Target Of Attacks Research Articles

CardioDefense: Defending against adversarial attack in ECG classification with adversarial distillation training

In clinics, doctors rely on electrocardiograms (ECGs) to assess severe cardiac disorders. Owing to the development of technology and the increase in health awareness, ECG signals are currently obtained by using medical and commercial devices. Deep neural networks (DNNs) can be used to analyze these signals because of their high accuracy rate. However, researchers have found that adversarial attacks can significantly reduce the accuracy of DNNs. Studies have been conducted to defend ECG-based DNNs against traditional adversarial attacks, such as projected gradient descent (PGD), and smooth adversarial perturbation (SAP) which targets ECG classification; however, to the best of our knowledge, no study has completely explored the defense against adversarial attacks targeting ECG classification. Thus, we did different experiments to explore the effects of defense methods against white-box adversarial attack and black-box adversarial attack targeting ECG classification, and we found that some common defense methods performed well against these attacks. Besides, we proposed a new defense method based on adversarial distillation training (named CardioDefense) which comes from defensive distillation and can effectively improve the generalization performance of DNNs. The results show that our method performed more effectively against adversarial attacks targeting on ECG classification than the other baseline methods, namely, adversarial training, defensive distillation, Jacob regularization, and noise-to-signal ratio regularization. Furthermore, we found that our method performed better against PGD attacks with low noise levels, which means that our method has stronger robustness.

Robustness of the International Wheat Trade Network

Grain is the material basis for human survival and development, wheat is one of the three significant grains, and the robustness of the wheat trade has seriously affected world grain security. In the paper, we analyze the robustness of the International Wheat Trade Network (IWTN) from three perspectives: global, regional, and national. We quantify the topological properties and evolution characteristics of IWTN from 1987 to 2020, and analyze the stability of IWTN using random attacks and target attacks. The import satisfaction of major wheat trading countries and trade communities is quantified through import stability indicators. Firstly, the results show that IWTN satisfies the scale-free property and that global wheat trade transactions continue to increase. Secondly, IWTN under random attack forms a “convex” curve regarding network efficiency. It shows that IWTN has better resistance to external shocks, and higher robustness. However, under the targeted attack, the network collapses when the node failure rate reaches 40%. Regardless of the attack mode, the decreasing trend of network efficiency and connectivity is the same in different years as the number of failed nodes gradually increases, and no year shows outstanding stability. Thirdly, different communities in the wheat trade are more satisfied with the wheat imports of their internal countries, and most of the wheat trade occurs within the same trading community. Fourthly, among the top five global wheat importers in 2020, Italy and Turkey have diversified sources of imports, but trade imports are relatively concentrated. In contrast, China, Egypt and Indonesia have relatively balanced wheat trade imports from their respective trading countries and are less at risk of being affected in the event of supply risks.

Ocena propozycji dodania art. 27a kk

The presented article concerns an extremely important and difficult issue, especially in the practice of applying national law. This is due to the fact that the right to express one’s beliefs in public and resulting from the professed religion has not been the subject of interest of the national legislator in the penal act so far. Meanwhile, it is of wide interest in the practical application of the human rights system. When analyzing the achievements of international tribunals and the Polish Constitutional Tribunal, the actual place of the proposed change should be assessed. The legislator, rationalizing legal provisions, should directly introduce norms implementing the constitutional standard of international protection of human rights within a given legal system. Introduction of Art. 27a to the Penal Code, together with the negative element added to it, is the implementation of the postulates resulting from the achievements of international tribunals. It is precisely protection against religious aggression that justifies stronger state activity. Poland became a front-line state in a hybrid war. With over 3 million refugees from Ukraine, most of them non-Catholic, it is obvious that they will be the target of attacks from outside. It is clear that there may be physical or similar religious attacks on a national basis. It is impossible not to notice that there are current threats from the outside, so the legislator is even obliged to counteract them.

