Biblio

Network Intrusion Detection Systems (IDSs) have been used to increase the level of network security for many years. The main purpose of such systems is to detect and block malicious activity in the network traffic. Researchers have been improving the performance of IDS technology for decades by applying various machine-learning techniques. From the perspective of academia, obtaining a quality dataset (i.e. a sufficient amount of captured network packets that contain both malicious and normal traffic) to support machine learning approaches has always been a challenge. There are many datasets publicly available for research purposes, including NSL-KDD, KDDCUP 99, CICIDS 2017 and UNSWNB15. However, these datasets are becoming obsolete over time and may no longer be adequate or valid to model and validate IDSs against state-of-the-art attack techniques. As attack techniques are continuously evolving, datasets used to develop and test IDSs also need to be kept up to date. Proven performance of an IDS tested on old attack patterns does not necessarily mean it will perform well against new patterns. Moreover, existing datasets may lack certain data fields or attributes necessary to analyse some of the new attack techniques. In this paper, we argue that academia needs up-to-date high-quality datasets. We compare publicly available datasets and suggest a way to provide up-to-date high-quality datasets for researchers and the security industry. The proposed solution is to utilize the network traffic captured from the Locked Shields exercise, one of the world’s largest live-fire international cyber defence exercises held annually by the NATO CCDCOE. During this three-day exercise, red team members consisting of dozens of white hackers selected by the governments of over 20 participating countries attempt to infiltrate the networks of over 20 blue teams, who are tasked to defend a fictional country called Berylia. After the exercise, network packets captured from each blue team’s network are handed over to each team. However, the countries are not willing to disclose the packet capture (PCAP) files to the public since these files contain specific information that could reveal how a particular nation might react to certain types of cyberattacks. To overcome this problem, we propose to create a dedicated virtual team, capture all the traffic from this team’s network, and disclose it to the public so that academia can use it for unclassified research and studies. In this way, the organizers of Locked Shields can effectively contribute to the advancement of future artificial intelligence (AI) enabled security solutions by providing annual datasets of up-to-date attack patterns.

Wireless Sensor networks can be composed of smart buildings, smart homes, smart grids, and smart mobility, and they can even interconnect all these fields into a large-scale smart city network. Software-Defined Networking is an ideal technology to realize Internet-of-Things (IoT) Network and WSN network requirements and to efficiently enhance the security of these networks. Software defines Networking (SDN) is used to support IoT and WSN related networking elements, additional security concerns rise, due to the elevated vulnerability of such deployments to specific types of attacks and the necessity of inter-cloud communication any IoT application would require. This work is a study of different security mechanisms available in SDN for IoT and WSN network secure communication. This work also formulates the problems when existing methods are implemented with different networks parameters.

On the one hand, laparoscopic surgery as medical state-of-the-art method is minimal invasive, and thus less stressful for patients. On the other hand, laparoscopy implies higher demands on physicians, such as mental load or preparation time, hence appropriate technical support is essential for quality and suc-cess. Medical Digital Twins provide an integrated and virtual representation of patients' and organs' data, and thus a generic concept to make complex information accessible by surgeons. In this way, minimal invasive surgery could be improved significantly, but requires also a much more complex software system to achieve the various resulting requirements. The biggest challenges for these systems are the safe and precise mapping of the digital twin to reality, i.e. dealing with deformations, movement and distortions, as well as balance out the competing requirement for intuitive and immersive user access and security. The case study ARAILIS is presented as a proof in concept for such a system and provides a starting point for further research. Based on the insights delivered by this prototype, a vision for future Medical Digital Twins in surgery is derived and discussed.

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.

This paper mainly explores the detection and defense of DDoS attacks in the SDN architecture of the 5G environment, and proposes a DDoS attack detection method based on the deep learning two-level model CNN-LSTM in the SDN network. Not only can it greatly improve the accuracy of attack detection, but it can also reduce the time for classifying and detecting network traffic, so that the transmission of DDoS attack traffic can be blocked in time to ensure the availability of network services.

