Timing Attacks Research Articles

Mechanical Performance and Durability of Mortar Based on Slag Cement and NaOH-Activated Slag

This paper investigates the valorization of slag in cement production in order to obtain a sustainable mortar and participate in protecting the environment. The study evaluated the setting time, hydration heat, mechanical strengths, drying shrinkage, sulfuric acid and sulfate attack of mortars. These mortars are based on Portland cement (PC), slag cements containing 10%, 30% and 50% slag and alkali-activated slag (AAS) using 6% and 9% of sodium hydroxide (NaOH). The results show that the increase in slag replacement rate increases the setting time accompanied by a drop in initial mechanical strength such that the compressive strength decreased by 30% at two days for a 50% slag substitution; also, it considerably reduces the shrinkage and hydration heat. The resistance to sulfate and sulfuric acid attack increases with the slag replacement rate. NaOH-activated slag mortar is the most resistant binder to sulfate attack and sulfuric acid, but it develops a lower mechanical strength and a more significant shrinkage than PC mortar. X-ray diffraction (XRD) analysis carried out on binder paste shows the formation of the same main hydration products in PC and slag cement with a small amount of portlandite in the last binder. Calcium silicate hydrate (CSH) and Hydrotalcite are the main hydration products of AAS.

TPPD: Targeted Pseudo Partitioning based Defence for cross-core covert channel attacks

Contemporary computing employs cache hierarchy to fill the speed gap between processors and main memories. In order to optimise system performance, Last Level Caches (LLC) are shared among all the cores. Cache sharing has made them an attractive surface for cross-core timing channel attacks. In these attacks, an attacker running on another core can exploit the access timing of the victim process to infiltrate the secret information. One such attack is called a cross-core Covert Channel Attack (CCA). Timely detection and then prevention of cross-core CCA is critical for maintaining the integrity and security of users, especially in a shared computing environment. In this work, we have proposed an efficient cross-core CCA mitigation technique. We propose a way-wise cache partitioning on targeted sets, only for the processes suspected to be attackers. In this way, the performance impact on the entire LLC is minimised, and benign applications can utilise the LLC to its full capacity. We have used a cycle-accurate simulator (gem5) to analyse the performance of the proposed method and its security effectiveness. It has been successful in abolishing the cross-core covert timing channel attack with no significant performance impact on benign applications. It causes 23% less cache misses in comparison to existing partitioning based solutions while requiring ≈0.26% storage overhead.

New Masks, New Meanings: Covid Perspectives on African Art History: Part 2: Coups, Pandemics, and Careers in African Art History

New Masks, New Meanings: Covid Perspectives on African Art History: Part 2: Coups, Pandemics, and Careers in African Art History

Analysis of the Variables Influencing Success in Elite Handball with Polar Coordinates

In today’s elite handball, coaching staff seek to know as much as possible about all the details of their sport to gain an advantage by adapting their model of play or by looking for the opponent’s weak points. Therefore, the aim of this study was to analyse which variables can influence success in each phase of the match with polar coordinates. Observational methodology was used to analyse success or failure within the nature of handball by means of an ad hoc observation instrument designed and validated for this research. A total of 14 elite men’s handball matches from the 2019–2020 season were analysed. The relationships between success and failure of all behaviours were performed with polar coordinates. The results show that one of the keys to achieving victory in matches is centred on a high level of success in the defensive phase that allows the team to recover the ball and to be able to go on the counterattack to obtain a clear option for a goal. This research allows us to see how we can achieve success in the different phases of the game and improve team performance with these indicators. These results suggest that it is necessary for teams to train at a high pace of play, linking the different phases of the game in order to recover the ball in the defensive phase and attack in the shortest possible time against an unstructured defence to achieve success in the match and the final victory.