IDLTM-DMT: Intelligent Deep Learning based Trust Management with Decision Making Tool for Healthcare Internet of Things and Big Data Environment with Neutrosophic Set Analysis

Over the last few years development of Internet of Things (IoT) devices and communication technologies have resulted in the massive generation of health-related data. In the context of healthcare, IoT offers several advantages, including being able to observe patients very closely and using data for analytics. A major challenging issue that exists in the usage of IoT and big data in the medical field is security. As healthcare data is highly vulnerable and becomes a target for attacks, there are significant privacy issues related to the usage of big data analytics. Besides, implementing new data analysis tools and strategies for handling big data decision-making is a major issue. The capability to examine this amount of data is a significant aspect of big data in health care. For resolving these issues, this paper presents a new intelligent deep learning-based trust management with decision making tool (IDLTM-DMT) for IoT healthcare big data environments, incorporating Neutrosophic Set Analysis (NSA). The proposed IDLTM-DMT model enables IoT devices to gather healthcare data. The IDLTM-DMT model involves a DL based bidirectional long short-term memory (BiLSTM) model for vulnerability detection and thereby identifies the malicious traffic in the Network. Hadoop MapReduce is used for handling big data and a decision-making tool using Deep Stacked Auto Encoder (DSAE) is used for the classification of diseases that exist in big data. To optimize the DSAE model's hyperparameters and improve classification performance, the Sandpiper Optimization (SPO) Algorithm is employed. Neutrosophic Set Analysis is integrated to manage the indeterminacy and inconsistency of the data, enhancing the decision-making process. Extensive experimental analysis is conducted on the EEG Eye State Dataset, with results analyzed using various performance measures. The findings indicate that the proposed method achieves improved accuracy compared to existing methods, demonstrating the effectiveness of incorporating Neutrosophic Set Analysis in IoT healthcare big data environments.

The role of the police in counter-terrorist activities

Security is an interdisciplinary concept that is important for the existence and development of individual people and entire societies. Many events can cause them anxiety, and this undoubtedly includes a terrorist act, which is inherently evil. Terrorism develops along with the development of civilization, which forces individual countries to constantly improve and modernize the methods of combating this threat. Although, according to research, Polish society is not afraid of terrorist attacks, in the current political situation it should be assumed that Poland may be the target of such attacks. State authorities have created an anti-terrorist system to ensure citizen safety. In which an important role in anti-theorist and counter-terrorist activities was assigned to the Police.

MALWARE DETECTION : A FRAMEWORK FOR REVERSE ENGINEERED ANDROID APPLICATIONS THROUGH MACHINE LEARNING ALGORITHMS

Today, Android is one of the most used operating systems in smartphone technology. This is the main reason, Android has become the favorite target for hackers and attackers. Malicious codes are being embedded in Android applications in such a sophisticated manner that detecting and identifying an application as a malware has become the toughest job for security providers. In terms of ingenuity and cognition, Android malware has progressed to the point where they're more impervious to conventional detection techniques. Approaches based on machine learning have emerged as a much more effective way to tackle the intricacy and originality of developing Android threats. They function by first identifying current patterns of malware activity and then using this information to distinguish between identified threats and unidentified threats with unknown behavior. This research paper uses Reverse Engineered Android applications’ features and Machine Learning algorithms to find vulnerabilities present in Smartphone applications. Our contribution is twofold. Firstly, we propose a model that incorporates more innovative static feature sets with the largest current datasets of malware samples than conventional methods. Secondly, we have used ensemble learning with machine learning algorithms such as AdaBoost, SVM, etc. to improve our model's performance. Our experimental results and findings exhibit 96.24% accuracy to detect extracted malware from Android applications, with a 0.3 False Positive Rate (FPR). The proposed model incorporates ignored detrimental features such as permissions, intents, API calls, and so on, trained by feeding a solitary arbitrary feature, extracted by reverse engineering as an input to the machine.

