Biblio

An important principle in computational security is to reduce the attack surface, by maintaining the Trusted Computing Base (TCB) small. Even so, no security technique ensures full protection against any adversary. Thus, sensitive applications should be designed with several layers of protection so that, even if a layer might be violated, sensitive content will not be compromised. In 2015, Intel released the Software Guard Extensions (SGX) technology in its processors. This mechanism allows applications to allocate enclaves, which are private memory regions that can hold code and data. Other applications and even privileged code, like the OS kernel and the BIOS, are not able to access enclaves' contents. This paper presents a novel password file protection scheme, which uses Intel SGX to protect authentication credentials in the PAM authentication framework, commonly used in UNIX systems. We defined and implemented an SGX-enabled version of the pam\_unix.so authentication module, called UniSGX. This module uses an SGX enclave to handle the credentials informed by the user and to check them against the password file. To add an extra security layer, the password file is stored using SGX sealing. A threat model was proposed to assess the security of the proposed solution. The obtained results show that the proposed solution is secure against the threat model considered, and that its performance overhead is acceptable from the user point of view. The scheme presented here is also suitable to other authentication frameworks.

Power systems domain has generally been very conservative in terms of conducting digital forensic investigations, especially so since the advent of smart grids. This lack of research due to a multitude of challenges has resulted in absence of knowledge base and resources to facilitate such an investigation. Digitalization in the form of smart grids is upon us but in case of cyber-attacks, attribution to such attacks is challenging and difficult if not impossible. In this research, we have identified digital forensic artifacts resulting from a cyber-attack on Wide Area Monitoring, Protection and Control (WAMPAC) systems, which will help an investigator attribute an attack using the identified evidences. The research also shows the usage of sandboxing for digital forensics along with hardware-in-the-loop (HIL) setup. This is first of its kind effort to identify and acquire all the digital forensic evidences for WAMPAC systems which will ultimately help in building a body of knowledge and taxonomy for power system forensics.

Design for Testability (DfT) techniques allow devices to be tested at various levels of the manufacturing process. Scan architecture is a dominantly used DfT technique, which supports a high level of fault coverage, observability and controllability. However, scan architecture can be used by hardware attackers to gain critical information stored within the device. The security threats due to an unrestricted access provided by scan architecture has to be addressed to ensure hardware security. In this work, a solution based on the Clock and Data Recovery (CDR) method has been presented to authenticate users and limit the access to the scan architecture to authorized users. As compared to the available solution the proposed method presents a robust performance and reduces the area overhead by more than 10%.

Secret passwords are very widely used for user authentication to websites, despite their known shortcomings. Most websites using passwords also implement password recovery to allow users to re-establish a shared secret if the existing value is forgotten; many such systems involve sending a password recovery email to the user, e.g. containing a secret link. The security of password recovery, and hence the entire user-website relationship, depends on the email being acted upon correctly; unfortunately, as we show, such emails are not always designed to maximise security and can introduce vulnerabilities into recovery. To understand better this serious practical security problem, we surveyed password recovery emails for 50 of the top English language websites. We investigated a range of security and usability issues for such emails, covering their design, structure and content (including the nature of the user instructions), the techniques used to recover the password, and variations in email content from one web service to another. Many well-known web services, including Facebook, Dropbox, and Microsoft, suffer from recovery email design, structure and content issues. This is, to our knowledge, the first study of its type reported in the literature. This study has enabled us to formulate a set of recommendations for the design of such emails.

This paper proposed an advanced round modification fault analysis (RMFA) at the theoretical level on AEGIS-128, which is one of seven finalists in CAESAR competition. First, we clarify our assumptions and simplifications on the attack model, focusing on the encryption security. Then, we emphasize the difficulty of applying vanilla RMFA to AEGIS-128 in the practical case. Finally we demonstrate our advanced fault analysis on AEGIS-128 using machine-solver based algebraic techniques. Our enhancement can be used to conquer the practical scenario which is difficult for vanilla RMFA. Simulation results show that when the fault is injected to the initialization phase and the number of rounds is reduced to one, two samples of injections can extract the whole 128 key bits within less than two hours. This work can also be extended to other versions such as AEGIS-256.

