Biblio

This Cognitive radio networks are vulnerable to specific intrusions due to the unique cognitive characteristics of these networks. This DoS attacks are known as the Primary User Emulation Attack and the Spectrum Sensing Data Falsification. If the intruder behavior is not statistically identical to the behavior of the primary users, intrusion detection techniques based on observing the energy of the received signals can be used. Both machine learning-based intrusion detection and sequential statistical analysis can be effectively applied. However, in some cases, statistical sequential analysis has some advantages in dealing with such challenges. This paper discusses aspects of using statistical sequential analysis methods to detect attacks in Cognitive radio networks.

Cognitive radio (CR) networks are an emerging and promising technology to improve the utilization of vacant bands. In CR networks, security is a very noteworthy domain. Two threatening attacks are primary user emulation (PUE) and spectrum sensing data falsification (SSDF). A PUE attacker mimics the primary user signals to deceive the legitimate secondary users. The SSDF attacker falsifies its observations to misguide the fusion center to make a wrong decision about the status of the primary user. In this paper, we propose a scheme based on clustering the secondary users to counter SSDF attacks. Our focus is on detecting and classifying each cluster as reliable or unreliable. We introduce two different methods using an artificial neural network (ANN) for both methods and five more classifiers such as support vector machine (SVM), random forest (RF), K-nearest neighbors (KNN), logistic regression (LR), and decision tree (DR) for the second one to achieve this goal. Moreover, we consider deterministic and stochastic scenarios with white Gaussian noise (WGN) for attack strategy. Results demonstrate that our method outperforms a recently suggested scheme.

Emerging safety-critical systems require high-performance data-parallel architectures and, problematically, ones that can guarantee tight and safe worst-case execution times. Given the complexity of existing architectures like GPUs, it is unlikely that sufficiently accurate models and algorithms for timing analysis will emerge in the foreseeable future. This motivates our work on Sim-D, a clean-slate approach to designing a real-time data-parallel architecture. Sim-D enforces a predictable execution model by isolating compute- and access resources in hardware. The DRAM controller uninterruptedly transfers tiles of data, requested by entire work-groups. This permits work-groups to be executed as a sequence of deterministic access- and compute phases, scheduling phases from up to two work-groups in parallel. Evaluation using a cycle-accurate timing model shows that Sim-D can achieve performance on par with an embedded-grade NVIDIA TK1 GPU under two conditions: applications refrain from using indirect DRAM transfers into large buffers, and Sim-D's scratchpads provide sufficient bandwidth. Sim-D's design facilitates derivation of safe WCET bounds that are tight within 12.7 percent on average, at an additional average performance penalty of \textbackslashsim∼9.2 percent caused by scheduling restrictions on phases.

Key challenges faced in the Internet today can be enumerated as follows: (1) complex route discovery mechanisms (2) latency and instability during link or device failure recovery (3) inadequacy in extending routing and addressing to limited domains, (4) complex interworking of multiple routing protocols at border routers. Routing table sizes increase with increasing number of networks indicating a scalability issue. One approach to address this spiraling complexity and performance challenges is to start fresh and re-think Internet routing and addressing. The Expedited Internet Bypass protocol (EIBP) is such a clean slate approach. In the interim, EIBP works in parallel with IP and has no dependency on layer 3 protocols. We demonstrated EIBP for routing and forwarding in an Autonomous system (AS) in our earlier work. In this article, we demonstrate EIBP for inter-AS routing. We compare EIBP's inter-AS operations and performance to Open Shortest Path First (OSPF) and Border Gateway Protocol (BGP) deployed in an intra-AS, inter-AS communications scenario with two AS.

Audit trails are critical components in enterprise business applications, typically used for storing, tracking, and auditing data. Entities in the audit trail applications have weak trust boundaries, which expose them to various security risks and attacks. To harden the security and develop secure by design applications, blockchain technology has been recently introduced in the audit trails. Blockchains take a consensus-driven clean slate approach to equip audit trails with secure and transparent data processing, without a trusted intermediary. On a downside, blockchains significantly increase the space-time complexity of the audit trails, leading to high storage costs and low transaction throughput. In this article, we introduce BlockTrail, a novel blockchain architecture that fragments the legacy blockchain systems into layers of codependent hierarchies, thereby reducing the space-time complexity and increasing the throughput. BlockTrail is prototyped on the “practical Byzantine fault tolerance” protocol with a custom-built blockchain. Experiments with BlockTrail show that compared to the conventional schemes, BlockTrail is secure and efficient, with low storage footprint.

