Biblio

As a mature and open mobile operating system, Android runs on many IoT devices, which has led to Android-based IoT devices have become a hotbed of malware. Existing static detection methods for malware using artificial intelligence algorithms focus only on the java code layer when extracting API features, however there is a lot of malicious behavior involving native layer code. Thus, to make up for the neglect of the native code layer, we propose a heterogeneous information network-based Android malware detection method with cross-layer features. We first translate the semantic information of apps and API calls into the form of meta-paths, and construct the adjacency of apps based on API calls, then combine information from different meta-paths using multi-core learning. We implemented our method on the dataset from VirusShare and AndroZoo, and the experimental results show that the accuracy of our method is 93.4%, which is at least 2% higher than other related methods using heterogeneous information networks for malware detection.

The excess buffering of packets in network elements, also referred to as bufferbloat, results in high latency. Considering the requirements of traffic generated by video conferencing systems like Zoom, cloud rendered gaming platforms like Google Stadia, or even video streaming services such as Netflix, Amazon Prime and YouTube, timeliness of such traffic is important. Ensuring low latency to IP flows with a high throughput calls for the application of Active Queue Management (AQM) schemes. This introduces yet another problem as the co-existence of scalable and classic congestion controls leads to the starvation of classic TCP flows. Technologies such as Low Latency Low Loss Scalable Throughput (L4S) and the corresponding dual queue coupled AQM, DualPI2, provide a robust solution to these problems. However, their deployment on hardware targets such as programmable switches is quite challenging due to the complexity of algorithms and architectural constraints of switching ASICs. In this study, we provide proof of concept implementations of two AQMs that enable the co-existence of scalable and traditional TCP traffic, namely DualPI2 and the preceding single-queue PI2 AQM, on an Intel Tofino switching ASIC. Given the fixed operation of the switch’s traffic manager, we investigate to what extent it is possible to implement a fully RFC-compliant version of the two AQMs on the Tofino ASIC. The study shows that an appropriate split between control and data plane operations is required while we also exploit fixed functionality of the traffic manager to support such solutions.

This paper is concered with the nonlinear cyber physical system (CPS) with uncertain parameters under false data injection (FDI) attacks. The interval type-2 (IT2) fuzzy model is utilized to approximate the nonlinear system, then the nonlinear system can be represented as a convex combination of linear systems. To detect the FDI attacks, a novel robust fuzzy extended state observer with H∞ preformance is proposed, where the fuzzy rules are utilized to the observer to estimate the FDI attacks. Utilizing the observation of the FDI attacks, a security control scheme is proposed in this paper, in which a compensator is designed to offset the FDI attacks. Simulation examples are given to illustrate the effecitveness of the proposed security scheme.

One of the major concerns in the real-time monitoring systems in a smart grid is the Cyber security threat. The false data injection attack is emerging as a major form of attack in Cyber-Physical Systems (CPS). A False data Injection Attack (FDIA) can lead to severe issues like insufficient generation, physical damage to the grid, power flow imbalance as well as economical loss. The recent advancements in machine learning algorithms have helped solve the drawbacks of using classical detection techniques for such attacks. In this article, we propose to use Autoencoders (AE’s) as a novel Machine Learning approach to detect FDI attacks without any major modifications. The performance of the method is validated through the analysis of the simulation results. The algorithm achieves optimal accuracy owing to the unsupervised nature of the algorithm.

The data sharing is a helpful and financial assistance provided by CC. Information substance security also rises out of it since the information is moved to some cloud workers. To ensure the sensitive and important data; different procedures are utilized to improve access manage on collective information. Here strategies, Cipher text-policyattribute based encryption (CP-ABE) might create it very helpful and safe. The conventionalCP-ABE concentrates on information privacy only; whereas client's personal security protection is a significant problem as of now. CP-ABE byhidden access (HA) strategy makes sure information privacy and ensures that client's protection isn't exposed also. Nevertheless, the vast majority of the current plans are ineffectivein correspondence overhead and calculation cost. In addition, the vast majority of thismechanism takes no thought regardingabilityauthenticationor issue of security spillescapein abilityverificationstage. To handle the issues referenced over, a security protectsCP-ABE methodby proficient influenceauthenticationis presented in this manuscript. Furthermore, its privacy keys accomplish consistent size. In the meantime, the suggestedplan accomplishes the specific safetyin decisional n-BDHE issue and decisional direct presumption. The computational outcomes affirm the benefits of introduced method.