From the past few years, DDoS attack incidents are continuously rising across the world. DDoS attackers have also shifted their target towards cloud environments as majority of services have shifted their operations to cloud. Various authors proposed distinct solutions to minimize the DDoS attacks effects on victim services and co-located services in cloud environments. In this work, we propose an approach by utilizing incoming request separation at the container-level. In addition, we advocate to employ scale-inside out [10] approach for all the suspicious requests. In this manner, we achieve the request serving of all the authenticated benign requests even in the presence of an attack. We also improve the usages of scale-inside out approach by applying it to a container which is serving the suspicious requests in a separate container. The results of our proposed technique show a significant decrease in the response time of benign users during the DDoS attack as compared with existing solutions.

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.

We propose DecCert, a decentralized public key infrastructure designed as a smart contract that solves the problem of identity attestation on public blockchains. Our system allows an individual to bind an identity to a public blockchain address. Once a claim of identity is made by an individual, other users can choose to verify the attested identity based on the evidence presented by an identity claim maker by staking cryptocurrency in the DecCert smart contract. Increasing levels of trust are naturally built based upon the amount staked and the duration the collateral is staked for. This mechanism replaces the usual utilization of digital signatures in a traditional hierarchical certificate authority model or the web of trust model to form a publicly verifiable decentralized stake of trust model. We also present a novel solution to the certificate revocation problem and implement our solution on the Ethereum blockchain. Further, we show that our design solves Zooko’s triangle as defined for public key infrastructure deployments.

Cyberattacks are one of the most pressing issues of our time. The impact of cyberthreats can damage various sectors such as business, health care, and governments, so one of the best solutions to deal with these cyberattacks and reduce cybersecurity threats is using Deep Learning. In this paper, we have created an in-depth study model to detect SQL Injection Attacks and Cross-Site Script attacks. We focused on XSS on the Stored-XSS attack type because SQL and Stored-XSS have similar site management methods. The advantage of combining deep learning with cybersecurity in our system is to detect and prevent short-term attacks without human interaction, so our system can reduce and prevent web attacks. This post-training model achieved a more accurate result more than 99% after maintaining the learning level, and 99% of our test data is determined by this model if this input is normal or dangerous.

Cyberattack on power system brings new challenges on the development of modern power system. Hackers may implement false data injection attack (FDIA) to cause unstable operating conditions of the power system. However, data from different power internet of things usually contains a lot of redundancy, making it difficult for current efficient discriminant model to precisely identify FDIA. To address this problem, we propose a deep learning network-based data fusion model to handle features from measurement data in power system. Proposed model includes a data enrichment module and a data fusion module. We firstly employ feature engineering technique to enrich features from power system operation in time dimension. Subsequently, a long short-term memory based autoencoder (LSTM-AE) is designed to efficiently avoid feature space explosion problem during data enriching process. Extensive experiments are performed on several classical attack detection models over the load data set from IEEE 14-bus system and simulation results demonstrate that fused data from proposed model shows higher detection accuracy with respect to the raw data.

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.

With the development of computer technology and information security technology, computer networks will increasingly become an important means of information exchange, permeating all areas of social life. Therefore, recognizing the vulnerabilities and potential threats of computer networks as well as various security problems that exist in reality, designing and researching computer quality architecture, and ensuring the security of network information are issues that need to be resolved urgently. The purpose of this article is to study the design and realization of information security technology and computer quality system structure. This article first summarizes the basic theory of information security technology, and then extends the core technology of information security. Combining the current status of computer quality system structure, analyzing the existing problems and deficiencies, and using information security technology to design and research the computer quality system structure on this basis. This article systematically expounds the function module data, interconnection structure and routing selection of the computer quality system structure. And use comparative method, observation method and other research methods to design and research the information security technology and computer quality system structure. Experimental research shows that when the load of the computer quality system structure studied this time is 0 or 100, the data loss rate of different lengths is 0, and the correct rate is 100, which shows extremely high feasibility.