Browsers are one of the most widely used types of software around the world. This prevalence makes browsers a prime target for cyberattacks. To mitigate these threats, users can practice safe browsing habits and take advantage of the security features available to browsers. These protections, however, could be severely crippled if the browser itself were malicious. Presented in this paper is the concept of the evil-twin browser (ETB), a clone of a legitimate browser that looks and behaves identically to the original browser, but discreetly performs other tasks that harm a user's security. To better understand the concept of the evil-twin browser, a prototype ETB named ChroNe was developed. The creation and installation process of ChroN e is discussed in this paper. This paper also explores the motivation behind creating such a browser, examines existing relevant work, inspects the open-source codebase Chromium that assisted in ChroNe's development, and discusses relevant topics like ways to deliver an ETB, the capabilities of an ETB, and possible ways to defend against ETBs.

Structured Query Language Injection (SQLi) is a client-side application vulnerability that allows attackers to inject malicious SQL queries with harmful intents, including stealing sensitive information, bypassing authentication, and even executing illegal operations to cause more catastrophic damage to users on the web application. According to OWASP, the top 10 harmful attacks against web applications are SQL Injection attacks. Moreover, based on data reports from the UK's National Fraud Authority, SQL Injection is responsible for 97% of data exposures. Therefore, in order to prevent the SQL Injection attack, detection SQLi system is essential. The contribution of this research is securing web applications by developing a browser extension for Google Chrome using Long Short-Term Memory (LSTM), which is a unique kind of RNN algorithm capable of learning long-term dependencies like SQL Injection attacks. The results of the model will be deployed in static analysis in a browser extension, and the LSTM algorithm will learn to identify the URL that has to be injected into Damn Vulnerable Web Application (DVWA) as a sample-tested web application. Experimental results show that the proposed SQLi detection model based on the LSTM algorithm achieves an accuracy rate of 99.97%, which means that a reliable client-side can effectively detect whether the URL being accessed contains a SQLi attack or not.

Blockchain is a relatively new technology, a distributed database used for sharing between nodes of computer networks. A blockchain stores all information in automated digital format as a database. Blockchain innovation ensures the accuracy and security of the data record and generates trust without the need for a trusted third party. The objectives of this paper are to determine the security risk of the blockchain systems, analyze the vulnerabilities exploited on the blockchain, and identify recent security challenges that the blockchain faces. This review paper presents some of the previous studies of the security threats that blockchain faces and reviews the security enhancement solutions for blockchain vulnerabilities. There are some studies on blockchain security issues, but there is no systematic examination of the problem, despite the blockchain system’s security threats. An observational research methodology was used in this research. Through this methodology, many research related to blockchain threats and vulnerabilities obtained. The outcomes of this research are to Identify the most important security threats faced by the blockchain and consideration of security recently vulnerabilities. Processes and methods for dealing with security concerns are examined. Intelligent corporate security academic challenges and limitations are covered throughout this review. The goal of this review is to serve as a platform as well as a reference point for future work on blockchain-based security.

The development of IoT has penetrated various sectors. The development of IoT devices continues to increase and is predicted to reach 75 billion by 2025. However, the development of IoT devices is not followed by security developments. Therefore, IoT devices can become gateways for cyber attacks, including brute force and sniffing attacks. Authentication mechanisms can be used to ward off attacks. However, the implementation of authentication mechanisms on IoT devices is challenging. IoT devices are dominated by constraint devices that have limited computing. Thus, conventional authentication mechanisms are not suitable for use. Two-factor authentication using RFID and fingerprint can be a solution in providing an authentication mechanism. Previous studies have proposed a two-factor authentication mechanism using RFID and fingerprint. However, previous research did not pay attention to message exchange security issues and did not provide mutual authentication. This research proposes a secure mutual authentication protocol using two-factor RFID and fingerprint using MQTT protocol. Two processes support the authentication process: the registration process and authentication. The proposed protocol is tested based on biometric security by measuring the false acceptance rate (FAR) and false rejection rate (FRR) on the fingerprint, measuring brute force attacks, and measuring sniffing attacks. The test results obtained the most optimal FAR and FRR at the 80% threshold. Then the equal error rate (ERR) on FAR and FRR is around 59.5%. Then, testing brute force and sniffing attacks found that the proposed protocol is resistant to both attacks.