AI-TASFIS: An Approach to Secure Vehicle-to-Vehicle Communication

ABSTRACT VANET provides communication between vehicles. VANET nodes are highly dynamic. Therefore, it is essential to increase the stability of the communication between nodes. The cluster head and cluster node provide stable communication between vehicles. Vehicle communications are being challenged by factors such as security, confidential communication, and severe delay. This work proposes an Artificial Intelligence (AI)-based Sugeno fuzzy inference system to overcome these issues. The proposed secure trust-based cluster techniques are less complex, with lesser communication delays, lower overhead, and more efficient in accurately locating trusted nodes for communication. Vehicular Ad hoc Networks (VANET) should send data between vehicles and use traffic safety indicators using an Enhanced Cluster-based routing protocol. AI-TASFIS is Artificial Intelligence-based Trust Authentication Sugeno fuzzy inference system that uses ANFIS-based Sugeno Fuzzy inference systems to calculate the node weights for choosing trusted cluster head and cluster member that reduces malicious attacks like Black Hole Attacks, Wormhole attacks, and Timing Attacks while transferring data packets. Simulation results show that the proposed Artificial Intelligence (AI)-based Sugeno fuzzy inference system provides network security, reduces end-to-end delay, and increases packet delivery ratio and throughput.

An Inertia-Based Data Recovery Scheme for False Data Injection Attack

Due to vulnerabilities exposed to cyberattacks in the cyber physical power system, increasing concerns have been paid to its cybersecurity, especially on the so-called false data injection attack. Timely recovering true values of measurements and states after encountering cyber-attacks is of paramount importance for ensuring the subsequent controls and operations of the cyber physical power system. This article, for the first time, discovers a measurement data inertia effect, and uses this effect to deduce coarse values of preattack measurements as a preliminary work for data recovery. Then, based on the deduced coarse values and suggested state bounds, an optimization model is proposed to recover the measurements and states contaminated by attacks in-time. Moreover, an error criterion named interval error is proposed to assess the entire performance of the proposed recovery scheme. Extensive and comprehensive experiments are implemented on the IEEE 30-bus test benchmark to verify the feasibility and effectiveness of the proposed recovery scheme. The numerical studies reveal that the proposed method can achieve high accuracy and efficient timeliness for data recovery.

MULBER: Effective Android Malware Clustering Using Evolutionary Feature Selection and Mahalanobis Distance Metric

Symmetric and asymmetric patterns are fascinating phenomena that show a level of co-existence in mobile application behavior analyses. For example, static phenomena, such as information sharing through collaboration with known apps, is a good example of a symmetric model of communication, and app collusion, where apps collaborate dynamically with unknown malware apps, is an example of a serious threat with an asymmetric pattern. The symmetric nature of app collaboration can become vulnerable when a vulnerability called PendingIntent is exchanged during Inter-Component Communication (ICC). The PendingIntent (PI) vulnerability enables a flexible software model, where the PendingIntent creator app can temporarily share its own permissions and identity with the PendingIntent receiving app. The PendingIntent vulnerability does not require approval from the device user or Android OS to share the permissions and identity with other apps. This is called a PI leak, which can lead to malware attacks such as privilege escalation and component hijacking attacks. This vulnerability in the symmetric behavior of an application without validating an app’s privileges dynamically leads to the asymmetric phenomena that can damage the robustness of an entire system. In this paper, we propose MULBER, a lightweight machine learning method for the detection of Android malware communications that enables a cybersecurity system to analyze multiple patterns and learn from them to help prevent similar attacks and respond to changing behavior. MULBER can help cybersecurity teams to be more proactive in preventing dynamic PI-based communication threats and responding to active attacks in real time. MULBER performs a static binary analysis on the APK file and gathers approximately 10,755 features, reducing it to 42 key features by grouping the permissions under the above-mentioned four categories. Finally, MULBER learns from these multivariate features using evolutionary feature selection and the Mahalanobis distance metric and classifies them as either benign or malware apps. In an evaluation of 22,638 malware samples from recent Android APK malware databases such as Drebin and CICMalDroid-2020, MULBER outperformed others by clustering applications based on the Mahalanobis distance metric and detected 95.69% of malware with few false alarms and the explanations provided for each detection revealed the relevant properties of the detected malware.