Java locking is an essential functionality and tool in the development of applications and systems, and this is mainly because several modules may run in a synchronized way inside an application and these modules need a good coordination manner in order for them to run properly and in order to make the whole application or system stable and normal. As such, this paper focuses on comparing various Java locking mechanisms in order to achieve a better understanding of how these locks work and how to conduct a proper locking mechanism. The comparison of locks is made according to CPU usage, memory consumption, and ease of implementation indicators, with the aim of providing guidance to developers in choosing locks for different scenarios. For example, if the Pessimistic Locks are used in any program execution environment, i.e., whenever a thread obtains resources, it needs to obtain the lock first, which can ensure a certain level of data security. However, it will bring great CPU overhead and reduce efficiency. Also, different locks have different memory consumption, and developers are sometimes faced with the need to choose locks rationally with limited memory, or they will cause a series of memory problems. In particular, the comparison of Java locks is able to lead to a systematic classification of these locks and can help improve the understanding of the taxonomy logic of the Java locks.

In today’s world, digital data are enormous due to technologies that advance data collection, storage, and analyses. As more data are shared or publicly available, privacy is of great concern. Having privacy means having control over your data. The first step towards privacy protection is to understand various aspects of privacy and have the ability to quantify them. Much work in structured data, however, has focused on approaches to transforming the original data into a more anonymous form (via generalization and suppression) while preserving the data integrity. Such anonymization techniques count data instances of each set of distinct attribute values of interest to signify the required anonymity to protect an individual’s identity or confidential data. While this serves the purpose, our research takes an alternative approach to provide quick privacy measures by way of anonymity especially when dealing with large-scale data. This paper presents a study of anonymity measures based on their relevant properties that impact privacy. Specifically, we identify three properties: uniformity, variety, and diversity, and formulate their measures. The paper provides illustrated examples to evaluate their validity and discusses the use of multi-aspects of anonymity and privacy measures.

Resilience of urban infrastructure to sudden, system-wide, potentially catastrophic events is a critical need across domains. The growing connectivity of infrastructure, including its cyber-physical components which can be controlled in real time, offers an attractive path towards rapid adaptation to adverse events and adjustment of system objectives. However, existing work in the field often offers disjoint approaches that respond to particular scenarios. On the other hand, abstract work on control of complex systems focuses on attempting to adapt to the changes in the system dynamics or environment, but without understanding that the system may simply not be able to perform its original task after an adverse event. To address this challenge, this programmatic paper proposes a vision for a new paradigm of infrastructure resilience. Such a framework treats infrastructure across domains through a unified theory of controlled dynamical systems, but remains cognizant of the lack of knowledge about the system following a widespread adverse event and aims to identify the system's fundamental limits. As a result, it will enable the infrastructure operator to assess and assure system performance following an adverse event, even if the exact nature of the event is not yet known. Building off ongoing work on assured resilience of control systems, in this paper we identify promising early results, challenges that motivate the development of resilience theory for infrastructure system, and possible paths forward for the proposed effort.

This paper deals with the implementation of robust control, based on the finite time Lyapunov stability theory, to solve the trajectory tracking problem of an unconventional quadrotor with rotating arms (also known as foldable drone). First, the model of this Unmanned Aerial Vehicle (UAV) taking into consideration the variation of the inertia, the Center of Gravity (CoG) and the control matrix is presented. The theoretical foundations of backstepping control enhanced by a Super-Twisting (ST) algorithm are then discussed. Numerical simulations are performed to demonstrate the effectiveness of the proposed control strategy. Finally, a qualitative and quantitative comparative study is made between the proposed controller and the classical backstepping controller. Overall, the results obtained show that the proposed control approach provides better performance in terms of accuracy and resilience.

In order to solve the problem that the traditional “centralized” access control technology can no longer guarantee the security of access control in the current Internet of Things (IoT)environment, a dynamic access control game mechanism based on trust is proposed. According to the reliability parameters of the recommended information obtained by the two elements of interaction time and the number of interactions, the user's trust value is dynamically calculated, and the user is activated and authorized to the role through the trust level corresponding to the trust value. The trust value and dynamic adjustment factor are introduced into the income function to carry out game analysis to avoid malicious access behavior of users. The hybrid Nash equilibrium strategy of both sides of the transaction realizes the access decision-making work in the IoT environment. Experimental results show that the game mechanism proposed in this paper has a certain restraining effect on malicious nodes and can play a certain incentive role in the legitimate access behavior of IoT users.