The consensus-based frequency control relying on a communication system is used to restore the frequency deviations introduced by the primary droop control in an islanded AC microgrid, a typical cyber-physical power system(CPPS). This paper firstly studies the performance of the CPPS under two types of Distributed Denial of Service (DDoS ) attacks, finds that the intelligent attacks may cause more damage than the brute force attacks, and analyzes some potential defense strategies of the CPPS from two points of view. Some simulation results are also given to show the performance of both the physical and cyber system of the CPPS under different operation conditions.

A brute force is a Hacking methodology used to decrypt login passwords, keys and credentials. Hacks that exploit vulnerabilities in packages are rare, whereas Brute Force attacks aim to be the simplest, cheapest, and most straightforward approach to access a website. Using Splunk to analyse massive amounts of data could be very beneficial. The application enables to capture, search, and analyse log information in real-time. By analysing logs as well as many different sources of system information, security events can be uncovered. A log file, which details the events that have occurred in the environment of the application and the server on which they run, is a valuable piece of information. Identifying the attacks against these systems is possible by analysing and correlating this information. Massive amounts of ambiguous and amorphous information can be analysed with its superior resolution. The paper includes instructions on setting up a Splunk server and routing information there from multiple sources. Practical search examples and pre-built add-on applications are provided. Splunk is a powerful tool that allows users to explore big data with greater ease. Seizure can be tracked in near real-time and can be searched through logs. A short amount of time can be spent on analysing big data using map-reduce technology. Briefly, it helps to analyse unstructured log data to better understand how the applications operate. With Splunk, client can detect patterns in the data through a powerful query language. It is easy to set up alerts and warnings based on the queries, which will help alert client about an ongoing (suspected) activity and generate a notification in real-time.

One of the fifth generation’s most promising solutions for addressing the network system capacity issue is the ultra-dense network. However, a new problem arises because the user equipment secure access is made up of access points that are independent, transitory, and dynamic. The APs are independent and equal in this. It is possible to think of it as a decentralized access network. The access point’s coverage is less than the standard base stations. The user equipment will interface with access points more frequently as it moves, which is a problem. The current 4G Authentication and Key Agreement method, however, is unable to meet this need for quick and frequent authentication. This study means to research how blockchain innovation is being utilized in production network the executives, as well as its forthcoming purposes and arising patterns. To more readily comprehend the direction of important exploration and illuminate the benefits, issues, and difficulties in the blockchain-production network worldview, a writing overview and a logical evaluation of the current examination on blockchain-based supply chains were finished. Multifaceted verification strategies have as of late been utilized as possible guards against blockchain attacks. To further develop execution, scatter administration, and mechanize processes, inventory network tasks might be upset utilizing blockchain innovation

The robustness of supply chain networks (SCNs) against sequential topology attacks is significant for maintaining firm relationships and activities. Although SCNs have experienced many emergencies demonstrating that mixed failures exacerbate the impact of cascading failures, existing studies of sequential attacks rarely consider the influence of mixed failure modes on cascading failures. In this paper, a reinforcement learning (RL)-based sequential attack strategy is applied to SCNs with cascading failures that consider mixed failure modes. To solve the large state space search problem in SCNs, a deep Q-network (DQN) optimization framework combining deep neural networks (DNNs) and RL is proposed to extract features of state space. Then, it is compared with the traditional random-based, degree-based, and load-based sequential attack strategies. Simulation results on Barabasi-Albert (BA), Erdos-Renyi (ER), and Watts-Strogatz (WS) networks show that the proposed RL-based sequential attack strategy outperforms three existing sequential attack strategies. It can trigger cascading failures with greater influence. This work provides insights for effectively reducing failure propagation and improving the robustness of SCNs.