How Not to Handle Keys: Timing Attacks on FIDO Authenticator Privacy

This paper presents a timing attack on the FIDO2 (Fast IDentity Online) authentication protocol that allows attackers to link user accounts stored in vulnerable authenticators, a serious privacy concern. FIDO2 is a new standard specified by the FIDO industry alliance for secure token online authentication. It complements the W3C WebAuthn specification by providing means to use a USB token or other authenticator (which holds the secret authenticating material and implements FIDO protocols) as a second factor during the authentication process. From a cryptographic perspective, the protocol is a simple challenge-response where the elliptic curve digital signature algorithm is used to sign challenges. To protect the privacy of the user the token uses unique key pairs per service. To accommodate for small memory, tokens use various techniques that make use of a special parameter called a key handle sent by the service to the token with which the token can securely produce an authentication key (through generation or decryption). We identify and analyse a vulnerability in the way the processing of key handles is implemented that allows attackers to remotely link user accounts on multiple services. We show that for vulnerable authenticators there is a difference between the time it takes to process a key handle for a different service but correct authenticator, and for a different authenticator but correct service. This difference can be used to perform a timing attack allowing an adversary to link user’s accounts across services. We present several real world examples of adversaries that are in a position to execute our attack and can benefit from linking accounts. We found that two of the eight hardware authenticators we tested were vulnerable despite FIDO level 1 certification, indicating a not insignificant problem. This vulnerability cannot be easily mitigated on authenticators because, for security reasons, they usually do not allow firmware updates. In addition, we show that due to the way existing browsers implement the WebAuthn standard, the attack can be executed remotely. However, we discuss countermeasures that can be implemented by browser providers to mitigate the remote form of the attack.

Hybrid Red Deer Algorithm with Physical Unclonable Function for Security Enhancement in IoT-WSN

In this modern era, Wireless Sensor Network (WSNs) have turned out to be a more attractive option, because of the advancements in communication. If the network is insecure, an attacker can intercept messages and break the sensor nodes’ security; as well as duplicate the authentication codes to launch a variety of attacks. As a result, Physical Unclonable Functions (PUFs) with unpredictable features are encouraged to be used in the development of lightweight cryptographic protocols. This work introduced the Red Deer Algorithm (RDA) with PUF as a new mutual authentication system. When a sensor receives a challenge from the gateway, the inbuilt PUF generates a key and distributes it to the sensor by providing complete resilience against malicious attacks. PUF is resistant to node acquisition, cloning, and malicious attacks, as well as node physical security flaws. Key distribution is moving too quickly and the adversary won’t be able to conduct a harmful attack in time. Furthermore, PUF’s unclonability and unexpected qualities provide key uniqueness and two-way authentication for improving the security. When compared to existing Tunicate Swarm Grey Wolf optimization (TSGWO) and PUF-Based Mutual-Authenticated Key Distribution (PUF-MAKD), the proposed RDA-PUF demonstrated better results. The simulation results are obtained in terms of minimizing energy consumption as 0.7 J, end-to-end delay as 7 sec, packet delivery ratio of 97%, increasing network lifetime to 1330 sec, and improving secure connectivity to 1.211.

High-throughput block cipher implementations with SIMD

The development of new technologies has put forward higher requirements for the performance and security of block ciphers. To meet the needs of these situations, Bitslicing is one of the block cipher implementation techniques which can provide high efficiency and resistance to cache-timing attacks. The S-box is usually the most time-consuming component for Bitsliced implementations. In this paper, we present techniques to optimize the S-box implementation and derive the most compact representations available for Bitslicing. Then we show that the efficiency of linear layers can be further improved by introducing a state-of-the-art technique. As a result, our implementations have significant advantages over previous implementations. The throughputs of our AES and SM4 implementations achieve 27068Mbps and 30026Mbps on i5-11335G7, both exceeding the throughput of AES-NI on the same platform.

