Cyber-Physical Power System (CPPS) is one of the most critical infrastructure systems due to deep integration between power grids and communication networks. In the power system, cascading failure is spreading more readily in CPPS, even leading to blackouts as well as there are new difficulties with the power system security simulation and faults brought by physical harm or network intrusions. The current study summarized the cross- integration of several fields such as computer and cyberspace security in terms of the robustness of Cyber-Physical Systems, viewed as Interconnected and secure network systems. Therefore, the security events that significantly influenced the power system were evaluated in this study, besides the challenges and future directions of power system security simulation technologies were investigated for posing both challenges and opportunities for simulation techniques of power system security like building a new power system to accelerate the transformation of the existing energy system to a clean, low-carbon, safe, and efficient energy system which is used to assure power system stability through fusion systems that combine the cyber-physical to integrate the battery power station, power generation and renewable energy resources through the internet with the cyber system that contains Smart energy system control and attacks.

In the 21st century, world-leading industries are under the accelerated development of digital transformation. Along with information and data resources becoming more transparent on the Internet, many new network technologies were introduced, but cyber-attack also became a severe problem in cyberspace. Over time, industrial control networks are also forced to join the nodes of the Internet. Therefore, cybersecurity is much more complicated than before, and suffering risk of browsing unknown websites also increases. To practice defenses against cyber-attack effectively, Cyber Range is the best platform to emulate all cyber-attacks and defenses. This article will use VMware virtual machine emulation technology, research cyber range systems under industrial control network architecture, and design and implement an industrial control cyber range system. Using the industrial cyber range to perform vulnerability analyses and exploits on web servers, web applications, and operating systems. The result demonstrates the consequences of the vulnerability attack and raises awareness of cyber security among government, enterprises, education, and other related fields, improving the practical ability to defend against cybersecurity threats.

Guidelines, directives, and policy statements are usually presented in “linear” text form - word after word, page after page. However necessary, this practice impedes full understanding, obscures feedback dynamics, hides mutual dependencies and cascading effects and the like-even when augmented with tables and diagrams. The net result is often a checklist response as an end in itself. All this creates barriers to intended realization of guidelines and undermines potential effectiveness. We present a solution strategy using text as “data”, transforming text into a structured model, and generate network views of the text(s), that we then can use for vulnerability mapping, risk assessments and note control point analysis. For proof of concept we draw on NIST conceptual model and analysis of guidelines for smart grid cybersecurity, more than 600 pages of text.

Cyber Attack is the most challenging issue all over the world. Nowadays, Cyber-attacks are increasing on digital systems and organizations. Innovation and utilization of new digital technology, infrastructure, connectivity, and dependency on digital strategies are transforming day by day. The cyber threat scope has extended significantly. Currently, attackers are becoming more sophisticated, well-organized, and professional in generating malware programs in Python, C Programming, C++ Programming, Java, SQL, PHP, JavaScript, Ruby etc. Accurate attack modeling techniques provide cyber-attack planning, which can be applied quickly during a different ongoing cyber-attack. This paper aims to create a new cyber-attack model that will extend the existing model, which provides a better understanding of the network’s vulnerabilities.Moreover, It helps protect the company or private network infrastructure from future cyber-attacks. The final goal is to handle cyber-attacks efficacious manner using attack modeling techniques. Nowadays, many organizations, companies, authorities, industries, and individuals have faced cybercrime. To execute attacks using our model where honeypot, the firewall, DMZ and any other security are available in any environment.