As cyber-physical systems are becoming more wide spread, it is imperative to secure these systems. In the real world these systems produce large amounts of data. However, it is generally impractical to test security techniques on operational cyber-physical systems. Thus, there exists a need to have realistic systems and data for testing security of cyber-physical systems [1]. This is often done in testbeds and cyber ranges. Most cyber ranges and testbeds focus on traditional network systems and few incorporate cyber-physical components. When they do, the cyber-physical components are often simulated. In the systems that incorporate cyber-physical components, generally only the network data is analyzed for attack detection and diagnosis. While there is some study in using physical signals to detect and diagnosis attacks, this data is not incorporated into current testbeds and cyber ranges. This study surveys currents testbeds and cyber ranges and demonstrates a prototype testbed that includes cyber-physical components and sensor data in addition to traditional cyber data monitoring.

A new privacy Laplace common recognition algorithm is designed to protect users’ privacy data in this paper. This algorithm disturbs state transitions and information generation functions using exponentially decaying Laplace noise to avoid attacks. The mean square consistency and privacy protection performance are further studied. Finally, the theoretical results obtained are verified by performing numerical simulations.

In today's society, with the continuous development of artificial intelligence, artificial intelligence technology plays an increasingly important role in social and economic development, and hass become the fastest growing, most widely used and most influential high-tech in the world today one. However, at the same time, information technology has also brought threats to network security to the entire network world, which makes information systems also face huge and severe challenges, which will affect the stability and development of society to a certain extent. Therefore, comprehensive analysis and research on information system security is a very necessary and urgent task. Through the security assessment of the information system, we can discover the key hidden dangers and loopholes that are hidden in the information source or potentially threaten user data and confidential files, so as to effectively prevent these risks from occurring and provide effective solutions; at the same time To a certain extent, prevent virus invasion, malicious program attacks and network hackers' intrusive behaviors. This article adopts the experimental analysis method to explore how to apply the most practical, advanced and efficient artificial intelligence theory to the information system security assessment management, so as to further realize the optimal design of the information system security assessment management system, which will protect our country the information security has very important meaning and practical value. According to the research results, the function of the experimental test system is complete and available, and the security is good, which can meet the requirements of multi-user operation for security evaluation of the information system.

With the development of sensor technology, people put forward a higher level, more diversified demand for portable rangefinders. However, its data storage method has not been developed in a large scale and breakthrough. This paper studies the design of portable sensor data storage system based on homomorphic encryption algorithm, which aims to maintain the security of sensor data storage through homomorphic encryption algorithm. This paper analyzes the functional requirements of the sensor data storage system, puts forward the overall design scheme of the system, and explains in detail the requirements and indicators for the specific realization of each part of the function. Analyze the different technical resources currently used in the storage system field, and dig deep into the key technologies that match the portable sensor data storage system. This paper has changed the problem of cumbersome operation steps and inconvenient data recovery in the sensor data storage system. This paper mainly uses the method of control variables and data comparison to carry out the experiment. The experimental results show that the success rate of the sensor data storage system under the homomorphic encryption algorithm is infinitely close to 100% as the number of data blocks increases.