Frequency domain resilient consensus of multi-agent systems under IMP-based and non IMP-based attacks

This paper presents a frequency domain resilient consensus problem of time-delayed multi-agent systems (MASs) under attacks. An internal model principle-based attack, a kind of network attack, is firstly analyzed and modeled in frequency domain. Then, a Quasi-H∞ robust controller is designed for resilient consensus of the MAS with the help of an internal model controller. This scheme is different from the existing works in the sense that the attacker may inject an attacking signal on the node of an agent instead of a communication link. It is also novel because it can eliminate the impacts of attack without excluding compromised nodes and restricting the number of compromised nodes. The proposed control scheme is applied to a linear delayed MAS with single-input single-output agents and then transformed into multi-input and multi-output agents. An innovative approach is employed for the tuning of the proposed controller to track the performance of the MAS and its robustness. Some necessary and sufficient conditions are extracted using Padé approximation and graph theory to minimize the impacts of time delay and attacks simultaneously. The results are extended for resilient consensus of the MAS under non-internal model principle-based attack. The simulation results are presented to demonstrate the validity of the proposed control strategy.

Computer network security evaluation method based on improved attack graph

ABSTRACT The traditional security detection and defense methods are relatively backward, and there are many problems, such as high false alarm and missed alarm rate, and detection lag, which have been difficult to meet the requirements of computer security defense. In order to strengthen the active defense of computer network security and improve the classification and prediction performance of computer network security events, a computer network security risk assessment model based on mrmr Ig feature selection model and attack graph model is proposed. The mrmr Ig feature selection model is used to classify the characteristics of security events, and the hidden Markov chain model and attack graph model are used to predict the attack intention. The experimental results show that the classification accuracy rate of the model is 99.79%, the false alarm rate and the false alarm rate are 0.11% and 0.18%, respectively. The model can accurately predict attacks in real time, and can provide reference for timely taking targeted computer network security reinforcement and active defense measures.

Stealthy attacks and attack-resilient interval observers

Industrial control systems have been frequent targets of cyber attacks during the last decade. Adversaries can hinder the safe operation of these systems by tampering with their sensors and actuators, while ensuring that the monitoring systems are not able to detect such attacks in time. This paper presents methods to design and overcome stealthy attacks on linear time-invariant control systems that estimate their state using an interval observer, in the presence of unknown but bounded noise and perturbations. We analyze scenarios in which a malicious agent compromises the sensors and/or the actuators of the system with additive attack signals to steer the state estimate outside of the bounds provided by the interval observer. We first show that maximally disruptive attack sequences that remain undetected by a linear monitor can be computed recursively via linear programming. We then design an attack-resilient interval observer for the system’s state, identifying sufficient conditions on the sensor data for such an observer to be realizable. We propose a computational method to determine optimal observer gains using semi-definite programming and compute bounds for the unknown attack signal as well. In numerical simulations, we illustrate and compare the ability of such interval observers to still provide accurate estimates when under attack.

Efficient hardware realization and high radix implementation of modular multi exponential techniques for public key cryptography

Modular multi exponentiation (MME) is a critical operation in most of the public-key cryptosystems and verification functions. Two novel algorithms: HRMME and SFWHRMME, are proposed to evaluate the MME efficiently. The evaluation process of the proposed algorithm has an inbuilt confusion mechanism, which increases the security level. The side-channel attacks and timing attacks are counteracted by algorithmic means instead of using only hardware measures. The high-radix implementation is efficiently reducing the required number of clock cycles. The performance of the proposed algorithms is analyzed in terms of power, throughput and energy. The experimental results show that the proposed techniques HRMME and SFWHRMME increase the throughput in the order of 23.55% and 23.67%, respectively and consume less power in the order of 21.78% and 21.88%. Xilinx Vivado 21.2 on Virtex-7 evaluation boards and ICARUS Verilog simulation & synthesis tools are used for FPGA for hardware realization. The hardware compatibility of proposed algorithms has also been checked using Cadence for ASIC.