This paper proposes an architecture of Secure Shell (SSH) honeypot using port knocking and Intrusion Detection System (IDS) to learn the information about attacks on SSH service and determine proper security mechanisms to deal with the attacks. Rapid development of information technology is directly proportional to the number of attacks, destruction, and data theft of a system. SSH service has become one of the popular targets from the whole vulnerabilities which is existed. Attacks on SSH service have various characteristics. Therefore, it is required to learn these characteristics by typically utilizing honeypots so that proper mechanisms can be applied in the real servers. Various attempts to learn the attacks and mitigate them have been proposed, however, attacks on SSH service are kept occurring. This research proposes a different and effective strategy to deal with the SSH service attack. This is done by combining port knocking and IDS to make the server keeps the service on a closed port and open it under user demand by sending predefined port sequence as an authentication process to control the access to the server. In doing so, it is evident that port knocking is effective in protecting SSH service. The number of login attempts obtained by using our proposed method is zero.

eAssessment uses technology to support online evaluation of students' knowledge and skills. However, challenging problems must be addressed such as trustworthiness among students and teachers in blended and online settings. The TeSLA system proposes an innovative solution to guarantee correct authentication of students and to prove the authorship of their assessment tasks. Technologically, the system is based on the integration of five instruments: face recognition, voice recognition, keystroke dynamics, forensic analysis, and plagiarism. The paper aims to analyze and compare the results achieved after the second pilot performed in an online and a blended university revealing the realization of trust-driven solutions for eAssessment.

In the distributed Internet of Things (IoT) architecture, sensors collect data from vehicles, home appliances and office equipment and other environments. Various objects contain the sensor which process data, cooperate and exchange information with other embedded devices and end users in a distributed network. It is important to provide end-to-end communication security and an authentication system to guarantee the security and reliability of the data in such a distributed system. Two-factor authentication is a solution to improve the security level of password-based authentication processes and immunized the system against many attacks. At the same time, the computational and storage overhead of an authentication method also needs to be considered in IoT scenarios. For this reason, many cryptographic schemes are designed especially for the IoT; however, we observe a lack of laboratory hardware test beds and modules, and universal authentication hardware modules. This paper proposes a design and analysis for a hardware module in the IoT which allows the use of two-factor authentication based on smart cards, while taking into consideration the limited processing power and energy reserves of nodes, as well as designing the system with scalability in mind.

In 2017, Gelernter et al. identified the ``password-reset man-in-the-middle'' attack, which can take over a user's account during two-factor authentication. In this attack, a password reset request is sent via an SMS message instead of an expected authentication request, and the user enters a reset code at the malicious man-in-the-middle website without recognizing that the code resets the password. Following this publication, most vulnerable websites attempted to remove the vulnerability. However, it is still not clear whether these attempts were sufficient to prevent careless users from being attacked. In this paper, we describe the results of an investigation involving domestic major websites that were vulnerable to this type of attack. To clarify the causes of vulnerability, we conducted experiments with 180 subjects. The SMS-message parameters were ``with/without warning'', ``numeric/alphanumeric code'', and ``one/two messages'', and subjects were tested to see if they input the reset code into the fake website. According to the result of the experiment, we found that the PRMitM risk odds were increased 4.6, 1.86, and 11.59 times higher in a no-warning case, a numeric-only reset code, and a behavior that change passwords very frequently, respectively.

Two-factor authentication (2FA) popularly works by verifying something the user knows (a password) and something she possesses (a token, popularly instantiated with a smart phone). Conventional 2FA systems require extra interaction like typing a verification code, which is not very user-friendly. For improved user experience, recent work aims at zero-effort 2FA, in which a smart phone placed close to a computer (where the user enters her username/password into a browser to log into a server) automatically assists with the authentication. To prove her possession of the smart phone, the user needs to prove the phone is on the login spot, which reduces zero-effort 2FA to co-presence detection. In this paper, we propose SoundAuth, a secure zero-effort 2FA mechanism based on (two kinds of) ambient audio signals. SoundAuth looks for signs of proximity by having the browser and the smart phone compare both their surrounding sounds and certain unpredictable near-ultrasounds; if significant distinguishability is found, SoundAuth rejects the login request. For the ambient signals comparison, we regard it as a classification problem and employ a machine learning technique to analyze the audio signals. Experiments with real login attempts show that SoundAuth not only is comparable to existent schemes concerning utility, but also outperforms them in terms of resilience to attacks. SoundAuth can be easily deployed as it is readily supported by most smart phones and major browsers.