Cyber-security incidents have grown significantly in modern networks, far more diverse and highly destructive and disruptive. According to the 2021 Cyber Security Statistics Report [1], cybercrime is up 600% during this COVID pandemic, the top attacks are but are not confined to (a) sophisticated phishing emails, (b) account and DNS hijacking, (c) targeted attacks using stealth and air gap malware, (d) distributed denial of services (DDoS), (e) SQL injection. Additionally, 95% of cyber-security breaches result from human error, according to Cybint Report [2]. The average time to identify a breach is 207 days as per Ponemon Institute and IBM, 2022 Cost of Data Breach Report [3]. However, various preventative controls based on cyber-security risk estimation and awareness results decrease most incidents, but not all. Further, any incident detection delay and passive actions to cyber-security incidents put the organizational assets at risk. Therefore, the cyber-security incident management system has become a vital part of the organizational strategy. Thus, the authors propose a framework to converge a "Security Operation Center" (SOC) and a "Network Operations Center" (NOC) in an "Integrated Network Security Operation Center" (INSOC), to overcome cyber-threat detection and mitigation inefficiencies in the near-real-time scenario. We applied the People, Process, Technology, Governance and Compliance (PPTGC) approach to develop the INSOC conceptual framework, according to the requirements we formulated for its operation [4], [5]. The article briefly describes the INSOC conceptual framework and its usefulness, including the central area of the PPTGC approach while designing the framework.

Phishing activity is undertaken by the hackers to compromise the computer networks and financial system. A compromised computer system or network provides data and or processing resources to the world of cybercrime. Cybercrimes are projected to cost the world \$6 trillion by 2021, in this context phishing is expected to continue being a growing challenge. Statistics around phishing growth over the last decade support this theory as phishing numbers enjoy almost an exponential growth over the period. Recent reports on the complexity of the phishing show that the fight against phishing URL as a means of building more resilient cyberspace is an evolving challenge. Compounding the problem is the lack of cyber security expertise to handle the expected rise in incidents. Previous research have proposed different methods including neural network, data mining technique, heuristic-based phishing detection technique, machine learning to detect phishing websites. However, recently phishers have started to use more sophisticated techniques to attack the internet users such as VoIP phishing, spear phishing etc. For these modern methods, the traditional ways of phishing detection provide low accuracy. Hence, the requirement arises for the application and development of modern tools and techniques to use as a countermeasure against such phishing attacks. Keeping in view the nature of recent phishing attacks, it is imperative to develop a state-of-the art anti-phishing tool which should be able to predict the phishing attacks before the occurrence of actual phishing incidents. We have designed such a tool that will work efficiently to detect the phishing websites so that a user can understand easily the risk of using of his personal and financial data.

Nowadays, the popularity of intelligent terminals makes malwares more and more serious. Among the many features of application, the call graph can accurately express the behavior of the application. The rapid development of graph neural network in recent years provides a new solution for the malicious analysis of application using call graphs as features. However, there are still problems such as low accuracy. This paper established a large-scale data set containing more than 40,000 samples and selected the class call graph, which was extracted from the application, as the feature and used the graph embedding combined with the deep neural network to detect the malware. The experimental results show that the accuracy of the detection model proposed in this paper is 97.7%; the precision is 96.6%; the recall is 96.8%; the F1-score is 96.4%, which is better than the existing detection model based on Markov chain and graph embedding detection model.

Federated learning (FL) has emerged as a promising paradigm for distributed training of machine learning models. In FL, several participants train a global model collaboratively by only sharing model parameter updates while keeping their training data local. However, FL was recently shown to be vulnerable to data poisoning attacks, in which malicious participants send parameter updates derived from poisoned training data. In this paper, we focus on defending against targeted data poisoning attacks, where the attacker’s goal is to make the model misbehave for a small subset of classes while the rest of the model is relatively unaffected. To defend against such attacks, we first propose a method called MAPPS for separating malicious updates from benign ones. Using MAPPS, we propose three methods for attack detection: MAPPS + X-Means, MAPPS + VAT, and their Ensemble. Then, we propose an attack mitigation approach in which a "clean" model (i.e., a model that is not negatively impacted by an attack) can be trained despite the existence of a poisoning attempt. We empirically evaluate all of our methods using popular image classification datasets. Results show that we can achieve \textgreater 95% true positive rates while incurring only \textless 2% false positive rate. Furthermore, the clean models that are trained using our proposed methods have accuracy comparable to models trained in an attack-free scenario.

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.