CBA-Detector: A Self-Feedback Detector Against Cache-Based Attacks

Cloud computing is convenient to provide adequate resources for tenants. However, since multiple tenants share the underlying hardware resources, malicious tenants can use the shared processor to launch cache-based attacks. Such attacks can help malicious tenants steal private data of other tenants bypassing isolation mechanisms provided by the system, resulting in information leakage. Moreover, Spectre and Meltdown vulnerabilities can even extract memory contents arbitrarily with the help of cache attacks. Therefore, cache-based attacks pose a serious threat to the security of cloud platforms. To defeat such attacks, many detection methods have been proposed. However, most methods induce high false positives because they completely rely on the hardware performance counters (HPCs) and detect attacks with static criteria. To solve this problem, this article proposes a self-feedback detector named CBA-Detector to detect cache-based attacks in real time. Specifically, CBA-Detector first uses machine learning technologies to create models for identifying suspicious programs with abnormal hardware behaviors, then analyzes suspicious programs from the instruction level to identify real attacks and provide feedback. Based on the feedback, the models can be updated to further improve their detection accuracy. As our experiments show, CBA-Detector can accurately identify cache-based attacks in real time and introduces a little overhead. Besides, the misjudgment rate decreases with the running time.

High-Speed Post-Quantum Cryptoprocessor Based on RISC-V Architecture for IoT

Public-key plays a significant role in today’s communication over the network. However, current state-of-the-art public-key encryption (PKE) schemes are too complex to be efficiently employed in resource-constrained devices. Moreover, they are vulnerable to quantum attacks and soon will not have the required security. In the last decade, lattice-based cryptography has been a progenitor platform of the post-quantum cryptography (PQC) due to its lower complexity, which makes it more suitable for Internet of Things applications. In this article, we propose an efficient implementation of the binary learning with errors over ring (Ring-BinLWE) on the reduced instruction set computer-five (RISC-V) platform. Our field-programmable gate array (FPGA) implementations improve the speed of scheme operations by more than 51% in terms of CPU cycles compared to previous work. The proposed hardware module has low complexity and only imposes around 6%–9% overhead to the original core. Moreover, it has constant-time operations and is resistant to timing attacks. Besides, a more reliable fault-resilient variant of the architecture with 1% area overhead is proposed. According to the application-specific integrated circuits (ASICs) implementations, the proposed architecture achieves at least 32%, 79%, and 50% lower power, energy, and area consumption compared to the previous work, respectively.

A Distributed Intrusion Detection System for AODV Network

Abstract: The Ad hoc On-Demand Distance Vector (AODV) routing protocol, designed for mobile ad hoc networks, offers quick adaptation to dynamic link conditions, low processing and memory overhead, and low network utilization. However, without keeping in mind the security issues in the protocol design, AODV is vulnerable to various kinds of attacks. This thesis analyzes some of the vulnerabilities, specifically discussing attacks against AODV that manipulate the routing messages. We propose a solution based on specification-based intrusion detection to detect attacks on AODV. Briefly, our approach involves the use of finite state machines for specifying correct AODV routing behavior and distributed network monitors for detecting run-time violation of the specifications. In addition, one additional field in the protocol message is proposed to enable the monitoring. We illustrate that our algorithm, which employs a tree data structure, can effectively detect most of the serious attacks in real time and with minimum overhead. Routing attacks will have distressing effects over the network and bequest a significant challenge once planning strong security mechanisms for vehicular communication. In this paper, we examine the effect and malicious activities of a number of the foremost common attacks and also mention some security schemes against some major attacks in VANET. The attacker's aim is only to modify the actual route or provides the false data about the route to the sender and also some attackers are only flooding unwanted packets to consume resources in available network. Various routing approaches are also mentioned in the paper because the routing of data is very important to deliver the traffic information to leading vehicles. It's advised that a number of the ways that to approach this made field of analysis issues in VANET might be to fastidiously design new secure routing protocols in which attacks are often rendered meaningless and because of the inherent constraints found in the network, there's a desire for light-weight and sturdy security mechanisms.

