Biblio

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.

Malware detection and analysis can be a burdensome task for incident responders. As such, research has turned to machine learning to automate malware detection and malware family classification. Existing work extracts and engineers static and dynamic features from the malware sample to train classifiers. Despite promising results, such techniques assume that the analyst has access to the malware executable file. Self-deleting malware invalidates this assumption and requires analysts to find forensic evidence of malware execution for further analysis. In this paper, we present and evaluate an approach to detecting malware that executed on a Windows target and further classify the malware into its associated family to provide semantic insight. Specifically, we engineer features from the Windows prefetch file, a file system forensic artifact that archives process information. Results show that it is possible to detect the malicious artifact with 99% accuracy; furthermore, classifying the malware into a fine-grained family has comparable performance to techniques that require access to the original executable. We also provide a thorough security discussion of the proposed approach against adversarial diversity.

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.

Analyzing reflectionally symmetric features inside an image is one of the important processes for recognizing the peculiar appearance of natural and man-made objects, biological patterns, etc. In this work, we will point out an efficient detector of reflectionally symmetric shapes by addressing a class of projection-based signatures that are structured by a generalized \textbackslashmathcalR\_fm-transform model. To this end, we will firstly prove the \textbackslashmathcalR\_fmˆ-transform in accordance with reflectional symmetry detection. Then different corresponding \textbackslashmathcalR\_fm-signatures of binary shapes are evaluated in order to determine which the corresponding exponentiation of the \textbackslashmathcalR\_fm-transform is the best for the detection. Experimental results of detecting on single/compound contour-based shapes have validated that the exponentiation of 10 is the most discriminatory, with over 2.7% better performance on the multiple-axis shapes in comparison with the conventional one. Additionally, the proposed detector also outperforms most of other existing methods. This finding should be recommended for applications in practice.

The object detection tasks based on edge computing have received great attention. A common concern hasn't been addressed is that edge may be unreliable and uploads the incorrect data to cloud. Existing works focus on the consistency of the transmitted data by edge. However, in cases when the inputs and the outputs are inherently different, the authenticity of data processing has not been addressed. In this paper, we first simply model the tampering detection. Then, bases on the feature insertion and game theory, the tampering detection and economic incentives mechanism (TDEI) is proposed. In tampering detection, terminal negotiates a set of features with cloud and inserts them into the raw data, after the cloud determines whether the results from edge contain the relevant information. The honesty incentives employs game theory to instill the distrust among different edges, preventing them from colluding and thwarting the tampering detection. Meanwhile, the subjectivity of nodes is also considered. TDEI distributes the tampering detection to all edges and realizes the self-detection of edge results. Experimental results based on the KITTI dataset, show that the accuracy of detection is 95% and 80%, when terminal's additional overhead is smaller than 30% for image and 20% for video, respectively. The interference ratios of TDEI to raw data are about 16% for video and 0% for image, respectively. Finally, we discuss the advantage and scalability of TDEI.

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.

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.

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.

In Wireless Sensor Networks (WSNs), energy and security are two critical concerns that must be addressed. Because of the scarcity of energy, several security measures are restricted. For secure data routing in WSN, it becomes vital to identify insider packet drop attacks. The trust mechanism is an effective strategy for detecting this assault. Each node in this system validates the trustworthiness of its neighbors before transmitting packets, ensuring that only trust-worthy nodes get packets. With such a trust-aware scheme, however, there is a risk of false alarm. This work develops an adaptive trust computation model (TCM)which is implemented in our already proposed Chimp Optimization Algorithm-based Energy-Aware Secure Routing Protocol (COA-EASRP) for WSN. The proposed technique computes the optimal path using the hybrid combination of COA-EASRP and AODV as well as TCM is used to indicate false alarms in detecting selfish nodes. Our Proposed approach provides the series of Simulation outputs carried out based on various parameters

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.

Security incident handling and response are essen-tial parts of every organization's information and cyber security. Security incident handling consists of several phases, among which digital forensic analysis has an irreplaceable place. Due to particular digital evidence being recorded at a specific time, timelines play an essential role in analyzing this digital evidence. One of the vital tasks of the digital forensic investigator is finding relevant records in this timeline. This operation is performed manually in most cases. This paper focuses on the possibilities of automatically identifying digital evidence pertinent to the case and proposes a model that identifies this digital evidence. For this purpose, we focus on Windows operating system and the NTFS file system and use outlier detection (Local Outlier Factor method). Collected digital evidence is preprocessed, transformed to binary values, and aggregated by file system inodes and names. Subsequently, we identify digital records (file inodes, file names) relevant to the case. This paper analyzes the combinations of attributes, aggregation functions, local outlier factor parameters, and their impact on the resulting selection of relevant file inodes and file names.

For a long time, online attacks were regarded to pose a severe threat to web - based applications, websites, and clients. It can bypass authentication methods, steal sensitive information from datasets and clients, and also gain ultimate authority of servers. A variety of ways for safeguarding online apps have been developed and used to deal the website risks. Based on the studies about the intersection of cybersecurity and machine learning, countermeasures for identifying typical web assaults have recently been presented (ML). In order to establish a better understanding on this essential topic, it is necessary to study ML methodologies, feature extraction techniques, evaluate datasets, and performance metrics utilised in a systematic manner. In this paper, we go through web security flaws like SQLi, XSS, malicious URLs, phishing attacks, path traversal, and CMDi in detail. We also go through the existing security methods for detecting these threats using machine learning approaches for URL classification. Finally, we discuss potential research opportunities for ML and DL-based techniques in this category, based on a thorough examination of existing solutions in the literature.