The Internet of things (IoT) is proving to be a boon in granting internet access to regularly used objects and devices. Sensors, programs, and other innovations interact and trade information with different gadgets and frameworks over the web. Even in modern times, IoT gadgets experience the ill effects of primary security threats, which expose them to many dangers and malware, one among them being IoT botnets. Botnets carry out attacks by serving as a vector and this has become one of the significant dangers on the Internet. These vectors act against associations and carry out cybercrimes. They are used to produce spam, DDOS attacks, click frauds, and steal confidential data. IoT gadgets bring various challenges unlike the common malware on PCs and Android devices as IoT gadgets have heterogeneous processor architecture. Numerous researches use static or dynamic analysis for detection and classification of botnets on IoT gadgets. Most researchers haven't addressed the multi-architecture issue and they use a lot of computing resources for analyzing. Therefore, this approach attempts to classify botnets in IoT by using PSI-Graphs which effectively addresses the problem of encryption in IoT botnet detection, tackles the multi-architecture problem, and reduces computation time. It proposes another methodology for describing and recognizing botnets utilizing graph-based Machine Learning techniques and Exploratory Data Analysis to analyze the data and identify how separable the data is to recognize bots at an earlier stage so that IoT devices can be prevented from being attacked.

The modern electrical grid is a complex cyber-physical system, and thus is vulnerable to measurement losses and attacks. In this paper, we consider the problem of detecting false data injection (FDI) attacks and bad data in unobservable power systems. Classical bad-data detection methods usually assume observable systems and cannot detect stealth FDI attacks. We use the smoothness property of the system states (voltages) w.r.t. the admittance matrix, which is also the Laplacian of the graph representation of the grid. First, we present the Laplacian-based regularized state estimator, which does not require full observability of the network. Then, we derive the Laplacian-regularized generalized likelihood ratio test (LR-GLRT). We show that the LR-GLRT has a component of a soft high-pass graph filter applied to the state estimator. Numerical results on the IEEE 118-bus system demonstrate that the LR-GLRT outperforms other detection approaches and is robust to missing data.

Phishing is a method of online fraud where attackers are targeted to gain access to the computer systems for monetary benefits or personal gains. In this case, the attackers pose themselves as legitimate entities to gain the users' sensitive information. Phishing has been significant concern over the past few years. The firms are recording an increase in phishing attacks primarily aimed at the firm's intellectual property and the employees' sensitive data. As a result, these attacks force firms to spend more on information security, both in technology-centric and human-centric approaches. With the advancements in cyber-security in the last ten years, many techniques evolved to detect phishing-related activities through websites and emails. This study focuses on the latest techniques used for detecting phishing attacks, including the usage of Visual selection features, Machine Learning (ML), and Artificial Intelligence (AI) to see the phishing attacks. New strategies for identifying phishing attacks are evolving, but limited standardized knowledge on phishing identification and mitigation is accessible from user awareness training. So, this study also focuses on the role of security-awareness movements to minimize the impact of phishing attacks. There are many approaches to train the user regarding these attacks, such as persona-centred training, anti-phishing techniques, visual discrimination training and the usage of spam filters, robust firewalls and infrastructure, dynamic technical defense mechanisms, use of third-party certified software to mitigate phishing attacks from happening. Therefore, the purpose of this paper is to carry out a systematic analysis of literature to assess the state of knowledge in prominent scientific journals on the identification and prevention of phishing. Forty-three journal articles with the perspective of phishing detection and prevention through awareness training were reviewed from 2011 to 2020. This timely systematic review also focuses on the gaps identified in the selected primary studies and future research directions in this area.

The problem of information security of critical information infrastructure objects in the conditions of openness is formulated. The concept of information infrastructure openness is analyzed. An approach to assessing the openness of an information system is presented. A set-theoretic model of information resources openness was developed. The formulation of the control problem over the degree of openness with restrictions on risk was carried out. An example of solving the problem of finding the coefficient of openness is presented.