Internet of Things (IoT) and those protocol CoAP and MQTT has security issues that have entirely changed the security strategy should be utilized and behaved for devices restriction. Several challenges have been observed in multiple domains of security, but Distributed Denial of Service (DDoS) have actually dangerous in IoT that have RT. Thus, the IoT paradigm and those protocols CoAP and MQTT have been investigated to seek whether network services could be efficiently delivered for resources usage, managed, and disseminated to the devices. Internet of Things is justifiably joined with the best practices augmentation to make this task enriched. However, factors behaviors related to traditional networks have not been effectively mitigated until now. In this paper, we present and deep, qualitative, and comprehensive systematic mapping to find the answers to the following research questions, such as, (i) What is the state-of-the-art in IoT security, (ii) How to solve the restriction devices challenges via infrastructure involvement, (iii) What type of technical/protocol/ paradigm needs to be studied, and (iv) Security profile should be taken care of, (v) As the proposals are being evaluated: A. If in simulated/virtualized/emulated environment or; B. On real devices, in which case which devices. After doing a comparative study with other papers dictate that our work presents a timely contribution in terms of novel knowledge toward an understanding of formulating IoT security challenges under the IoT restriction devices take care.

Computer and Vehicular networks, both are prone to multiple information security breaches because of many reasons like lack of standard protocols for secure communication and authentication. Distributed Denial of Service (DDoS) is a threat that disrupts the communication in networks. Detection and prevention of DDoS attacks with accuracy is a necessity to make networks safe.In this paper, we have experimented two machine learning-based techniques one each for attack detection and attack prevention. These detection & prevention techniques are implemented in different environments including vehicular network environments and computer network environments. Three different datasets connected to heterogeneous environments are adopted for experimentation. The first dataset is the NSL-KDD dataset based on the traffic of the computer network. The second dataset is based on a simulation-based vehicular environment, and the third CIC-DDoS 2019 dataset is a computer network-based dataset. These datasets contain different number of attributes and instances of network traffic. For the purpose of attack detection AdaBoostM1 classification algorithm is used in WEKA and for attack prevention Logit Model is used in STATA. Results show that an accuracy of more than 99.9% is obtained from the simulation-based vehicular dataset. This is the highest accuracy rate among the three datasets and it is obtained within a very short period of time i.e., 0.5 seconds. In the same way, we use a Logit regression-based model to classify packets. This model shows an accuracy of 100%.

Wireless sensor networks are used in many areas such as war field surveillance, monitoring of patient, controlling traffic, environmental and building surveillance. Wireless technology, on the other hand, brings a load of new threats with it. Because WSNs communicate across radio frequencies, they are more susceptible to interference than wired networks. The authors of this research look at the goals of WSNs in terms of security as well as DDOS attacks. The majority of techniques are available for detecting DDOS attacks in WSNs. These alternatives, on the other hand, stop the assault after it has begun, resulting in data loss and wasting limited sensor node resources. The study finishes with a new method for detecting the UDP Reflection Amplification Attack in WSN, as well as instructions on how to use it and how to deal with the case.

Network security is a prominent topic that is gaining international attention. Distributed Denial of Service (DDoS) attack is often regarded as one of the most serious threats to network security. Software Defined Network (SDN) decouples the control plane from the data plane, which can meet various network requirements. But SDN can also become the object of DDoS attacks. This paper proposes an automated DDoS attack mitigation method that is based on the programmability of the Ryu controller and the features of the OpenFlow switch flow tables. The Mininet platform is used to simulate the whole process, from SDN traffic generation to using a K-Nearest Neighbor model for traffic classification, as well as identifying and mitigating DDoS attack. The packet counts of the victim's malicious traffic input port are significantly lower after the mitigation method is implemented than before the mitigation operation. The purpose of mitigating DDoS attack is successfully achieved.

In recent decades, a Distributed Denial of Service (DDoS) attack is one of the most expensive attacks for business organizations. The DDoS is a form of cyber-attack that disrupts the operation of computer resources and networks. As technology advances, the styles and tools used in these attacks become more diverse. These attacks are increased in frequency, volume, and intensity, and they can quickly disrupt the victim, resulting in a significant financial loss. In this paper, it is described the significance of DDOS attacks and propose a new method for detecting and mitigating the DDOS attacks by analyzing the traffics coming to the server from the BOTNET in attacking system. The process of analyzing the requests coming from the BOTNET uses the Machine learning algorithm in the decision making. The simulation is carried out and the results analyze the DDOS attack.

