HTTP flood DDoS (Distributed Denial of Service) attacks send illegitimate HTTP requests to the targeted site or server. These kinds of attacks corrupt the networks with the help of massive attacking nodes thus blocking incoming traffic. Computer network connected devices are the major source to distributed denial of service attacks (or) botnet attacks. The computer manufacturers rapidly increase the network devices as per the requirement increases in the different environmental needs. Generally the manufacturers cannot ship computer network products with high level security. Those network products require additional security to prevent the DDoS attacks. The present technology is filled with 4G that will impact DDoS attacks. The million DDoS attacks had experienced in every year by companies or individuals. DDoS attack in a network would lead to loss of assets, data and other resources. Purchasing the new equipment and repair of the DDoS attacked network is financially becomes high in the value. The prevention mechanisms like CAPTCHA are now outdated to the bots and which are solved easily by the advanced bots. In the proposed work a secured botnet prevention mechanism provides network security by prevent and mitigate the http flooding based DDoS attack and allow genuine incoming traffic to the application or server in a network environment with the help of integrating invisible challenge and Resource Request Rate algorithms to the application. It offers double security layer to handle malicious bots to prevent and mitigate.

The botnet is a serious network security threat that can cause servers crash, so how to detect the behavior of Botnet has already become an important part of the research of network security. DNS(Domain Name System) request is the first step for most of the mainframe computers controlled by Botnet to communicate with the C&C(command; control) server. The detection of DNS request domain names is an important way for mainframe computers controlled by Botnet. However, the detection method based on fixed rules is hard to take effect for botnet based on DGA(Domain Generation Algorithm) because malicious domain names keep evolving and derive many different generation methods. Contrasted with the traditional methods, the method based on machine learning is a better way to detect it by learning and modeling the DGA. This paper presents a method based on the Naive Bayes model, the XGBoost model, the SVM(Support Vector Machine) model, and the MLP(Multi-Layer Perceptron) model, and tests it with real data sets collected from DGA, Alexa, and Secrepo. The experimental results show the precision score, the recall score, and the F1 score for each model.

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.

Chaos is an interesting phenomenon for nonlinear systems that emerges due to its complex and unpredictable behavior. With the escalated use of low-powered edge-compute devices, data security at the edge develops the need for security in communication. The characteristic that Chaos synchronizes over time for two different chaotic systems with their own unique initial conditions, is the base for chaos implementation in communication. This paper proposes an encryption architecture suitable for communication of on-chip sensors to provide a POC (proof of concept) with security encrypted on the same chip using different chaotic equations. In communication, encryption is achieved with the help of microcontrollers or software implementations that use more power and have complex hardware implementation. The small IoT devices are expected to be operated on low power and constrained with size. At the same time, these devices are highly vulnerable to security threats, which elevates the need to have low power/size hardware-based security. Since the discovery of chaotic equations, they have been used in various encryption applications. The goal of this research is to take the chaotic implementation to the CMOS level with the sensors on the same chip. The hardware co-simulation is demonstrated on an FPGA board for Chua encryption/decryption architecture. The hardware utilization for Lorenz, SprottD, and Chua on FPGA is achieved with Xilinx System Generation (XSG) toolbox which reveals that Lorenz’s utilization is 9% lesser than Chua’s.

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.

Requirement Elicitation is a key phase in software development. The fundamental goal of security requirement elicitation is to gather appropriate security needs and policies from stakeholders or organizations. The majority of systems fail due to incorrect elicitation procedures, affecting development time and cost. Security requirement elicitation is a major activity of requirement engineering that requires the attention of developers and other stakeholders. To produce quality requirements during software development, the authors suggested a methodology for effective requirement elicitation. Many challenges surround requirement engineering. These concerns can be connected to scope, preconceptions in requirements, etc. Other difficulties include user confusion over technological specifics, leading to confusing system aims. They also don't realize that the requirements are dynamic and prone to change. To protect the privacy of medical images, the proposed image cryptosystem uses a CCM-generated chaotic key series to confuse and diffuse them. A hexadecimal pre-processing technique is used to increase the security of color images utilising a hyper chaos-based image cryptosystem. Finally, a double-layered security system for biometric photos is built employing chaos and DNA cryptography.