Compressive radar receiver has attracted a lot of research interest due to its capability to keep balance between sub-Nyquist sampling and high resolution. In evaluating the performance of compressive time delay estimator, Cramer-Rao bound (CRB) has been commonly utilized for lower bounding the mean square error (MSE). However, behaving as a local bound, CRB is not tight in the a priori performance region. In this paper, we introduce the Ziv-Zakai bound (ZZB) methodology into compressive sensing framework, and derive a deterministic ZZB for compressive time delay estimators as a function of the compressive sensing kernel. By effectively incorporating the a priori information of the unknown time delay, the derived ZZB performs much tighter than CRB especially in the a priori performance region. Simulation results demonstrate that the derived ZZB outperforms the Bayesian CRB over a wide range of signal-to-noise ratio, where different types of a priori distribution of time delay are considered.

The Compressive Sensing (CS) has wide range of applications in various domains. The sampling of sparse signal, which is periodic or aperiodic in nature, is still an out of focus topic. This paper proposes novel Sparse Spasmodic Sampling (SSS) techniques for different sparse signal in original domain. The SSS techniques are proposed to overcome the drawback of the existing CS sampling techniques, which can sample any sparse signal efficiently and also find location of non-zero components in signals. First, Sparse Spasmodic Sampling model-1 (SSS-1) which samples random points and also include non-zero components is proposed. Another sampling technique, Sparse Spasmodic Sampling model-2 (SSS-2) has the same working principle as model-1 with some advancements in design. It samples equi-distance points unlike SSS-1. It is demonstrated that, using any sampling technique, the signal is able to reconstruct with a reconstruction algorithm with a smaller number of measurements. Simulation results are provided to demonstrate the effectiveness of the proposed sampling techniques.

The demand for increasing flexibility use in power systems is stressed by the changing grid utilization. Making use of largely untapped flexibility potential is possible through novel flexibility markets. Different approaches for these markets are being developed and vary considering their handling of transaction schemes and relation of participating entities. This paper delivers the conceptual development of a holistic system architecture for the realization of an interregional flexibility market, which targets a market based congestion management in the transmission and distribution system through trading between system operators and flexibility providers. The framework combines a market mechanism with the required supplements like appropriate control algorithms for emergency situations, cyber-physical system monitoring and cyber-security assessment. The resulting methods are being implemented and verified in a remote-power-hardware-in-the-loop setup coupling a real world low voltage grid with a geographically distant real time simulation using state of the art control system applications with an integration of the aforementioned architecture components.

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.

The increasing number of vehicles registered demands for safe and secure carparks due to increase in vehicle theft. The current Automatic Number Plate Recognition (ANPR) systems is a single authentication system and hence it is not secure. Therefore, this research has developed a double authentication system by combing ANPR with a Quick Response (QR) code system to create ANPR-DAS that improves the security at a carpark. It has yielded an accuracy of up to 93% and prevents car theft at a car park.

Wireless ad hoc networks are characterized by dynamic topology and high node mobility. Network attacks on wireless ad hoc networks can significantly reduce performance metrics, such as the packet delivery ratio from the source to the destination node, overhead, throughput, etc. The article presents an experimental study of an intrusion detection system prototype in mobile ad hoc networks based on machine learning. The experiment is carried out in a MANET segment of 50 nodes, the detection and prevention of DDoS and cooperative blackhole attacks are investigated. The dependencies of features on the type of network traffic and the dependence of performance metrics on the speed of mobile nodes in the network are investigated. The conducted experimental studies show the effectiveness of an intrusion detection system prototype on simulated data.

This paper conducts the development of the key technologies of the legal case management information system considering QoS optimization. The designed system administrator can carry out that the all-round management of the system, including account management, database management, security setting management, core data entry management, and data statistics management. With this help, the QoS optimization model is then integrated to improve the systematic performance of the system as the key technology. Similar to the layering in the data source, the data set is composed of the fields of the data set, and contains the relevant information of the attribute fields of various entity element categories. Furthermore, the designed system is analyzed and implemented on the public data sets to show the results.

With the development of society, people have higher and higher requirements for the quality of life, and the management of legal cases has become more and more important. In this case, the research on how to realize electronization and networking has become the inevitable demand of the current information age. Therefore, this paper designs and develops the legal case management information system based on J2EE. Firstly, this paper introduces the related technologies of J2EE, then expounds the importance of legal case management informatization, and designs the legal case management information system according to the technical framework of J2EE. Finally, the performance of the system is tested. The test results show that the load capacity of the system is strong, the response time is 2–4 seconds, the resource utilization is relatively low, and the number of concurrent users is maintained at about 150. These show that the performance of the system fully meets the needs of legal case information management.

Large-scale onboarding of industrial cyber physical systems requires efficiency and security. In situations with the dynamic addition of devices (e.g., from subcontractors entering a workplace), automation of the onboarding process is desired. The Eclipse Arrowhead framework, which provides a platform for industrial automation, requires reliable, flexible, and secure device onboarding to local clouds. In this paper, we propose a device onboarding method in the Arrowhead framework where decentralized authorization is provided by Power of Attorney. The model allows users to subgrant power to trusted autonomous devices to act on their behalf. We present concepts, an implementation of the proposed system, and a use case for scalable onboarding where Powers of Attorney at two levels are used to allow a subcontractor to onboard its devices to an industrial site. We also present performance evaluation results.

This paper presents a Ph.D. research plan that focuses on solving the existing problems in risk management of critical infrastructures, by means of a novel DevSecOps-enabled framework. Critical infrastructures are complex physical and cyber-based systems that form the lifeline of a modern society, and their reliable and secure operation is of paramount importance to national security and economic vitality. Therefore, this paper proposes DevSecOps technology for managing risk throughout the entire development life cycle of such systems.