TrES: Tropical Encryption Scheme Based on Double Key Exchange

Shor’s quantum algorithm establishes a polynomial time attack on the discrete logarithm problem in any group. Recent announcements of progress in building quantum computers highlight the need for new concepts to create cryptosystems that are resistant to quantum attacks. In this paper, we present а new message encryption scheme. To enhance the security of the scheme, we suggest double key-exchange protocol (KEP). The first stage of the key exchange uses a matrix power function (MPF) in a tropical semiring. These functions are based on the action of a matrix semiring acting on some matrix set. MPFs can be considered as one-way functions because they are based on some generalized satisfiability problems that are potentially NP-complete. The obtained shared secret key at the first stage of the key exchange serves as an input for the second stage. The security of the second phase relies on the difficulty of the semiring action problem. In our protocol, we suggest using left or right action of the tropical semiring (which can be both min-plus and max-plus) on the group of commutative matrices (circulant matrices, in our case). The fact that the key-exchange protocol works in two phases contributes to its security, since an attacker needs to solve two difficult problems in order to break it. The main advantages of the presented protocol are: the increased efficiency and improved security.

Development of a model for detecting network traffic anomalies in distributed wireless ad hoc networks

Mobile ad hoc networks are one of the promising directions of the edge computing technology and they are used in various applications, in particular, in the development of intelligent transport systems. A feature of mobile ad hoc networks lies in the constantly changing dynamic network topology, as a result of which it is necessary to use reactive routing protocols when transmitting packets between nodes. Mobile ad hoc networks are vulnerable to cyber-attacks, so there is a need to develop measures to identify network threats and develop rules for responding to them based on machine learning models. The subject of this study is the development of a dynamic model for detecting network traffic anomalies in wireless distributed ad hoc networks. Within the framework of this study, methods and algorithms of data mining and machine learning were applied. The proposed approach to traffic monitoring in wireless distributed ad hoc networks consists in the implementation of two stages: initial traffic analysis to identify anomalous events and subsequent in-depth study of cybersecurity incidents to classify the type of attack. Within the framework of this approach, the corresponding models are constructed based on ensemble methods of machine learning. A comparative analysis and selection of the most efficient machine learning algorithms and their optimal hyperparameters has been carried out. In this paper, a formalization of the traffic anomaly detection model in distributed wireless ad hoc networks is carried out, the main quantitative metrics of network performance are identified, a generalized algorithm for detecting traffic anomalies in mobile ad hoc networks is presented, and an experimental study of the network segment simulation is carried out from the point of view of performance degradation during the implementation of various network attack scenarios. Network distributed denial of service attacks and cooperative blackhole attacks have the greatest negative impact on the performance of the mobile ad hoc network segment. In addition, the network simulation results were used to build a machine learning model to detect anomalies and classify types of attacks. The results of a comparative analysis of machine learning algorithms showed that the use of the LightGBM method is the most effective for detecting network traffic anomalies with an accuracy of 91 %, and for determining directly the type of attack being carried out with an accuracy of 90 %. The proposed approach for network anomalies detection through the use of trained traffic analysis models makes it possible to identify the considered types of attacks in due time. The future development direction of this research is the consideration of new scenarios for the emergence of network attacks and online additional training of the constructed identification models. The developed software tool for detecting network traffic anomalies in distributed mobile ad hoc networks can be used for any type of wireless ad hoc networks.

Prediction model of heart diseases based on hybrid model

Heart diseases are crucial cause of global death. Premature detection of heart diseases and uninterrupted analysis decreases the death rate. The detection of heart diseases using various machine learning algorithm is implemented for analysis of the heart attack in minimum time. Machine learning plays a very important role in the prevention of diseases and huge comprehension a health record. Recent trend in machine learning methodology being wield in contemporary evolution in different sector of internet of things. There is different algorithm in machine learning, which is analysis of heart diseases along with different accuracy. The purpose of this research to develop machine learning model to detect the heart diseases. In this a hybrid algorithm is proposed using combination of KNN and logistic regression algorithm. Furthermore, a protype is developed which consist of set of sensors to monitor the timely for prediction or analysis of heart attack.