Communication Strategy of Terrorism at Major Sporting Events

Sport has historically played a vital role in fostering positive values and fostering connections between cultures and nations globally. However, over the past 50 years, sporting events have increasingly become targets for terrorist attacks, posing significant security challenges. To explore this issue, we conducted an integrative review, viewing terrorism at sporting events as an ecosystem with various elements and interactions. Our goals were threefold: to evaluate terrorist attacks at major sporting events since the 1972 Olympics, analyzing terrorists' communication strategies, and to identify trends for detecting and preventing terrorism. Our findings contribute to unraveling the complexities surrounding the motivations of perpetrators, the vulnerabilities of the sporting environment, and the effectiveness of counterterrorism measures. In essence, it is a comprehensive examination that seeks to enhance knowledge, inform policy, and contribute to the development of strategies aimed at ensuring the safety and security of sporting events

DDoS attacks detection using different decision tree algorithms

In today's world, the banking sector, government organizations, and various users in the finance and insurance sectors have grown exponentially. In such situations, they become primary targets for attackers. The main focus of these attackers is to disrupt services for legitimate users. Recently, attackers have targeted banks in Ukraine during the Russia- Ukraine war, causing a shortage of money in banks and making it difficult for people to withdraw funds. These types of attacks fall under the category of Distributed Denial of Service (DDoS) attacks. The primary objectives of these DDoS attacks are to gain financial control and damage the reputation of the affected organization or country. The purpose of this paper is to detect DDoS attacks using various Decision Tree Classifiers in Machine Learning algorithms. We utilized the 'caret' package in R, which is well-known for its Classification and Regression Techniques. We split the KDD'99 dataset based on the outcome variable. We employed the 'rpart' method to classify the dataset using CART and C4.5 algorithms. Experimental results indicate that our classification methods achieve a better accuracy rate compared to other decision tree methods.

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The question of the terrorist activities of the russian federation during the full-scale war against Ukraine aimed at destroying the country�s economic potential and achieving the political objectives of this armed conflict is considered. An analysis of the main targets of attacks on strategic objects of Ukraine, the consequences of these attacks, and ways of countering terror on the part of the aggressor country has been carried out.

Unsafe at any Bandwidth: Towards Understanding Risk Factors for Ransomware in Higher Education

United States higher education institutions host an assortment of services that are accessible via public IP addresses. The wide variety of network services and the important personal and institutional data stored on such services make higher education institutions particularly desirable targets for attackers. This study analyses the vulnerabilities found through Shodan scans on these networks, in conjunction with institutional characteristics data taken from the National Center for Education Statistics (NCES), to examine correlations between an institution’s characteristics and the vulnerabilities found on its networks. By exploring this data, the study aims to bring awareness to the current state of higher education institution network security and determine vulnerability trends between certain institutions. Our analysis reveals that most institutions have many medium impacts but highly exploitable vulnerabilities, with most being on Apache HTTP servers. We also present that the most significant indicators of an institution’s vulnerability are its enrolment and yearly total expenses. We investigate how smaller institutions have lower numbers of vulnerabilities, but their vulnerabilities have the potential for higher impact. We conclude that there is a significant chance of ransomware risk in US higher educational institutions.

Terrorism and institutional soft target attacks in Nigeria: the way forward

Almost five decades have passed since after the Nigerian civil war that claimed closed to five million lives between Nigerian and Biafran separatists. Yet, rather than learn lessons learnt to forestall peace, it is worrisome that the escalation of terror against state and its institutions has taken heightened dimensions at various times. It is this worry that gave vent to this theoretical paper that utilizes content analysis to explore relevant parameters that are factorial to this dangerous escalation of terror in Nigeria particularly targeting its institutions as helpless soft targets for attack. Consequently, the paper questioned institutional alienation, inequality, and unhealthy politicization of religion and education as largely responsible for the increasing terror attacks. Drawing from these insights, the paper using induction and deduction tools, outlined major ways forward in addressing the menace of terror. Inclusive governance, income redistribution, secularism as a functional state policy and liberalization of access to education according literature, were strong factors that point to solutions that can lead productive engagement and inclusive involvement in governance and decision-making process in order to ultimately encourage the de-escalation of terror attacks against state and its institutions.

Side-Channel Attack Analysis on In-Memory Computing Architectures

In-memory computing (IMC) systems have great potential for accelerating data-intensive tasks such as deep neural networks (DNNs). As DNN models are generally highly proprietary, the neural network architectures become valuable targets for attacks. In IMC systems, since the whole model is mapped on chip and weight memory read can be restricted, the pre-mapped DNN model acts as a “black box” for users. However, the localized and stationary weight and data patterns may subject IMC systems to other attacks. In this paper, we propose a side-channel attack methodology on IMC architectures. We show that it is possible to extract model architectural information from power trace measurements without any prior knowledge of the neural network. We first developed a simulation framework that can emulate the dynamic power traces of the IMC macros. We then performed side-channel leakage analysis to reverse engineer model information such as the stored layer type, layer sequence, output channel/feature size and convolution kernel size from power traces of the IMC macros. Based on the extracted information, full networks can potentially be reconstructed without any knowledge of the neural network. Finally, we discuss potential countermeasures for building IMC systems that offer resistance to these model extraction attack.