The ever-evolving capabilities of cyber attackers force security administrators to focus on the early identification of emerging threats. Targeted cyber attacks usually consist of several phases, from initial reconnaissance of the network environment to final impact on objectives. This paper investigates the identification of multi-step cyber threat scenarios using kill chain and attack graphs. Kill chain and attack graphs are threat modeling concepts that enable determining weak security defense points. We propose a novel kill chain attack graph that merges kill chain and attack graphs together. This approach determines possible chains of attacker’s actions and their materialization within the protected network. The graph generation uses a categorization of threats according to violated security properties. The graph allows determining the kill chain phase the administrator should focus on and applicable countermeasures to mitigate possible cyber threats. We implemented the proposed approach for a predefined range of cyber threats, especially vulnerability exploitation and network threats. The approach was validated on a real-world use case. Publicly available implementation contains a proof-of-concept kill chain attack graph generator.

Software supply chain attacks occur during the processes of producing software is compromised, resulting in vulnerabilities that target downstream customers. While the number of successful exploits is limited, the impact of these attacks is significant. Despite increased awareness and research into software supply chain attacks, there is limited information available on mitigating or architecting for these risks, and existing information is focused on singular and independent elements of the supply chain. In this paper, we extensively review software supply chain security using software development tools and infrastructure. We investigate the path that attackers find is least resistant followed by adapting and finding the next best way to complete an attack. We also provide a thorough discussion on how common software supply chain attacks can be prevented, preventing malicious hackers from gaining access to an organization's development tools and infrastructure including the development environment. We considered various SSC attacks on stolen code-sign certificates by malicious attackers and prevented unnoticed malware from passing by security scanners. We are aiming to extend our research to contribute to preventing software supply chain attacks by proposing novel techniques and frameworks.

With the rapid development of information technology, hacker invasion, Internet fraud and privacy disclosure and other events frequently occur, therefore information security issues become the focus of attention. Protecting the secure transmission of information has become a hot topic in today's research. As the carrier of information, image has the characteristics of vivid image and large amount of information. It has become an indispensable part of people's communication. In this paper, we proposed the key simulation analysis research based on M-J set. The research uses a complex iterative mapping to construct M set. On the basis of the constructed M set, the constructed Julia set is used to form the encryption key. The experimental results show that the generalized M-set has the characteristics of chaotic characteristic and initial value sensitivity, and the complex mapping greatly exaggerates the key space. The research on the key space based on the generalized M-J set is helpful to improve the effect of image encryption.

We present a novel chaotic laser coding technology of alternate variable secret-key (AVSK) for optics secure communication using alternate variable orbits (AVOs) method. We define the principle of chaotic AVSK encoding and decoding, and introduce a chaotic AVSK communication platform and its coding scheme. And then the chaotic AVSK coding technology be successfully achieved in encrypted optics communications while the presented AVO function, as AVSK, is adjusting real-time chaotic phase space trajectory, where the AVO function and AVSK according to our needs can be immediately variable and adjustable. The coding system characterizes AVSK of emitters. And another combined AVSK coding be discussed. So the system's security enhances obviously because it increases greatly the difficulty for intruders to decipher the information from the carrier. AVSK scheme has certain reference value for the research of chaotic laser secure communication and laser network synchronization.

To protect the security of video information use encryption technology to be effective means. In practical applications, the structural complexity and real-time characteristics of video information make the encryption effect of some commonly used algorithms have some shortcomings. According to the characteristics of video, to design practical encryption algorithm is necessary. This paper proposed a novel scheme of chaotic image encryption, which is based on scrambling and diffusion structure. Firstly, the breadth first search method is used to scramble the pixel position in the original image, and then the pseudo-random sequence generated by the time-varying bilateral chaotic symbol system is used to transform each pixel of the scrambled image ratio by ratio or encryption. In the simulation experiment and analysis, the performance of the encrypted image message entropy displays that the new chaotic image encryption scheme is effective.

