Biblio

A single RFID (Radio Frequency Identification) is a technology for the remote identification of objects or people. It integrates a reader that receives the information contained in an RFID tag through an RFID authentication protocol. RFID provides several security services to protect the data transmitted between the tag and the reader. However, these advantages do not prevent an attacker to access this communication and remaining various security and privacy issues in these systems. Furthermore, with the rapid growth of IoT, there is an urgent need of security authentication and confidential data protection. Authentication protocols based on elliptic curve cryptographic (ECC) were widely investigated and implemented to guarantee protection against the various attacks that can suffer an RFID system. In this paper, we are going to focus on a comparative study between the most efficient ECC-based RFID authentication protocols that are already published, and study their security against the different wireless attacks.

The Internet of Things (IoT) is rapidly evolving, allowing physical items to share information and coordinate with other nodes, increasing IoT’s value and being widely applied to various applications. Radio Frequency Identification (RFID) is usually used in IoT applications to automate item identification by establishing symmetrical communication between the tag device and the reader. Because RFID reading data is typically in plain text, a security mechanism is required to ensure that the reading results from this RFID data remain confidential. Researchers propose a lightweight encryption algorithm framework for IoT-based RFID applications to address this security issue. Furthermore, this research assesses the implementation of lightweight encryption algorithms, such as Grain v1 and Espresso, as two systems scenarios. The Grain v1 encryption is the final eSTREAM project that accepts an 80-bit key, 64-bit IV, and has a 160-bit internal state with limited application. In contrast, the Espresso algorithm has been implemented in various applications such as 5G wireless communication. Furthermore, this paper tested the performance of each encryption algorithm in the microcontroller and inspected the network performance in an IoT system.

Thefts problem in household needs to be anticipated with home security system. One of simple methods is using automatic solenoid door lock system, so that it is difficult to be duplicated and will reduce the chance of theft action when the house is empty. Therefore, a home security system prototype that can be accessed by utilizing biometric fingerprint, Radio Frequency Identification (RFID), and keypad sensors was designed and tested. Arduino Uno works to turn on the door lock solenoid, so door access will be given when authentication is successful. Experimental results show that fingerprint sensor works well by being able to read fingerprints perfectly and the average time required to scan a fingerprint was 3.7 seconds. Meanwhile, Radio Frequency Identification (RFID) sensor detects Electronic-Kartu Tanda Penduduk (E-KTP) and the average time required for Radio Frequency Identification (RFID) to scan the card is about 2.4 seconds. Keypad functions to store password to unlock the door which produces the average time of 3.7 seconds after 10 trials. Average time to open with multi-sensor is 9.8 seconds. However, its drawback is no notification or SMS which directly be accessed by a cellphone or website with Wi-Fi or Telegram applications allow homeowners to monitor their doors from afar as to minimize the number of house thefts.

The current face recognition technology has rapidly come into the public life, from unlocking cell phone face to mobile payment, which has brought a lot of convenience to life. However, it is undeniable that it also brings security challenges. Based on this paper, we will discuss the risks of face recognition in the mobile payment and put forward relevant suggestions.

Despite the strict measures taken by authorities for children safety, crime against children is increasing. To curb this crime, it is important to improve the safety of children. School authorities can be severely penalized for these incidents, hence monitoring the school bus is significantly important in limiting these incidents. The developing worry of families for the security and insurance of their kids has started incredible interest in creating strong frameworks that give successful following and oversight of kids driving among home and school. Coordinated transport following permits youngsters to partake more in their normal schoolwork longer than trusting that a transport will be late with the assistance of notice and guarantees the security of every understudy. These days, reacting to the necessities existing apart from everything else, numerous instructive foundations have begun to push more towards a compelling global positioning framework of their vehicles that ensures the wellbeing of their understudies. Effective transport following is accomplished by procuring the geographic directions utilizing the GPS module and communicating the informationto a distant server. The framework depends on prepared to-utilize inactive RFID peruses. Make a message pop-up from the server script subsequent to checking the understudy's RFID tag be. The RFID examine exhibiting that the understudy boarded the vehicle to the specific trained professionals and the parent. Successful transport following permits school specialists, guardians, and drivers to precisely design their schedules while protecting kids from the second they get on until they get off the transport. The framework overall makes it conceivable to educate the administration regarding crises or protests. A variety of reports can be generated for different school-wide real-time bus and vehicle activities. This paper reviews the various smart security transport systems proposed for providing security features.