Undoubtedly, technology has not only transformed our world of work and lifestyle, but it also carries with it a lot of security challenges. The Distributed Denial-of-Service (DDoS) attack is one of the most prominent attacks witnessed by cyberspace of the current era. This paper outlines several DDoS attacks, their mitigation stages, propagation of attacks, malicious codes, and finally provides redemptions of exhibiting normal and DDoS attacked scenarios. A case study of a SYN flooding attack has been exploited by using Metasploit. The utilization of CPU frame length and rate have been observed in normal and attacked phases. Preliminary results clearly show that in a normal scenario, CPU usage is about 20%. However, in attacked phases with the same CPU load, CPU execution overhead is nearly 90% or 100%. Thus, through this research, the major difference was found in CPU usage, frame length, and degree of data flow. Wireshark tool has been used for network traffic analyzer.

Software Defined Networking (SDN) is an emerging technology, which provides the flexibility in communicating among network. Software Defined Network features separation of the data forwarding plane from the control plane which includes controller, resulting centralized network. Due to centralized control, the network becomes more dynamic, and resources are managed efficiently and cost-effectively. Network Virtualization is transformation of network from hardware-based to software-based. Network Function Virtualization will permit implementation, adaptable provisioning, and even management of functions virtually. The use of virtualization of SDN networks permits network to strengthen the features of SDN and virtualization of NFV and has for that reason has attracted notable research awareness over the last few years. SDN platform introduces network security challenges. The network becomes vulnerable when a large number of requests is encapsulated inside packet\_in messages and passed to controller from switch for instruction, if it is not recognized by existing flow entry rules. which will limit the resources and become a bottleneck for the entire network leading to DDoS attack. It is necessary to have quick provisional methods to prevent the switches from breaking down. To resolve this problem, the researcher develops a mechanism that detects and mitigates flood attacks. This paper provides a comprehensive survey which includes research relating frameworks which are utilized for detecting attack and later mitigation of flood DDoS attack in Software Defined Network (SDN) with the help of NFV.

Cities are becoming increasingly smart as the Internet of Things (IoT) proliferates. With IoT devices interconnected, smart cities can offer novel and ubiquitous services as well as automate many of our daily lives (e.g., smart health, smart home). The abundance in the number of IoT devices leads to divergent types of security threats as well. One of such important attacks is the Distributed Denial of Service attack(DDoS). DDoS attacks have become increasingly common in the internet of things because of the rapid growth of insecure devices. These attacks slow down legitimate network requests. Although DDoS attacks were first reported in 1996, the sophistication of these attacks has increased significantly. In mid-August 2020, a 2 Terabytes per second(TBps) attack targeting critical infrastructure, such as finance, was reported. In the next two years, it is predicted that this number will double to 15 million attacks. Blockchain technology, whose development dates back to the advent of the internet, has become one of the most important advancements to come along since that time. Several applications can use this technology to secure exchanges. Using blockchain to mitigate DDoS attacks is discussed in this survey paper in diverse domains to date. Its purpose is to expose the strengths, weaknesses, and limitations of the different approaches to DDoS mitigation. As a research and development platform for DDoS mitigation, this paper will act as a central hub for a more comprehensive understanding of these approaches.