Cyber-Physical Systems (CPSs), a class of complex intelligent systems, are considered the backbone of Industry 4.0. They aim to achieve large-scale, networked control of dynamical systems and processes such as electricity and gas distribution networks and deliver pervasive information services by combining state-of-the-art computing, communication, and control technologies. However, CPSs are often highly nonlinear and uncertain, and their intrinsic reliance on open communication platforms increases their vulnerability to security threats, which entails additional challenges to conventional control design approaches. Indeed, sensor measurements and control command signals, whose integrity plays a critical role in correct controller design, may be interrupted or falsely modified when broadcasted on wireless communication channels due to cyber attacks. This can have a catastrophic impact on CPS performance. In this paper, we first conduct a thorough analysis of recently developed secure and resilient control approaches leveraging the solid foundations of adaptive control theory to achieve security and resilience in networked CPSs against sensor and actuator attacks. Then, we discuss the limitations of current adaptive control strategies and present several future research directions in this field.

Over the past two decades, several forms of non-intrusive technology have been deployed in cooperation with medical specialists in order to aid patients diagnosed with some form of mental, cognitive or psychological condition. Along with the availability and accessibility to applications offered by mobile devices, as well as the advancements in the field of Artificial Intelligence applications and Natural Language Processing, Conversational Agents have been developed with the objective of aiding medical specialists detecting those conditions in their early stages and monitoring their symptoms and effects on the cognitive state of the patient, as well as supporting the patient in their effort of mitigating those symptoms. Coupled with the recent advances in the the scientific field of machine and deep learning, we aim to explore the grade of applicability of such technologies into cognitive health support Conversational Agents, and their impact on the acceptability of such applications bytheir end users. Therefore, we conduct a systematic literature review, following a transparent and thorough process in order to search and analyze the bibliography of the past five years, focused on the implementation of Conversational Agents, supported by Artificial Intelligence technologies and in service of patients diagnosed with Mild Cognitive Impairment and its variants.

Online information security labs intended for training and facilitating hands-on learning for distance students at master’s level are not easy to develop and administer. This research focuses on analyzing the results of a DSR project for design, development, and implementation of an InfoSec lab. This research work contributes to the existing research by putting forth an initial outline of a generalized model for design theory for InfoSec labs aimed at hands-on education of students in the field of information security. The anatomy of design theory framework is used to analyze the necessary components of the anticipated design theory for InfoSec labs in future.

Mid-infrared spectroscopic imaging (MIRSI) is an emerging class of label-free, biochemically quantitative technologies targeting digital histopathology. Conventional histopathology relies on chemical stains that alter tissue color. This approach is qualitative, often making histopathologic examination subjective and difficult to quantify. MIRSI addresses these challenges through quantitative and repeatable imaging that leverages native molecular contrast. Fourier transform infrared (FTIR) imaging, the best-known MIRSI technology, has two challenges that have hindered its widespread adoption: data collection speed and spatial resolution. Recent technological breakthroughs, such as photothermal MIRSI, provide an order of magnitude improvement in spatial resolution. However, this comes at the cost of acquisition speed, which is impractical for clinical tissue samples. This paper introduces an adaptive compressive sampling technique to reduce hyperspectral data acquisition time by an order of magnitude by leveraging spectral and spatial sparsity. This method identifies the most informative spatial and spectral features, integrates a fast tensor completion algorithm to reconstruct megapixel-scale images, and demonstrates speed advantages over FTIR imaging while providing spatial resolutions comparable to new photothermal approaches.

Model checking is one of the most commonly used technique in formal verification. However, the exponential scale state space renders exhaustive state enumeration inefficient even for a moderate System on Chip (SoC) design. In this paper, we propose a method that leverages symbolic execution to accelerate state space search and pinpoint security vulnerabilities. We automatically convert the hardware design to functionally equivalent C++ code and utilize the KLEE symbolic execution engine to perform state exploration through heuristic search. To reduce the search space, we symbolically represent essential input signals while making non-critical inputs concrete. Experiment results have demonstrated that our method can precisely identify security vulnerabilities at significantly lower computation cost.

