and managed by decoupling the control plane from the data plane, but it also expands attack surface for attackers. Existing studies about the security of SDN-enabled smart grid (SDSG) mainly focused on static methods such as access control and identity authentication, which is vulnerable to attackers that carefully probe the system. As the attacks become more variable and complex, there is an urgent need for dynamic defense methods. In this paper, we propose a security function virtualization (SFV) based moving target defense of SDSG which makes the attack surface constantly changing. First, we design a dynamic defense mechanism by migrating virtual security function (VSF) instances as the traffic state changes. The centralized SDN controller is re-designed for global status monitoring and migration management. Moreover, we formalize the VSF instances migration problem as an integer nonlinear programming problem with multiple constraints and design a pre-migration algorithm to prevent VSF instances' resources from being exhausted. Simulation results indicate the feasibility of the proposed scheme.

This paper introduces an approach based on control theory to model, analyze and select optimal security policies for Moving Target Defense (MTD) deployment strategies. A Markov Decision Process (MDP) scheme is presented to model states of the system from attacking point of view. The employed value iteration method is based on the Bellman optimality equation for optimal policy selection for each state defined in the system. The model is then utilized to analyze the impact of various costs on the optimal policy. The MDP model is then applied to two case studies to evaluate the performance of the model.

With the interconnection of services and customers, network attacks are capable of large amounts of damage. Flexible Random Virtual IP Multiplexing (FRVM) is a Moving Target Defence (MTD) technique that protects against reconnaissance and access with address mutation and multiplexing. Security techniques must be trusted, however, FRVM, along with past MTD techniques, have gaps in realistic evaluation and thorough analysis of security and performance. FRVM, and two comparison techniques, were deployed on a virtualised network to demonstrate FRVM's security and performance trade-offs. The key results include the security and performance trade-offs of address multiplexing and address mutation. The security benefit of IP address multiplexing is much greater than its performance overhead, deployed on top of address mutation. Frequent address mutation significantly increases an attackers' network scan durations as well as effectively obfuscating and hiding network configurations.

Machine learning (ML) classifiers are vulnerable to adversarial examples. An adversarial example is an input sample which is slightly modified to induce misclassification in an ML classifier. In this work, we investigate white-box and grey-box evasion attacks to an ML-based malware detector and conduct performance evaluations in a real-world setting. We compare the defense approaches in mitigating the attacks. We propose a framework for deploying grey-box and black-box attacks to malware detection systems.

Operators of cyber-physical systems (CPSs) need to maintain awareness of the cyber situation in order to be able to adequately address potential issues in a timely manner. For instance, detecting early symptoms of cyber attacks may speed up the incident response process and mitigate consequences of attacks (e.g., business interruption, safety hazards). However, attaining a full understanding of the cyber situation may be challenging, given the complexity of CPSs and the ever-changing threat landscape. In particular, CPSs typically need to be continuously operational, may be sensitive to active scanning, and often provide only limited in-depth analysis capabilities. To address these challenges, we propose to utilize the concept of digital twins for enhancing cyber situational awareness. Digital twins, i.e., virtual replicas of systems, can run in parallel to their physical counterparts and allow deep inspection of their behavior without the risk of disrupting operational technology services. This paper reports our work in progress to develop a cyber situational awareness framework based on digital twins that provides a profound, holistic, and current view on the cyber situation that CPSs are in. More specifically, we present a prototype that provides real-time visualization features (i.e., system topology, program variables of devices) and enables a thorough, repeatable investigation process on a logic and network level. A brief explanation of technological use cases and outlook on future development efforts completes this work.