With the recent advances in information and communication technology, Web and Mobile Internet applications have become a part of our daily lives. These developments have also emerged Information Security concept due to the necessity of protecting information of institutions from Internet attackers. There are many security approaches to provide information security in Enterprise applications. However, using only one of these approaches may not be efficient enough to obtain security. This paper describes a Multi-Layered Framework of implementing two-factor and single sign-on authentication together. The proposed framework generates unique one-time passwords (OTP), which are used to authenticate application data. Nevertheless, using only OTP mechanism does not meet security requirements. Therefore, implementing a separate authentication application which has single sign-on capability is necessary.

In this paper, we propose a new authentication method to prevent authentication vulnerability of Claim Token method of Membership Service provide in Private BlockChain. We chose Hyperledger Fabric v1.0 using JWT authentication method of membership service. TOTP, which generate OTP tokens and user authentication codes that generate additional time-based password on existing authentication servers, has been applied to enforce security and two-factor authentication method to provide more secure services.

Cyber-Physical Systems (CPS) and Internet of Things (IoT) are emerging technologies, which makes the remote sensing and control across heterogeneous network a reality, and has good prospects in industrial applications. Due to the resource constrained feature of CPS devices, the design of security and efficiency balanced authentication scheme for CPS/IoT devices becomes a big challenge in CPS/IoT applications. This paper presents a two-factor authentication with key agreement scheme for CPS/IoT applications. The proposed mechanism integrates IMSI identifier and identity-based remote mutual authentication scheme on BAN logic designs. It supports flawless two-factor and mutual authentication of participants and agreement of session keys for user, device and gateway server. The proposed mechanism also provide user anonymity, it can be adopt in critical applications. Besides, it does not require modifying the software of clients; thus, it is highly flexibly. We believe the proposed mechanism is usable for CPS/IoT applications.

User authentication on smartphones must satisfy both security and convenience, an inherently difficult balancing art. Apple's FaceID is arguably the latest of such efforts, at the cost of additional hardware (e.g., dot projector, flood illuminator and infrared camera). We propose a novel user authentication system EchoPrint, which leverages acoustics and vision for secure and convenient user authentication, without requiring any special hardware. EchoPrint actively emits almost inaudible acoustic signals from the earpiece speaker to "illuminate" the user's face and authenticates the user by the unique features extracted from the echoes bouncing off the 3D facial contour. To combat changes in phone-holding poses thus echoes, a Convolutional Neural Network (CNN) is trained to extract reliable acoustic features, which are further combined with visual facial landmark locations to feed a binary Support Vector Machine (SVM) classifier for final authentication. Because the echo features depend on 3D facial geometries, EchoPrint is not easily spoofed by images or videos like 2D visual face recognition systems. It needs only commodity hardware, thus avoiding the extra costs of special sensors in solutions like FaceID. Experiments with 62 volunteers and non-human objects such as images, photos, and sculptures show that EchoPrint achieves 93.75% balanced accuracy and 93.50% F-score, while the average precision is 98.05%, and no image/video based attack is observed to succeed in spoofing.

Ubiquitous Healthcare System (U-Healthcare) is a well-known application of wireless sensor networking (WSN). In this system, the sensors take less power for operating the function. As the data transfers between sensor and other stations is sensitive so there needs to provide a security scheme. Due to the low life of sensor nodes in Wireless Sensor Networks (WSN), asymmetric key based security (AKS) architecture is always considered as unsuitable for these types of networks. Several papers have been published in recent past years regarding how to incorporate AKS in WSN, Haque et al's Asymmetric key based Architecture (AKA) is one of them. But later it is found that this system has authentication problem and therefore prone to man-in-the-middle (MITM) attack, furthermore it is not a truly asymmetric based scheme. We address these issues in this paper and proposed a complete asymmetric approach using PEKS-PM (proposed by Pham in [8]) to remove impersonation attack. We also found some other vulnerabilities in the original AKA system and proposed solutions, therefore making it a better and enhanced asymmetric key based architecture.

Public key cryptography or asymmetric keys are widely used in the implementation of data security on information and communication systems. The RSA algorithm (Rivest, Shamir, and Adleman) is one of the most popular and widely used public key cryptography because of its less complexity. RSA has two main functions namely the process of encryption and decryption process. Digital Signature Algorithm (DSA) is a digital signature algorithm that serves as the standard of Digital Signature Standard (DSS). DSA is also included in the public key cryptography system. DSA has two main functions of creating digital signatures and checking the validity of digital signatures. In this paper, the authors compare the computational times of RSA and DSA with some bits and choose which bits are better used. Then combine both RSA and DSA algorithms to improve data security. From the simulation results, the authors chose RSA 1024 for the encryption process and added digital signatures using DSA 512, so the messages sent are not only encrypted but also have digital signatures for the data authentication process.