In this paper, a novel composite right/left-handed transmission line (CRLH TL) 3-unit cell is presented for finding excellent time-delay (TD) efficiency of Chipless RFID's True-Time-Delay Lines (TTDLs). RFID (Radio Frequency Identification) is a non-contact automatic identification technology that uses radio frequency (RF) signals to identify target items automatically and retrieve pertinent data without the need for human participation. However, as compared to barcodes, RFID tags are prohibitively expensive and complex to manufacture. Chipless RFID tags are RFID tags that do not contain silicon chips and are therefore less expensive and easier to manufacture. It combines radio broadcasting technology with radar technology. Radio broadcasting technology use radio waves to send and receive voice, pictures, numbers, and symbols, whereas radar technology employs the radio wave reflection theory. Chipless RFID lowers the cost of sensors such as gas, temperature, humidity, and pressure. In addition, Chipless RFID tags can be used as sensors which are also required for security purposes and future IoT applications.

Radio Frequency Identification (RFID) improves the efficiency of managing assets in supply chain applications throughout an entire life cycle or while in transport. Transfer of ownership of RFID-tagged items involves replacing information authorizing the old owner with information authorizing the new owner. In this work, we present a two-party, multiple tag, single-owner protocol for ownership transfer: 2P-mtOTP. This two-party protocol depends only on the communication among the two owners and the tags. Further, 2P-mtOTP is robust to attacks on its security, and it preserves the privacy of the owners and tags. We analyze our work in comparison to recent ownership transfer protocols in terms of security, privacy, and efficiency.

Recommenders are central in many applications today. The most effective recommendation schemes, such as those based on collaborative filtering (CF), exploit similarities between user profiles to make recommendations, but potentially expose private data. Federated learning and decentralized learning systems address this by letting the data stay on user's machines to preserve privacy: each user performs the training on local data and only the model parameters are shared. However, sharing the model parameters across the network may still yield privacy breaches. In this paper, we present Rex, the first enclave-based decentralized CF recommender. Rex exploits Trusted execution environments (TEE), such as Intel software guard extensions (SGX), that provide shielded environments within the processor to improve convergence while preserving privacy. Firstly, Rex enables raw data sharing, which ultimately speeds up convergence and reduces the network load. Secondly, Rex fully preserves privacy. We analyze the impact of raw data sharing in both deep neural network (DNN) and matrix factorization (MF) recommenders and showcase the benefits of trusted environments in a full-fledged implementation of Rex. Our experimental results demonstrate that through raw data sharing, Rex significantly decreases the training time by 18.3 x and the network load by 2 orders of magnitude over standard decentralized approaches that share only parameters, while fully protecting privacy by leveraging trustworthy hardware enclaves with very little overhead.

Recent years have witnessed a surge in ransomware attacks. Especially, many a new variant of ransomware has continued to emerge, employing more advanced techniques distributing the payload while avoiding detection. This renders the traditional static ransomware detection mechanism ineffective. In this paper, we present our Hardware Anomaly Realtime Detection - Lightweight (HARD-Lite) framework that employs semi-supervised machine learning method to detect ransomware using low-level hardware information. By using an LSTM network with a weighted majority voting ensemble and exponential moving average, we are able to take into consideration the temporal aspect of hardware-level information formed as time series in order to detect deviation in system behavior, thereby increasing the detection accuracy whilst reducing the number of false positives. Testing against various ransomware across multiple families, HARD-Lite has demonstrated remarkable effectiveness, detecting all cases tested successfully. What's more, with a hierarchical design that distributing the classifier from the user machine that is under monitoring to a server machine, Hard-Lite enables good scalability as well.