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.

Chaotic cryptography is structurally related to the concepts of confusion and diffusion in traditional cryptography theory. Chaotic cryptography is formed by the inevitable connection between chaos theory and pure cryptography. In order to solve the shortcomings of the existing research on information encryption security system, this paper discusses the realization technology of information security, the design principles of encryption system and three kinds of chaotic mapping systems, and discusses the selection of development tools and programmable devices. And the information encryption security system based on chaos algorithm is designed and discussed, and the randomness test of three groups of encrypted files is carried out by the proposed algorithm and the AES (Advanced Encryption Standard) algorithm. Experimental data show that the uniformity of P-value value of chaos algorithm is 0.714 on average. Therefore, it is verified that the information encryption security system using chaos algorithm has high security.

Since data security is an important branch of the wide concept of security, using simple and interpretable data security methods is deemed necessary. A considerable volume of data that is transferred through the internet is in the form of image. Therefore, several methods have focused on encrypting and decrypting images but some of the conventional algorithms are complex and time consuming. On the other hand, denial method or steganography has attracted the researchers' attention leading to more security for transferring images. This is because attackers are not aware of encryption on images and therefore they do not try to decrypt them. Here, one of the most effective and simplest operators (XOR) is employed. The received shares in destination only with XOR operation can recover original images. Users are not necessary to be familiar with computer programing, data coding and the execution time is lesser compared to chaos-based methods or coding table. Nevertheless, for designing the key when we have messy images, we use chaotic functions. Here, in addition to use the XOR operation, eliminating the pixel expansion and meaningfulness of the shared images is of interest. This method is simple and efficient and use both encryption and steganography; therefore, it can guarantee the security of transferred images.

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.

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.

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.

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.

Critical infrastructures such as the electricity grid can be severely impacted by cyber-attacks on its supply chain. Hence, having a robust cybersecurity infrastructure and management system for the electricity grid is a high priority. This paper proposes a cyber-security protocol for defense against man-in-the-middle (MiTM) attacks to the supply chain, which uses encryption and cryptographic multi-party authentication. A cyber-physical simulator is utilized to simulate the power system, control system, and security layers. The correctness of the attack modeling and the cryptographic security protocol against this MiTM attack is demonstrated in four different attack scenarios.

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.

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.

Traditional risk assessment process based on knowledge of threat occurrence probability against every system’s asset. One should consider asset placement, applied security measures on asset and network levels, adversary capabilities and so on: all of that has significant influence on probability value. We can measure threat probability by modelling complex attack process. Such process requires creating an attack tree, which consist of elementary attacks against different assets and relations between elementary attacks and impact on influenced assets. However, different attack path may lead to targeted impact – so task of finding optimal attack chain on a given system topology emerges. In this paper method for complex attack graph creation presented, allowing automatic building various attack scenarios for a given system. Assuming that exploits of particular vulnerabilities represent by independent events, we can compute the overall success probability of a complex attack as the product of the success probabilities of exploiting individual vulnerabilities. This assumption makes it possible to use algorithms for finding the shortest paths on a directed graph to find the optimal chain of attacks for a given adversary’s target.

Since the provision of digital services in our days (e.g. container management, transport of COVID vaccinations or LNG) in most economic sectors (e.g. maritime, health, energy) involve national, EU and non-EU stakeholders compose complex Supply Chain Services (SCS). The security of the SCS is most important and it emphasized in the NIS 2 directive [3] and it is a shared responsibility of all stakeholders involved that will need to be compliant with a scheme. In this paper we present an overview of the proposed Cybersecurity Certification Scheme for Supply Chain Services (EUSCS) as proposed by the European Commission (EC) project CYRENE [1]. The EUSCS scheme covers all the three assurance levels defined in the Cybersecurity Act (CSA) [2] taking into consideration the criticality of SCS according to the NIS 2 directive [3], the ENISA Threat Landscape for Supply Chain Attacks [4] and the CYRENE extended online Information Security Management System (ISMS) that allows all SCS stakeholders to provide and access all information needed for certification purposes making the transition from current national schemes in the EU easier.