With the pervasiveness of the Internet of Things (IoT) and the rapid progress of wireless communications, Wireless Body Area Networks (WBANs) have attracted significant interest from the research community in recent years. As a promising networking paradigm, it is adopted to improve the healthcare services and create a highly reliable ubiquitous healthcare system. However, the flourish of WBANs still faces many challenges related to security and privacy preserving. In such pervasive environment where the context conditions dynamically and frequently change, context-aware solutions are needed to satisfy the users' changing needs. Therefore, it is essential to design an adaptive access control scheme that can simultaneously authorize and authenticate users while considering the dynamic context changes. In this paper, we propose a context-aware access control and anonymous authentication approach based on a secure and efficient Hybrid Certificateless Signcryption (H-CLSC) scheme. The proposed scheme combines the merits of Ciphertext-Policy Attribute-Based Signcryption (CP-ABSC) and Identity-Based Broadcast Signcryption (IBBSC) in order to satisfy the security requirements and provide an adaptive contextual privacy. From a security perspective, it achieves confidentiality, integrity, anonymity, context-aware privacy, public verifiability, and ciphertext authenticity. Moreover, the key escrow and public key certificate problems are solved through this mechanism. Performance analysis demonstrates the efficiency and the effectiveness of the proposed scheme compared to benchmark schemes in terms of functional security, storage, communication and computational cost.

Connected cars have received massive attention in Intelligent Transportation System. Many potential services, especially safety-related ones, rely on spatial-temporal messages periodically broadcast by cars. Without a secure authentication algorithm, malicious cars may send out invalid spatial-temporal messages and then deny creating them. Meanwhile, a lot of private information may be disclosed from these spatial-temporal messages. Since cars move on expressways at high speed, any authentication must be performed in real-time to prevent crashes. In this paper, we propose a Fast and Anonymous Spatial-Temporal Trust (FastTrust) mechanism to ensure these properties. In contrast to most authentication protocols which rely on fixed infrastructures, FastTrust is distributed and mostly designed on symmetric-key cryptography and an entropy-based commitment, and is able to fast authenticate spatial-temporal messages. FastTrust also ensures the anonymity and unlinkability of spatial-temporal messages by developing a pseudonym-varying scheduling scheme on cars. We provide both analytical and simulation evaluations to show that FastTrust achieves the security and privacy properties. FastTrust is low-cost in terms of communication and computational resources, authenticating 20 times faster than existing Elliptic Curve Digital Signature Algorithm.

Design of anonymous authentication scheme is one of the most important challenges in Vehicular Ad hoc Networks (VANET). Most of the existing schemes have high computational and communication overhead and they do not meet security requirements. Recently, Azees et al. have introduced an Efficient Anonymous Authentication with Conditional Privacy-Preserving (EAAP) scheme for VANET and claimed that it is secure. In this paper, we show that this protocol is vulnerable against replay attack, impersonation attack and message modification attack. Also, we show that the messages sent by a vehicle are linkable. Therefore, an adversary can easily track the vehicles. In addition, it is shown that vehicles face with some problems when they enter in a new Trusted Authority (TA) range. As a solution, we propose a new authentication protocol which is more secure than EAAP protocol without increasing its computational and communication overhead.

In spite of being a promising technology which will make our lives a lot easier we cannot be oblivious to the fact IoT is not safe from online threat and attacks. Thus, along with the growth of IoT we also need to work on its aspects. Taking into account the limited resources that these devices have it is important that the security mechanisms should also be less complex and do not hinder the actual functionality of the device. In this paper, we propose an ECC based lightweight authentication for IoT devices which deploy RFID tags at the physical layer. ECC is a very efficient public key cryptography mechanism as it provides privacy and security with lesser computation overhead. We also present a security and performance analysis to verify the strength of our proposed approach.

A privately owned smart device connected to a corporate network using a USB connection creates a potential channel for malware infection and its subsequent spread. For example, air-gapped (a.k.a. isolated) systems are considered to be the most secure and safest places for storing critical datasets. However, unlike network communications, USB connection streams have no authentication and filtering. Consequently, intentional or unintentional piggybacking of a malware infected USB storage or a mobile device through the air-gap is sufficient to spread infection into such systems. Our findings show that the contact rate has an exceptional impact on malware spread and destabilizing free malware equilibrium. This work proposes a USB authentication and delegation protocol based on radiofrequency identification (RFID) in order to stabilize the free malware equilibrium in air-gapped networks. The proposed protocol is modelled using Coloured Petri nets (CPN) and the model is verified and validated through CPN tools.