IoT has been an efficient technology for interconnecting different physical objects with the internet. Several cyber-attacks have resulted in compromise in security. Blockchain distributed ledger provide immutability that can answer IoT security concerns. The paper aims at highlighting the challenges & problems currently associated with IoT implementation in real world and how these problems can be minimized by implementing Blockchain based solutions and smart contracts. Blockchain helps in creation of new highly robust IoT known as Blockchain of Things(BCoT). We will also examine presently employed projects working with integrating Blockchain & IoT together for creating desired solutions. We will also try to understand challenges & roadblocks preventing the further implementation of both technologies merger.

With the rapid development of blockchain technology, it provides a new technical solution for secure storage of data and trusted computing. However, in the actual application of data traceability, blockchain technology has an obvious disadvantage: the large amount of data stored in the blockchain system will lead to a long response time for users to query data. Higher query delay severely restricts the development of block chain technology in the traceability system. In order to solve this problem, we propose an efficient, secure and low storage overhead blockchain query scheme. Specifically, we design an index structure independent of Merkle tree to support efficient intra-block query, and create new fields in the block header to optimize inter-block query. Compared with several existing schemes, our scheme ensures the security of data. Finally, we simulate and evaluate our proposed scheme. The results show that the proposed scheme has better execution efficiency while reducing additional overhead.

In recent decennium, hardware security has gained a lot of attention due to different types of attacks being launched, such as IP theft, reverse engineering, counterfeiting, etc. The critical testing infrastructure incorporated into ICs is very popular among attackers to mount side-channel attacks. The IEEE standard 1687 (IJTAG) is one such testing infrastructure that is the focus of attackers these days. To secure access to the IJTAG network, various techniques based on Locking SIB (LSIB) have been proposed. One such very effective technique makes use of Security Linear Feedback Shift Register (SLFSR) along with LSIB. The SLFSR obfuscates the scan chain information from the attacker and hence makes the brute-force attack against LSIB ineffective.In this work, it is shown that the SLFSR based Locking SIB is vulnerable to side-channel attacks. A power analysis attack along with known-plaintext attack is used to determine the IJTAG network structure. First, the known-plaintext attack is used to retrieve the SLFSR design information. This information is further used along with power analysis attack to determine the exact length of the scan chain which in turn breaks the whole security scheme. Further, a countermeasure is proposed to prevent the aforementioned hybrid attack.

In this work, we discuss data breaches based on the “2012 SocialArks data breach” case study. Data leakage refers to the security violations of unauthorized individuals copying, transmitting, viewing, stealing, or using sensitive, protected, or confidential data. Data leakage is becoming more and more serious, for those traditional information security protection methods like anti-virus software, intrusion detection, and firewalls have been becoming more and more challenging to deal with independently. Nevertheless, fortunately, new IT technologies are rapidly changing and challenging traditional security laws and provide new opportunities to develop the information security market. The SocialArks data breach was caused by a misconfiguration of ElasticSearch Database owned by SocialArks, owned by “Tencent.” The attack methodology is classic, and five common Elasticsearch mistakes discussed the possibilities of those leakages. The defense solution focuses on how to optimize the Elasticsearch server. Furthermore, the ElasticSearch database’s open-source identity also causes many ethical problems, which means that anyone can download and install it for free, and they can install it almost anywhere. Some companies download it and install it on their internal servers, while others download and install it in the cloud (on any provider they want). There are also cloud service companies that provide hosted versions of Elasticsearch, which means they host and manage Elasticsearch clusters for their customers, such as Company Tencent.

Recently, a mechanism that randomly shuffles the data sent and allows securing the communication without the need to encrypt all the information has been proposed. This proposal is ideal for IoT systems with low computational capacity. In this work, we analyze the strength of this proposal from a brute-force attack approach to obtain the original message without knowledge of the applied disordering. It is demonstrated that for a set of 10x10 16-bit data, the processing time and the required memory are unfeasible with current technology. Therefore, it is safe.

Currently, the rapid development of digital communication and multimedia has made security an increasingly prominent issue of communicating, storing, and transmitting digital data such as images, audio, and video. Encryption techniques such as chaotic map based encryption can ensure high levels of security of data and have been used in many fields including medical science, military, and geographic satellite imagery. As a result, ensuring image data confidentiality, integrity, security, privacy, and authenticity while transferring and storing images over an unsecured network like the internet has become a high concern. There have been many encryption technologies proposed in recent years. This paper begins with a summary of cryptography and image encryption basics, followed by a discussion of different kinds of chaotic image encryption techniques and a literature review for each form of encryption. Finally, by examining the behaviour of numerous existing chaotic based image encryption algorithms, this paper hopes to build new chaotic based image encryption strategies in the future.