Contrastive Learning for Robust Android Malware Familial Classification

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware family analysis can be achieved through contrastive learning. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and other types of malware. Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps can be obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IFDroid</i> and perform extensive evaluations on two datasets. Experimental results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IFDroid</i> is superior to state-of-the-art Android malware familial classification systems. Moreover, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IFDroid</i> is capable of maintaining a 98.4% F1 on classifying 69,421 obfuscated malware samples.

Returning the War to Russia: Drones and Discrimination in the Defense of Ukraine

AbstractThis essay assesses the morality of Ukraine's use of drones to attack targets inside Russia. Following its invasion by Russian forces, Ukraine has had a just cause to wage a war of self-defense. However, its efforts to achieve that cause remain subject to moral limits. Even a state that has been unjustly attacked may not, for example, respond by deliberately targeting the attacking state's civilian population. To do so would violate the jus in bello principle of discrimination. The essay first describes how drone technology has frequently enabled long-range strikes against Russian military assets as well as other targets inside cities. It then explains why it would be morally wrong for Ukraine to attack its enemy's population centers. First, Russian civilians are not liable to attack, and this nonliability is undiminished by the injustice of Russia's invasion or by any in bello wrongs committed by the Russian military. Second, attacking Russian cities with drones would arguably achieve little or no self-defensive benefit for Ukraine, and it could even be counterproductive.

Machine Recognition of DDoS Attacks Using Statistical Parameters

As part of the research in the recently ended project SANET II, we were trying to create a new machine-learning system without a teacher. This system was designed to recognize DDoS attacks in real time, based on adaptation to real-time arbitrary traffic and with the ability to be embedded into the hardware implementation of network probes. The reason for considering this goal was our hands-on experience with the high-speed SANET network, which interconnects Slovak universities and high schools and also provides a connection to the Internet. Similar to any other public-facing infrastructure, it is often the target of DDoS attacks. In this article, we are extending our previous research, mainly by dealing with the use of various statistical parameters for DDoS attack detection. We tested the coefficients of Variation, Kurtosis, Skewness, Autoregression, Correlation, Hurst exponent, and Kullback–Leibler Divergence estimates on traffic captures of different types of DDoS attacks. For early machine recognition of the attack, we have proposed several detection functions that use the response of the investigated statistical parameters to the start of a DDoS attack. The proposed detection methods are easily implementable for monitoring actual IP traffic.

Training And Recognition of The Importance of Cybersecurity For Umkm In Entering The Digital Market

Borobudur district is a district located in Magelang district with 20 districts. Dusun Ngaran I is one of the dunes in Borobudur, with a number of UMKM perpetrators more than 15 UMKM. Asoy Warung UMKM is a UMKM in Dusun ngaran I that shakes in the field of processing of soy milk snakes. UMKM itself is a productive enterprise that is considered by individuals or enterprises that have met the criteria as a micro enterprise. The Covid-19 pandemic encourages UMKM to conduct business activities through digital platforms. So UMKM becomes a potential target for cyber security attacks. Lack of education and introduction to cyber security, many UMKM perpetrators are not aware of the threat of cyberattacks.The urgency of this activity is that the attacks targeting UMKM indirectly are the effect of the Covid-19 pandemic that requires to shift the process of transactions mainly sales and sales and promotions using digital media and the Internet. The education and training provided is to recognize digital assets, security defence updates, password management, threats of phishing and malware attacks as well as backup and data recovery. The objective of this activity is to provide training training and recognition of the importance of cyber capabilities for UMKM in entering the digital market. The methods carried out are training and support to know the digital asset and the application of the basics of cybersecurity in preventing cyber attacks.