Cybersecurity is important in the field of information technology. One most recent pressing issue is information security. When we think of cybersecurity, the first thing that comes to mind is cyber-attacks, which are on the rise, such as Ransomware. Various governments and businesses take a variety of measures to combat cybercrime. People are still concerned about ransomware, despite numerous cybersecurity precautions. In ransomware, the attacker encrypts the victim’s files/data and demands payment to unlock the data. Cybersecurity is a collection of tools, regulations, security guards, security ideas, guidelines, risk management, activities, training, insurance, best practices, and technology used to secure the cyber environment, organization, and user assets. This paper analyses ransomware attacks, techniques for dealing with these attacks, and future challenges.

Cyber security is turning into a significant angle in each industry like in banking part, force and computerization segments. Servers are basic resources in these enterprises where business basic touch information is put away. These servers frequently join web servers in them through which any business information and tasks are performed remotely. Thus, clearly for a solid activity, security of web servers is extremely basic. This paper gives another testing way to deal with defenselessness appraisal of web applications by methods for breaking down and utilizing a consolidated arrangement of apparatuses to address a wide scope of security issues.

Physical Unclonable Functions (PUFs) are the secured hardware primitives to authenticate Integrated Circuits (ICs) from various unauthorized attacks. The secured key generation mechanism through PUFs is based on random Process Variations (PVs) inherited by the CMOS transistors. In this paper, we proposed a chaotic-based challenge generation mechanism to feed the arbiter PUFs. The chaotic property is introduced to increase the non-linearity in the arbitration mechanism thereby the uncertainty of the keys is attained. The chaotic sequences are easy to generate, difficult to intercept, and have the additional advantage of being in a large number Challenge-Response Pair (CRP) generation. The proposed design has a significant advantage in key generation with improved uniqueness and diffuseness of 47.33%, and 50.02% respectively. Moreover, the enhancement in the reliability of 96.14% and 95.13% range from −40C to 125C with 10% fluctuations in supply voltage states that it has prominent security assistance to the Internet of Things (IoT) enabled devices against malicious attacks.

A novel secure physical layer key generation method for Connected and Autonomous Vehicles (CAVs) against an attacker is proposed under fading and Additive White Gaussian Noise (AWGN). In the proposed method, a random sequence key is added to the demodulated sequence to generate a unique pre-shared key (PSK) to enhance security. Extensive computer simulation results proved that an attacker cannot extract the same legitimate PSK generated by the received vehicle even if identical fading and AWGN parameters are used both for the legitimate vehicle and attacker.

Safety-critical systems require resiliency against both cyberattacks and environmental faults. Researches have shown that microkernels can isolate components and limit the capabilities of would-be attackers by confining the attack in the component that it is initiated in. This limits the propagation of faults to sensitive components in the system. Nonetheless, the isolation mechanism in microkernels is not fully investigated for its resiliency against hardware faults. This paper investigates whether microkernels provide protection against hardware faults and, if so, to what extent quantitatively. This work is part of an effort in establishing an overlap between security and reliability with the goal of maximizing both while minimizing their impact on performance. In this work, transient faults are emulated on the seL4 microkernel and Linux kernel using debugger-induced bit flips across random timestamps in benchmark applications. Results show differences in the frequency and final outcome of fault to error manifestation in the seL4 environment compared to the Linux environment, including a reduction in silent data corruptions.

Connected Autonomous Vehicle (CAV) applications have shown the promise of transformative impact on road safety, transportation experience, and sustainability. However, they open large and complex attack surfaces: an adversary can corrupt sensory and communication inputs with catastrophic results. A key challenge in development of security solutions for CAV applications is the lack of effective infrastructure for evaluating such solutions. In this paper, we address the problem by designing an automated, flexible evaluation infrastructure for CAV security solutions. Our tool, CAVELIER, provides an extensible evaluation architecture for CAV security solutions against compromised communication and sensor channels. The tool can be customized for a variety of CAV applications and to target diverse usage models. We illustrate the framework with a number of case studies for security resiliency evaluation in Cooperative Adaptive Cruise Control (CACC).