A distributed denial-of-service (DDoS) is a malicious attempt by attackers to disrupt the normal traffic of a targeted server, service or network. This is done by overwhelming the target and its surrounding infrastructure with a flood of Internet traffic. The multiple compromised computer systems (bots or zombies) then act as sources of attack traffic. Exploited machines can include computers and other network resources such as IoT devices. The attack results in either degraded network performance or a total service outage of critical infrastructure. This can lead to heavy financial losses and reputational damage. These attacks maximise effectiveness by controlling the affected systems remotely and establishing a network of bots called bot networks. It is very difficult to separate the attack traffic from normal traffic. Early detection is essential for successful mitigation of the attack, which gives rise to a very important role in cybersecurity to detect the attacks and mitigate the effects. This can be done by deploying machine learning or deep learning models to monitor the traffic data. We propose using various machine learning and deep learning algorithms to analyse the traffic patterns and separate malicious traffic from normal traffic. Two suitable datasets have been identified (DDoS attack SDN dataset and CICDDoS2019 dataset). All essential preprocessing is performed on both datasets. Feature selection is also performed before detection techniques are applied. 8 different Neural Networks/ Ensemble/ Machine Learning models are chosen and the datasets are analysed. The best model is chosen based on the performance metrics (DEEP NEURAL NETWORK MODEL). An alternative is also suggested (Next best - Hypermodel). Optimisation by Hyperparameter tuning further enhances the accuracy. Based on the nature of the attack and the intended target, suitable mitigation procedures can then be deployed.

Target attack identification and detection has always been a concern of network security in the current environment. However, the economic losses caused by DDoS attacks are also enormous. In recent years, DDoS attack detection has made great progress mainly in the user application layer of the network layer. In this paper, a review and discussion are carried out according to the different detection methods and platforms. This paper mainly includes three parts, which respectively review statistics-based machine learning detection, target attack detection on SDN platform and attack detection on cloud service platform. Finally, the research suggestions for DDoS attack detection are given.

Intrusion detection systems (IDS) are most efficient way of defending against network-based attacks aimed at system devices, especially wireless devices. These systems are used in almost all large-scale IT infrastructures components, and they effected with different types of network attacks such as DDoS attack. Distributed Denial of-Services (DDoS) attacks the protocols and systems that are intended to provide services (to the public) are inherently vulnerable to attacks like DDoS, which were launched against a number of important Internet sites where security precautions were in place.

The issues of development and legal regulation of cybersecurity in Ukraine are considered. The expediency of further improvement of the regulatory framework, its implementation and development of cybersecurity systems is substantiated. Further development of the theoretical base of cyber defense using spline functions is proposed. The characteristics of network traffic are considered from the point of view of detecting DDoS cyber attacks (SYN-Flood, ICMP-Flood, UDP-Flood) and predicting DDoS cyber-attacks using spline functions. The spline extrapolation method makes it possible to predict DDoS cyber attacks with great accuracy.

Video summarization aims to improve the efficiency of large-scale video browsing through producting concise summaries. It has been popular among many scenarios such as video surveillance, video review and data annotation. Traditional video summarization techniques focus on filtration in image features dimension or image semantics dimension. However, such techniques can make a large amount of possible useful information lost, especially for many videos with rich text semantics like interviews, teaching videos, in that only the information relevant to the image dimension will be retained. In order to solve the above problem, this paper considers video summarization as a continuous multi-dimensional decision-making process. Specifically, the summarization model predicts a probability for each frame and its corresponding text, and then we designs reward methods for each of them. Finally, comprehensive summaries in two dimensions, i.e. images and semantics, is generated. This approach is not only unsupervised and does not rely on labels and user interaction, but also decouples the semantic and image summarization models to provide more usable interfaces for subsequent engineering use.

In recent years, in order to continuously promote the construction of safe cities, security monitoring equipment has been widely used all over the country. How to use computer vision technology to realize effective intelligent analysis of violence in video surveillance is very important to maintain social stability and ensure people's life and property safety. Video surveillance system has been widely used because of its intuitive and convenient advantages. However, the existing video monitoring system has relatively single function, and generally only has the functions of monitoring video viewing, query and playback. In addition, relevant researchers pay less attention to the complex abnormal behavior of violence, and relevant research often ignores the differences between violent behaviors in different scenes. At present, there are two main problems in video abnormal behavior event detection: the video data of abnormal behavior is less and the definition of abnormal behavior in different scenes cannot be clearly distinguished. The main existing methods are to model normal behavior events first, and then define videos that do not conform to the normal model as abnormal, among which the learning method of video space-time feature representation based on deep learning shows a good prospect. In the face of massive surveillance videos, it is necessary to use deep learning to identify violent behaviors, so that the machine can learn to identify human actions, instead of manually monitoring camera images to complete the alarm of violent behaviors. Network training mainly uses video data set to identify network training.