In this cyber era, the number of cybercrime problems grows significantly, impacting network communication security. Some factors have been identified, such as malware. It is a malicious code attack that is harmful. On the other hand, a botnet can exploit malware to threaten whole computer networks. Therefore, it needs to be handled appropriately. Several botnet activity detection models have been developed using a classification approach in previous studies. However, it has not been analyzed about selecting features to be used in the learning process of the classification algorithm. In fact, the number and selection of features implemented can affect the detection accuracy of the classification algorithm. This paper proposes an analysis technique for determining the number and selection of features developed based on previous research. It aims to obtain the analysis of using features. The experiment has been conducted using several classification algorithms, namely Decision tree, k-NN, Naïve Bayes, Random Forest, and Support Vector Machine (SVM). The results show that taking a certain number of features increases the detection accuracy. Compared with previous studies, the results obtained show that the average detection accuracy of 98.34% using four features has the highest value from the previous study, 97.46% using 11 features. These results indicate that the selection of the correct number and features affects the performance of the botnet detection model.

The internet has developed and transformed the world dramatically in recent years, which has resulted in several cyberattacks. Cybersecurity is one of society’s most serious challenge, costing millions of dollars every year. The research presented here will look into this area, focusing on malware that can establish botnets, and in particular, detecting connections made by infected workstations connecting with the attacker’s machine. In recent years, the frequency of network security incidents has risen dramatically. Botnets have previously been widely used by attackers to carry out a variety of malicious activities, such as compromising machines to monitor their activities by installing a keylogger or sniffing traffic, launching Distributed Denial of Service (DDOS) attacks, stealing the identity of the machine or credentials, and even exfiltrating data from the user’s computer. Botnet detection is still a work in progress because no one approach exists that can detect a botnet’s whole ecosystem. A detailed analysis of a botnet, discuss numerous parameter’s result of detection methods related to botnet attacks, as well as existing work of botnet identification in field of machine learning are discuss here. This paper focuses on the comparative analysis of various classifier based on design of botnet detection technique which are able to detect P2P botnet using machine learning classifier.

CAPTCHAs are designed to prevent malicious bot programs from abusing websites. Most online service providers deploy audio CAPTCHAs as an alternative to text and image CAPTCHAs for visually impaired users. However, prior research investigating the security of audio CAPTCHAs found them highly vulnerable to automated attacks using Automatic Speech Recognition (ASR) systems. To improve the robustness of audio CAPTCHAs against automated abuses, we present the design and implementation of an audio adversarial CAPTCHA (aaeCAPTCHA) system in this paper. The aaeCAPTCHA system exploits audio adversarial examples as CAPTCHAs to prevent the ASR systems from automatically solving them. Furthermore, we conducted a rigorous security evaluation of our new audio CAPTCHA design against five state-of-the-art DNN-based ASR systems and three commercial Speech-to-Text (STT) services. Our experimental evaluations demonstrate that aaeCAPTCHA is highly secure against these speech recognition technologies, even when the attacker has complete knowledge of the current attacks against audio adversarial examples. We also conducted a usability evaluation of the proof-of-concept implementation of the aaeCAPTCHA scheme. Our results show that it achieves high robustness at a moderate usability cost compared to normal audio CAPTCHAs. Finally, our extensive analysis highlights that aaeCAPTCHA can significantly enhance the security and robustness of traditional audio CAPTCHA systems while maintaining similar usability.

Advanced Encryption Standard (AES) algorithm plays an important role in a data security application. In general S-box module in AES will give maximum confusion and diffusion measures during AES encryption and cause significant path delay overhead. In most cases, either L UTs or embedded memories are used for S- box computations which are vulnerable to attacks that pose a serious risk to real-world applications. In this paper, implementation of the composite field arithmetic-based Sub-bytes and inverse Sub-bytes operations in AES is done. The proposed work includes an efficient multiple round AES cryptosystem with higher-order transformation and composite field s-box formulation with some possible inner stage pipelining schemes which can be used for throughput rate enhancement along with path delay optimization. Finally, input biometric-driven key generation schemes are used for formulating the cipher key dynamically, which provides a higher degree of security for the computing devices.