With the inception of the Spectre attack in 2018, microarchitecture mitigation strategies propose secure cache hi-erarchies that do not leak the speculative state. Among many mitigation strategies, MuonTrap, proposes an efficient, secure cache hierarchy that provides speculative attack resiliency with minimum performance slowdown. Hardware prefetchers play a significant role in improving application performance by fetching and bringing data and instructions into caches before time. To prevent hardware prefetchers from leaking information about the speculative blocks brought into the cache, MuonTrap trains and triggers hardware prefetchers on the committed instruction streams, eliminating speculative state leakage. We find that on-commit prefetching can lead to significant performance slowdown as high as 20.46 % (primarily because of prefetch timeliness issues), making hardware prefetchers less effective. We propose Speculative yet Secure Prefetching (SpecPref), enhancements on top of the MuonTrap hierarchy that allows prefetching both on-commit and speculatively. We focus on improving the performance slowdown with the state-of-the-art hardware prefetchers without compromising the security guarantee provided by the MuonTrap implementation and provide an average performance slowdown of 1.17%.

Recently, Cloud Computing became one of today’s great innovations for provisioning Information Technology (IT) resources. Moreover, a new model has been introduced named Fog Computing, which addresses Cloud Computing paradigm issues regarding time delay and high cost. However, security challenges are still a big concern about the vulnerabilities to both Cloud and Fog Computing systems. Man- in- the- Middle (MITM) is considered one of the most destructive attacks in a Fog Computing context. Moreover, it’s very complex to detect MiTM attacks as it is performed passively at the Software-Defined Networking (SDN) level, also the Fog Computing paradigm is ideally suitable for MITM attacks. In this paper, a MITM mitigation scheme will be proposed consisting of an SDN network (Fog Leaders) which controls a layer of Fog Nodes. Furthermore, Multi-Path TCP (MPTCP) has been used between all edge devices and Fog Nodes to improve resource utilization and security. The proposed solution performance evaluation has been carried out in a simulation environment using Mininet, Ryu SDN controller and Multipath TCP (MPTCP) Linux kernel. The experimental results showed that the proposed solution improves security, network resiliency and resource utilization without any significant overheads compared to the traditional TCP implementation.

Many smartphones are lost every year, with a meager percentage recovered. In many cases, users with malicious intent access these phones and use them to acquire sensitive data. There is a need for continuous monitoring and surveillance in smartphones, and keystroke dynamics play an essential role in identifying whether a phone is being used by its owner or an impersonator. Also, there is a growing need to replace expensive 2-tier authentication methods like One-time passwords (OTP) with cheaper and more robust methods. The methods proposed in this paper are applied to existing data and are proven to train more robust classifiers. A novel feature extraction method by wavelet transformation is demonstrated to convert keystroke data into features. The comparative study of classifiers trained on the extracted features vs. features extracted by existing methods shows that the processes proposed perform better than the state-of-art feature extraction methods.

Keystroke dynamics is one solution to enhance the security of password authentication without adding any disruptive handling for users. Industries are looking for more security without impacting too much user experience. Considered as a friction-less solution, keystroke dynamics is a powerful solution to increase trust during user authentication without adding charge to the user. In this paper, we address the problem of user authentication considering the keystroke dynamics modality. We proposed a new approach based on the conversion of behavioral biometrics data (time series) into a 3D image. This transformation process keeps all the characteristics of the behavioral signal. The time series do not receive any filtering operation with this transformation and the method is bijective. This transformation allows us to train images based on convolutional neural networks. We evaluate the performance of the authentication system in terms of Equal Error Rate (EER) on a significant dataset and we show the efficiency of the proposed approach on a multi-instance system.

This paper presents a novel authentication method based on a distributed version of Kerberos for UAVs. One of the major problems of UAVs in recent years has been cyber-attacks which allow attackers to control the UAV or access its information. The growing use of UAVs has encouraged us to investigate the methods of their protection especially authentication of their users. In the past, the Kerberos system was rarely used for authentication in UAV systems. In our proposed method, based on a distributed version of Kerberos, we can authenticate multiple ground stations, users, and controllers for one or more UAVs. This method considers most of the security aspects to protect UAV systems mainly in the authentication phase and improves the security of UAVs and ground control stations and their communications considerably.