A case for the inclusion of oculocutaneous albinism as a skin-related Neglected Tropical Disease

Oculocutaneous albinism (OCA) is genetically transmitted. In this paper we advocate for this disease to be included in the NTD list of the WHO. OCA type 2 is the most common form of albinism in sub-Saharan Africa, with a prevalence of 1 in 7900 among the Bamileke of Cameroon, 1 in 3900 in South Africa and 1 in 1100 among the Ibos of Nigeria, as compared to a prevalence of 1 in 10,000 among African Americans and 1 in 36,000 among White Americans and Europeans. The medical problems related to ophthalmological aspects (poor visual acuity, ametropia, nystagmus, photophobia) and dermatological aspects of albinism (sensitivity to UV rays from the sun and development of skin cancers) are well known. However, their management is often challenging for persons with albinism in sub-Saharan Africa because of their financial burden and the difficulty of accessing medical specialists. In many African countries, persons with albinism are also very often the subject of social, cultural, medical, moral and economic discrimination, which can limit their access to education, employment and community life. They are considered 'white Africans', intermediary and incomplete, with innate powers for good and evil. This particularity has made persons with albinism the targets of mutilations and/or ritual attacks for the purposes of using their body parts in the preparation of charms to bring good luck, health or prosperity. On 13 June 2013, as a result of lobbying by the Canadian NGO Under the Same Sun and African albinism associations, United Nations bodies including UNESCO and the WHO (World Health Organization) responded and a Resolution addressing the discrimination and attacks was voted in. The date has since become International Albinism Awareness Day and is celebrated on a different theme each year with great energy and impact, especially by French, English and Portuguese speaking albinism associations across sub-Saharan Africa. In 2015 the Human Rights Council created the position of Independent Expert on Albinism to better collect and analyse data on the rights of persons with albinism around the world, and especially in countries where ritual attacks occur. The data collected by albinism associations and the authorities thus go directly to the UN Human Rights Directorate. Despite this international attention to the attacks on persons with albinism, one of the biggest threats is skin cancer, which very often leads to early death. In 2022, the WHO launched a strategic framework for the control and management of neglected skin-related neglected tropical diseases - an additional reason to include oculocutaneous albinism as an NTD. Although the focus is currently limited to dermatoses of an infectious nature, we argue here for the integration of oculocutaneous albinism among NTDs because the deadliness of these carcinomas in sub-Saharan Africa is well-known and has been examined in a number of medical publications. Here, we propose that oculocutaneous albinism in sub-Saharan Africa be classified as an NTD to help people with albinism have access to health, economic, social and cultural rights.

무력충돌시 문화재 파괴 행위에 대한 국제형사책임의 과제

Since the 1990s, situations in which culturally significant heritage sites serve as symbolic targets for intentional attacks, leading to the 'destruction of enemy communities,' have frequently occurred. The International Criminal Tribunal for the former Yugoslavia (ICTY), established to prosecute serious violations of international humanitarian law within the former Yugoslav territories, addresses prominent cases of intentional attacks and destruction of cultural property during the armed conflict, specifically targeting the annihilation of ethnic identity. This article examines the implications and limitations of international legal norms regarding the protection of cultural heritage during armed conflicts, focusing on the Dubrovnik Old Town shelling incident, a representative case of cultural property destruction prosecuted by the ICTY. Firstly, the intentional destruction of cultural heritage is undeniably a significant violation of values that the international community must protect. The pursuit of individual criminal responsibility for the destruction of cultural heritage within the international criminal tribunal signifies a substantial advancement in international efforts to protect cultural heritage during armed conflicts. The negative impact of the intentional destruction of cultural property on human dignity and universal human values is undeniable. However, ensuring effective protection of cultural property in the current normative context, where attacks on cultural property for military purposes may be justified, remains challenging. Secondly, military necessity, rooted in customary international law, limits attacks on enemy forces to military objectives, focusing on the centralization of enemy forces, balancing the military necessity of concentrated attacks on enemy forces with humanitarian considerations to protect civilian objects. However, determining what qualifies as a military objective can be challenging, as it depends on the circumstances of combat and operations. Regardless of whether the principle of proportionality is applicable, evaluating the military necessity from the perspective of cultural heritage protection is crucial. ICTY recognizes that even when military necessity justifies attacks on civilian objects, the intentional destruction of cultural heritage can have a significant impact on the psychological well-being of civilians and may upset the balance with military superiority. Thus, careful consideration is necessary when assessing the military necessity in cases where the protection of cultural property, with a higher need for protection than general civilian objects, is involved. Moreover, even if a target has dual-use, being considered a military objective, evaluating proportionality in the attack is essential to prevent harm to civilians, in accordance with the principle of proportionality. In conclusion, the intentional destruction of cultural heritage should be treated as a threat to the common values of the international community, necessitating appropriate legal and policy responses in line with the actual circumstances of the armed coflict. Military commanders should not only view attacks on cultural heritage as inevitable collateral damage but also recognize the gravity of such acts as war crimes and consider them strategically within military operations.