Today, social communication through the Internet has become more popular and has become a crucial part of our daily life. Naturally, sending and receiving various data through the Internet has also grown a lot. Keeping important data secure in transit has become a challenge for individuals and even organizations. Therefore, the trinity of confidentiality, integrity, and availability will be essential, and encryption will definitely be one of the best solutions to this problem. Of course, for image data, it will not be possible to use conventional encryption methods for various reasons, such as the redundancy of image data, the strong correlation of adj acent pixels, and the large volume of image data. Therefore, special methods were developed for image encryption. Among the prevalent methods for image encryption is the use of DNA sequences as well as chaos signals. In this paper, a cycling 3D chaotic map and DNA sequences are used to present a new method for color image encryption. Several experimental analyses were performed on the proposed method, and the results proved that the presented method is secure and efficient.

E-health, smart health and telemedicine are examples of sophisticated healthcare systems. For end-to-end communication, these systems rely on digital medical information. Although this digitizing saves much time, it is open source. As a result, hackers could potentially manipulate the digital medical image as it is being transmitted. It is harder to diagnose an actual disease from a modified digital medical image in medical diagnostics. As a result, ensuring the security and confidentiality of clinical images, as well as reducing the computing time of encryption algorithms, appear to be critical problems for research groups. Conventional approaches are insufficient to ensure high-level medical image security. So this review paper focuses on depicting advanced methods like DNA cryptography and Chaotic Map as advanced techniques that could potentially help in encrypting the digital image at an effective level. This review acknowledges the key accomplishments expressed in the encrypting measures and their success indicators of qualitative and quantitative measurement. This research study also explores the key findings and reasons for finding the lessons learned as a roadmap for impending findings.

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 spread of Internet of Things (IoT) devices in our homes, healthcare, industries etc. are more easily infiltrated than desktop computers have resulted in a surge in botnet attacks based on IoT devices, which may jeopardize the IoT security. Hence, there is a need to detect these attacks and mitigate the damage. Existing systems rely on supervised learning-based intrusion detection methods, which require a large labelled data set to achieve high accuracy. Botnets are onerous to detect because of stealthy command & control protocols and large amount of network traffic and hence obtaining a large labelled data set is also difficult. Due to unlabeled Network traffic, the supervised classification techniques may not be used directly to sort out the botnet that is responsible for the attack. To overcome this limitation, a semi-supervised Deep Learning (DL) approach is proposed which uses Semi-supervised GAN (SGAN) for IoT botnet detection on N-BaIoT dataset which contains "Bashlite" and "Mirai" attacks along with their sub attacks. The results have been compared with the state-of-the-art supervised solutions and found efficient in terms of better accuracy which is 99.89% in binary classification and 59% in multi classification on larger dataset, faster and reliable model for IoT Botnet detection.

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.

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is a widely used technology to distinguish real users and automated users such as bots. However, the advance of AI technologies weakens many CAPTCHA tests and can induce security concerns. In this paper, we propose a user-friendly text-based CAPTCHA generation method named Robust Text CAPTCHA (RTC). At the first stage, the foregrounds and backgrounds are constructed with font and background images respectively sampled from font and image libraries, and they are then synthesized into identifiable pseudo adversarial CAPTCHAs. At the second stage, we utilize a highly transferable adversarial attack designed for text CAPTCHAs to better obstruct CAPTCHA solvers. Our experiments cover comprehensive models including shallow models such as KNN, SVM and random forest, as well as various deep neural networks and OCR models. Experiments show that our CAPTCHAs have a failure rate lower than one millionth in general and high usability. They are also robust against various defensive techniques that attackers may employ, including adversarially trained CAPTCHA solvers and solvers trained with collected RTCs using manual annotation. Codes available at https://github.com/RulinShao/RTC.

The high maneuverability of the unmanned aerial vehicle (UAV), facilitating fast and flexible deployment of communication infrastructures, brings potentially valuable opportunities to the future wireless communication industry. Nevertheless, UAV communication networks are faced with severe security challenges since air to ground (A2G) communications are more vulnerable to eavesdropping attacks than terrestrial communications. To solve the problem, we propose a coding scheme that hierarchically utilizes polar codes in order to address channel multi-state variation for UAV wiretap channels, without the instantaneous channel state information (CSI) known at the transmitter. The theoretical analysis and simulation results show that the scheme achieves the security capacity of the channel and meets the conditions of reliability and security.