A recently emerged cellular network based One-Tap Authentication (OTAuth) scheme allows app users to quickly sign up or log in to their accounts conveniently: Mobile Network Operator (MNO) provided tokens instead of user passwords are used as identity credentials. After conducting a first in-depth security analysis, however, we have revealed several fundamental design flaws among popular OTAuth services, which allow an adversary to easily (1) perform unauthorized login and register new accounts as the victim, (2) illegally obtain identities of victims, and (3) interfere OTAuth services of legitimate apps. To further evaluate the impact of our identified issues, we propose a pipeline that integrates both static and dynamic analysis. We examined 1,025/894 Android/iOS apps, each app holding more than 100 million installations. We confirmed 396/398 Android/iOS apps are affected. Our research systematically reveals the threats against OTAuth services. Finally, we provide suggestions on how to mitigate these threats accordingly.

Flush-based cache attacks like Flush+Reload and Flush+Flush are highly precise and effective. Most of the flush-based attacks provide high accuracy in controlled and isolated environments where attacker and victim share OS pages. However, we observe that these attacks are prone to low accuracy on a noisy multi-core system with co-running applications. Two root causes for the varying accuracy of flush-based attacks are: (i) the dynamic nature of core frequencies that fluctuate depending on the system load, and (ii) the relative placement of victim and attacker threads in the processor, like same or different physical cores. These dynamic factors critically affect the execution latency of key instructions like clflush and mov, rendering the pre-attack calibration step ineffective.We propose DABANGG, a set of novel refinements to make flush-based attacks resilient to system noise by making them aware of frequency and thread placement. First, we introduce pre-attack calibration that is aware of instruction latency variation. Second, we use low-cost attack-time optimizations like fine-grained busy waiting and periodic feedback about the latency thresholds to improve the effectiveness of the attack. Finally, we provide victim-specific parameters that significantly improve the attack accuracy. We evaluate DABANGG-enabled Flush+Reload and Flush+Flush attacks against the standard attacks in side-channel and covert-channel experiments with varying levels of compute, memory, and IO-intensive system noise. In all scenarios, DABANGG+Flush+Reload and DABANGG+Flush+Flush outperform the standard attacks in stealth and accuracy.

We have proposed a new Smart Meter Application (SMA) Framework. This application registers consumers at utility provider (Electricity), takes the meter reading for electricity and makes billing. The proposed application might offer higher level of flexibility and security, time saving and trustworthiness between consumers and authority offices. It’s expected that the application will be developed by Flutter to support Android and iOS Mobile Operating Systems.

Driven by the progress of data and compute-intensive methods in various scientific domains, there is an in-creasing demand from researchers working with highly sensitive data to have access to the necessary computational resources to be able to adapt those methods in their respective fields. To satisfy the computing needs of those researchers cost-effectively, it is an open quest to integrate reliable security measures on existing High Performance Computing (HPC) clusters. The fundamental problem with securely working with sensitive data is, that HPC systems are shared systems that are typically trimmed for the highest performance - not for high security. For instance, there are commonly no additional virtualization techniques employed, thus, users typically have access to the host operating system. Since new vulnerabilities are being continuously discovered, solely relying on the traditional Unix permissions is not secure enough. In this paper, we discuss a generic and secure workflow that can be implemented on typical HPC systems allowing users to transfer, store and analyze sensitive data. In our experiments, we see an advantage in the asynchronous execution of IO requests, while reaching 80 % of the ideal performance.

We use mobile apps on a daily basis and there is an app for everything. We trust these applications with our most personal data. It is therefore important that these apps are as secure and well usable as possible. So far most studies on the maintenance and security of mobile applications have been done on Android applications. We do, however, not know how well these results translate to iOS.This research project aims to close this gap by analysing iOS applications with regards to maintainability and security. Regarding maintainability, we analyse code smells in iOS applications, the evolution of code smells in iOS applications and compare code smell distributions in iOS and Android applications. Regarding security, we analyse the evolution of the third-party library dependency network for the iOS ecosystem. Additionally, we analyse how publicly reported vulnerabilities spread in the library dependency network.Regarding maintainability, we found that the distributions of code smells in iOS and Android applications differ. Code smells in iOS applications tend to correspond to smaller classes, such as Lazy Class. Regarding security, we found that the library dependency network of the iOS ecosystem is not growing as fast as in some other ecosystems. There are less dependencies on average than for example in the npm ecosystem and, therefore, vulnerabilities do not spread as far.