Vehicular Ad-hoc Networks (VANETs) are a subset of Mobile Ad-hoc Networks (MANETs). They are deployed to introduce the ability of inter-communication among vehicles in order to guarantee safety and provide services for people while driving. VANETs are exposed to many types of attacks like denial of service, spoofing, ID disclosure and Sybil attacks. In this paper, a novel lightweight approach for preventing Sybil attack in VANETs is proposed. The presented protocol scheme uses symmetric key encryption and authentication between Road Side Units (RSUs) and vehicles on the road so that no malicious vehicle could gain more than one identity inside the network. This protocol does not need managers for Road Side Units (RSUs) or Certification Authority (CA) and uses minimum amount of messages exchanged with RSU making the scheme efficient and effective.

A novel scheme is proposed to authenticate sensors and detect data integrity attacks in a Cyber Physical System (CPS). The proposed technique uses the hardware characteristics of a sensor and physics of a process to create unique patterns (herein termed as fingerprints) for each sensor. The sensor fingerprint is a function of sensor and process noise embedded in sensor measurements. Uniqueness in the noise appears due to manufacturing imperfections of a sensor and due to unique features of a physical process. To create a sensor's fingerprint a system-model based approach is used. A noise-based fingerprint is created during the normal operation of the system. It is shown that under data injection attacks on sensors, noise pattern deviations from the fingerprinted pattern enable the proposed scheme to detect attacks. Experiments are performed on a dataset from a real-world water treatment (SWaT) facility. A class of stealthy attacks is designed against the proposed scheme and extensive security analysis is carried out. Results show that a range of sensors can be uniquely identified with an accuracy as high as 98%. Extensive sensor identification experiments are carried out on a set of sensors in SWaT testbed. The proposed scheme is tested on a variety of attack scenarios from the reference literature which are detected with high accuracy

Device-to-device communication is widely used for mobile devices and Internet of Things. Authentication and key agreement are critical to build a secure channel between two devices. However, existing approaches often rely on a pre-built fingerprint database and suffer from low key generation rate. We present GeneWave, a fast device authentication and key agreement protocol for commodity mobile devices. GeneWave first achieves bidirectional initial authentication based on the physical response interval between two devices. To keep the accuracy of interval estimation, we eliminate time uncertainty on commodity devices through fast signal detection and redundancy time cancellation. Then, we derive the initial acoustic channel response for device authentication. We design a novel coding scheme for efficient key agreement while ensuring security. Therefore, two devices can authenticate each other and securely agree on a symmetric key. GeneWave requires neither special hardware nor pre-built fingerprint database, and thus it is easyto-use on commercial mobile devices. We implement GeneWave on mobile devices (i.e., Nexus 5X and Nexus 6P) and evaluate its performance through extensive experiments. Experimental results show that GeneWave efficiently accomplish secure key agreement on commodity smartphones with a key generation rate 10× faster than the state-of-the-art approach.

To prevent users' privacy from leakage, more and more mobile devices employ biometric-based authentication approaches, such as fingerprint, face recognition, voiceprint authentications, etc., to enhance the privacy protection. However, these approaches are vulnerable to replay attacks. Although state-of-art solutions utilize liveness verification to combat the attacks, existing approaches are sensitive to ambient environments, such as ambient lights and surrounding audible noises. Towards this end, we explore liveness verification of user authentication leveraging users' lip movements, which are robust to noisy environments. In this paper, we propose a lip reading-based user authentication system, LipPass, which extracts unique behavioral characteristics of users' speaking lips leveraging build-in audio devices on smartphones for user authentication. We first investigate Doppler profiles of acoustic signals caused by users' speaking lips, and find that there are unique lip movement patterns for different individuals. To characterize the lip movements, we propose a deep learning-based method to extract efficient features from Doppler profiles, and employ Support Vector Machine and Support Vector Domain Description to construct binary classifiers and spoofer detectors for user identification and spoofer detection, respectively. Afterwards, we develop a binary tree-based authentication approach to accurately identify each individual leveraging these binary classifiers and spoofer detectors with respect to registered users. Through extensive experiments involving 48 volunteers in four real environments, LipPass can achieve 90.21% accuracy in user identification and 93.1% accuracy in spoofer detection.