Performance Evaluation of Deep Learning Techniques in The Detection of IOT Malware

Internet of Things (IoT) equipment is rapidly being used in a variety of businesses and for a variety of reasons (for example, sensing and collecting data from the environment in both public and military settings). Because of their expanding involvement in a wide range of applications and their rising computational and processing capabilities, they are a viable attack target for malware tailored to infect specific IoT devices. This study investigates the potential of detecting IoT malware using different deep learning techniques: the classic feedforward neural network (FNN), convolutional neural networks (CNN), long short-term memory (LSTM), and recurrent neural networks (RNN). The proposed method analyses the execution operation codes of IOT app sequences using modern NLP (natural language processing) methods. The current work utilized an IoT application dataset with 500 malware (collected from the IOTPOT dataset) and 500 goodware samples to train the proposed algorithms. The trained model is tested against 2971 fresh IoT malware and goodware samples. The samples were input into deep learning models, and performance metrics were obtained. The results demonstrate that the RNN model had the best accuracy (99.19%) in detecting fresh malware samples. On the other hand, the results were compared by the time required for training; the CNN model shows that it could achieve high accuracy (98.05%) with less training time. A comparison with various deep learning classifiers demonstrates that the RNN and CNN techniques produce the best results.

COUNTERTERRORISM SECURITY OF THE STATE IN THE CURRENT CONTEXT

The article analyzes previous research and highlights the features of the counterterrorism security of the state as a component of the national security system.&#x0D; The article aims at identifying problems with ensuring counterterrorism security during the Russian Federation's war against Ukraine, analyzing Ukrainian legislation in this area, and outlining the ways of forming the counterterrorism security system of the state in the current context.&#x0D; The biggest terrorist threat to Ukraine is the large-scale armed aggression by the Russian Federation and the hybrid war, which aims not only to occupy certain territories of Ukraine but also to completely eliminate Ukrainian statehood. This war widely employs terrorist methods including the intimidation and killing of civilians, the use of powerful weapons against populated areas, and the destruction of critical civilian infrastructure.&#x0D; The problems above call for a comprehensive reconsideration of the counterterrorism security of the state, given the need to significantly enhance the protection of possible sites of terrorist attacks and to fulfill the tasks of implementing the national security priorities under the 2020 National Security Strategy of Ukraine Human Security Equals the Country’s Security.&#x0D; The perpetration of terrorist acts by the Russian Federation against the national critical infrastructure has become a striking challenge for Ukraine, the consequences of which are difficult to overestimate. During the armed aggression, the Ukrainian state has been confronted with new types of terrorist activities, in particular, psychological and nuclear terrorism. All this requires strengthening the counterterrorism capacities for protecting critical infrastructure and other targets of terrorist attacks, primarily through practical and research-based approaches to the counterterrorism security of the state.&#x0D; Based on the results of the study, the authors conclude that there is a need to significantly enhance the institutional strengthening of the national counterterrorism system and to revise Ukrainian legislation in this area. The article outlines interrelations between counterterrorism security and other components within the national security system of Ukraine, including security of the state, military security and defense, public security, cybersecurity, nuclear and radiation security, etc. The authors propose to establish an effective system of counterterrorism security of the state, primarily with respect to improving its legislative basis, which calls for the development of the Counterterrorism Strategy in Ukraine that would become the foundation of state policy in countering terrorism.