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.

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

Distributed ledger technologies (DLTs) based on Directed Acyclic Graphs (DAGs) have been gaining much attention due to their performance advantage over the traditional blockchain. IOTA is an example of DAG-based DLT that has shown its significance in the Internet of Things (IoT) environment. Despite that, IOTA is vulnerable to double-spend attacks, which threaten the immutability of the ledger. In this paper, we propose an efficient yet simple method for detecting a parasite chain, which is one form of attempting a double-spend attack in the IOTA network. In our method, a score function measuring the importance of each transaction in the IOTA network is employed. Any abrupt change in the importance of a transaction is reflected in the 1st and 2nd order derivatives of this score function, and therefore used in the calculation of an anomaly score. Due to how the score function is formulated, this anomaly score can be used in the detection of a particular type of parasite chain, characterized by sudden changes in the in-degree of a transaction in the IOTA graph. The experimental results demonstrate that the proposed method is accurate and linearly scalable in the number of edges in the network.

Cybersecurity is without doubt becoming a societal challenge. It even starts to affect sectors that were not considered to be at risk in the past because of their relative isolation. One of these sectors is aviation in general, and specifically air traffic management. Nowadays, the cyber security is one of the essential issues of current Air Traffic Systems. Compliance with the basic principles of cyber security is mandated by European Union law as well as the national law. Therefore, EUROCONTROL as the provider of several tools or services (ARTAS, EAD, SDDS, etc.), is regularly conducting various activities, such as the cyber-security assessments, penetration testing, supply chain risk assessment, in order to maintain and improve persistence of the products against the cyber-attacks.

Traditional side-channel attacks have shortcomings such as low efficiency, extremely difficult collection and injection of fault information in real environments, and poor applicability of attacks. The cache timing attack proposed in recent years is a new type of side-channel attack method. This attack method uses the difference in the reading speed of the computer CPU cache to enable the attacker to obtain the confidential information during the execution of the algorithm. The attack efficiency is high, and the cost is relatively low. little. Present algorithm is a lightweight block cipher proposed in 2007. The algorithm has excellent hardware implementation and concise round function design. On this basis, scholars at home and abroad have carried out different side-channel attacks on it, such as differential attacks., multiple differential chain attacks, algebraic attacks, etc. At present, there is no published research on the Cache timing attack against the Present algorithm at home and abroad. In this paper, the output value of the S box in the first and second rounds of the encryption process is obtained through the combination of the Cache timing attack and the side-channel Trojan horse, and Combined with the key recovery algorithm, the master key of the algorithm is finally recovered.

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.

In the era of Internet usage growth, storage services are widely used where users' can store their data, while hackers techniques pose massive threats to users' data security. The proposed system introduces multiple layers of security where data confidentiality, integrity and availability are achieved using honey encryption, hashed random passwords as well as detecting intruders and preventing them. The used techniques can ensure security against brute force and denial of service attacks. Our proposed methodology proofs the efficiency for storing and retrieving data using honey words and password hashing with less execution time and more security features achieved compared with other systems. Other systems depend on user password leading to easily predict it, we avoid this approach by making the password given to the user is randomly generated which make it unpredictable and hard to break. Moreover, we created a simple user interface to interact with users to take their inputs and store them along with the given password in true database, if an adversary detected, he will be processed as a normal user but with fake information taken from another database called false database, after that, the admin will be notified about this illegitimate access by providing the IP address. This approach will make the admin have continuous detection and ensure availability and confidentiality. Our execution time is efficient as the encryption process takes 244 ms and decryption 229 ms.