The Internet has evolved to the point that gigabytes and even terabytes of data are generated and processed on a daily basis. Such a stream of data is characterised by high volume, velocity and variety and is referred to as Big Data. Traditional data processing tools can no longer be used to process big data, because they were not designed to handle such a massive amount of data. This problem concerns also cyber security, where tools like intrusion detection systems employ classification algorithms to analyse the network traffic. Achieving a high accuracy attack detection becomes harder when the amount of data increases and the algorithms must be efficient enough to keep up with the throughput of a huge data stream. Due to the challenges posed by a big data environment, some monitoring systems have already shifted from deep packet inspection to flow-level inspection. The goal of this paper is to evaluate the applicability of an existing intrusion detection technique that performs deep packet inspection in a big data setting. We have conducted several experiments with Apache Spark to assess the performance of the technique when classifying anomalous packets, showing that it benefits from the use of Spark.

Though several deep learning (DL) detectors have been proposed for the network attack detection and achieved high accuracy, they are computationally expensive and struggle to satisfy the real-time detection for high-speed networks. Recently, programmable switches exhibit a remarkable throughput efficiency on production networks, indicating a possible deployment of the timely detector. Therefore, we present Soter, a DL enhanced in-network framework for the accurate real-time detection. Soter consists of two phases. One is filtering packets by a rule-based decision tree running on the Tofino ASIC. The other is executing a well-designed lightweight neural network for the thorough inspection of the suspicious packets on the CPU. Experiments on the commodity switch demonstrate that Soter behaves stably in ten network scenarios of different traffic rates and fulfills per-flow detection in 0.03s. Moreover, Soter naturally adapts to the distributed deployment among multiple switches, guaranteeing a higher total throughput for large data centers and cloud networks.

The growing number of cybersecurity incidents and the always increasing complexity of cybersecurity attacks is forcing the industry and the research community to develop robust and effective methods to detect and respond to network attacks. Many tools are either built upon a large number of rules and signatures which only large third-party vendors can afford to create and maintain, or are based on complex artificial intelligence engines which, in most cases, still require personalization and fine-tuning using costly service contracts offered by the vendors.This paper introduces an open-source network traffic monitoring system based on the concept of cyberscore, a numerical value that represents how a network activity is considered relevant for spotting cybersecurity-related events. We describe how this technique has been applied in real-life networks and present the result of this evaluation.

Current intrusion detection techniques cannot keep up with the increasing amount and complexity of cyber attacks. In fact, most of the traffic is encrypted and does not allow to apply deep packet inspection approaches. In recent years, Machine Learning techniques have been proposed for post-mortem detection of network attacks, and many datasets have been shared by research groups and organizations for training and validation. Differently from the vast related literature, in this paper we propose an early classification approach conducted on CSE-CIC-IDS2018 dataset, which contains both benign and malicious traffic, for the detection of malicious attacks before they could damage an organization. To this aim, we investigated a different set of features, and the sensitivity of performance of five classification algorithms to the number of observed packets. Results show that ML approaches relying on ten packets provide satisfactory results.

The MANET architecture's future growth will make extensive use of encryption and encryption to keep network participants safe. Using a digital signature node id, we illustrate how we may stimulate the safe growth of subjective clusters while simultaneously addressing security and energy efficiency concerns. The dynamic topology of MANET allows nodes to join and exit at any time. A form of attack known as a black hole assault was used to accomplish this. To demonstrate that he had the shortest path with the least amount of energy consumption, an attacker in MATLAB R2012a used a digital signature ID to authenticate the node from which he wished to intercept messages (DSEP). “Digital Signature”, “MANET,” and “AODV” are all terms used to describe various types of digital signatures. Black Hole Attack, Single Black Hole Attack, Digital Signature, and DSEP are just a few of the many terms associated with MANET.