Attack Attempts Research Articles

Characteristics of suicidal attacks in pregnancy; a multicenter study

Background Maternal death, fetal death and suicidal attack (SA), each one of these topics are an important public health problem. A suicide attack attempt during pregnancy includes all these important issues together and requires additional attention. Some factors may show regional differences such as suicidal method, distribution of attempts according to the gestational week and the most common preferred drugs. The predetermination of these variables may allow taking preventive measures and advantages can be gains on maternal-fetal health. Methods The data of pregnant women who were admitted to 3 different university hospital emergency departments in same city between 2015 and 2020 after a SA was investigated. SAs features and distribution of attacks based on variables such as age, gravidity and gestational week etc. was recorded. In addition, obstetric/non-obstetric injuries and pregnancy outcomes was also analyzed. Results The mean age of 78 cases was 26.9 ± 6.4 (17–44) years. SAs were detected most frequently in the 1st trimester (42.3%) and at least in the 3rd trimester (20.5%). The most preferred SA method (89.7%) was high-dose drug intake. The most commonly preferred drugs were paracetamol, iron/folic acid replacement therapy drugs and antidepressants. Conclusions Pregnant women are at risk of SA, especially in the first trimester. Contrary to popular belief, 75% of pregnant women who have SA do not have a known psychiatric disease diagnosis before. Therefore, psychiatric evaluation should be a part of routine pregnancy follow-up examination, especially in the first trimester. In this way, pregnant women which have increased risk factors for SA such as alcohol addiction, unwanted pregnancy, and depressive mood can be detected early. In this special patient group, the most commonly SA method is high-dose drug intake. Knowing the high dose treatments for frequently used drugs such as paracetamol by emergency physicians and obstetricians, educating medical staff about treatments can save additional time for mother and fetus and can be a life saver approach.

Detection and Mitigation of Known and Unknown DDoS Attacks on Advanced Metering Infrastructure Systems in the Nigeria Using Hybrid Machine Learning (Ai) Techniques

The enormous rise of network traffic and its diversity on the Internet have posed new and serious obstacles for detecting network attack activity. Distributed denial of service (DDoS) attack is designed to restrict genuine users from accessing a service for an extended period of time. In this attack, the attacker attempts to compromise a large number of hosts to transmit a large volume of traffic to genuine users. Detecting DDoS attacks is difficult and complicated, primarily different DDoS attacks do not common characteristics through which they can be detected. DDoS attacks are very difficult to fight or trace due to their distributed nature, and automated software tools for conducting DDoS attacks are freely available. This paper proposed a DDoS detection model based on hybrid machine learning technique on AMI systems in the Nigeria Utility Business. It has been discovered that detecting unknown DDoS attacks is difficult to analyze as sometimes the IP packets and header are encrypted. This study proposed a combination of Support Vector Machine (SVM) and Artificial Neural Network (ANN) to detect unknown attacks. The AES 256 algorithm has been employed to decrypt the encrypted IP header. Keywords: ANN, SVM, DDoS attack, AES algorithm, AMI DOI: 10.7176/NCS/13-04 Publication date: May 31 st 2022

Paradigm Shift in Adaptive Cyber Defense for Securing the Web Data: The Future Ahead

Web Applications are becoming more sophisticated to cater the ever-growing demand of data processing and computing. Fast technological advancements in web engineering not only facilitate data intensive and high-performance computing, but also raise serious concerns on security. Cyber threats are also ramping up at the equal pace and attackers are now more organised and equipped with high-end servers. The Data over Web needs to be more authenticated and reliable. Data Provenance-aware methods are capable of identification of data breaches and manipulation through various attacks. They analyse underlying data for the potential threats to ensure protection against various attacks. Cyber Security Practitioners are witnessing severe issues in securing the Web Data and applications as the security risks are growing rapidly due to the sudden eruption in internet usage due to the pandemic in the last few years. People and organisations are relying more on Internet and web applications than ever before. The efforts for securing the web data on such a massive scale is premature to counter the ever-evolving attack attempts. Nature-inspired Cyber Security (NICS) facilitates the development and implementation of robust defensive mechanisms which are more adaptive and highly tolerant to online malicious programs. These methods are also capable of dealing with the common algorithmic issues like incompleteness and uncertainty of information and to provide a high-level security mechanism by effectively implementing the bio-inspired methodologies like deception, and camouflage etc. This article will attempt to explore the effectiveness of NICS in web data and application security to provide smart security methods.

Towards Attack to MemGuard with Nonlocal-Means Method

An adversarial example is the weakness of the machine learning (ML), and it can be utilized as the tool to defend against the inference attacks launched by ML classifiers. Jia et al. proposed MemGuard, which applied the idea of adversarial example to defend against membership inference attack. In a membership inference attack, the attacker attempts to infer whether a particular sample is in the training set of the target classifier, which may be a software or a service whose model parameters are unknown to the attacker. MemGuard does not tamper the training process of the target classifier, meanwhile achieving better tradeoff between the privacy and utility loss. However, many defenses of the adversarial example have been proposed, which decreases the effectiveness of the adversarial example. Inspired by the defenses of the adversarial example, we try to attack the MemGuard. As a result, we utilize the nonlocal-means method to do the attack by using the inherent relationship between neighbor entries to remove the added noise. Due to the low dimensionality of the confidence score vector, our attack avoids the high computation overhead of the nonlocal-means method. Besides, we use practical datasets to test our attack, and the experimental results demonstrate the effectiveness of our attack.

The Efficacy of Airpower in Counterinsurgency

Since 2001, the United States has relied upon air strikes in its global counterterrorism campaign against insurgencies throughout the world. With advances in air strike technology, public opinion growing increasingly intolerant of deployments of ground forces abroad, and the proliferation of terrorist groups around the world, the use of air strikes appears to be the future of US counterterrorism policy. This study tests the efficacy of air strikes as a counterinsurgency tool by geocoordinating US air strike data and merging it with three major databases on conflict events to assess whether air strikes influence the rate of insurgent attacks. Our analysis reveals that air strikes reduce insurgents’ capacity to carry out attacks over the long term. At the same time, air strikes carry a short-term, provocative effect on insurgent attacks when they result in civilian fatalities. Finally, there is some evidence that air strikes increase attack attempts, but these attempts are not always successful, nor directed toward government forces.

Gradient Techniques To Predict Distributed Denial-Of-Service Attack

A distributed denial-of-service (DDoS) attack attempts to prevent people from accessing a server. A website may become inaccessible due to a DDoS attack because the server is inundated with fake requests and cannot handle real ones. A DDoS attack affects a large number of computers. Attackers employ a zombie network, which is a collection of infected machines on which the attacker has hidden the denial-of-service attacking application to carry out a DDoS attack. The MATLAB 2018a simulator was used in this study for training. Additionally, during design, the knowledge discovery dataset (KDD) was cleaned and the values of attacks were incorporated. A neural network model was subsequently developed, and the KDD was trained using a recursive artificial neural network. This network was developed using five distinct training algorithms: 1) Fletcher–Powell conjugate gradient, 2) Polak–Ribiére conjugate gradient of, 3) resilient backpropagation, 4) gradient conjugation with Powell/Beale restarts, and 5) gradient descent algorithm with variable learning rate. The artificial neural network toolset in MATLAB was used to investigate the detection of DDoS attacks. The conjugate gradient with Powell/Beale restart algorithm had a success rate of 99.9% and a training time of 00:53. This inquiry uses the KDD-CUP99 dataset. Has a better level of accuracy, according to the results

Adversarial Game Against Hybrid Attacks in UAV Communications With Partial Information

Unmanned aerial vehicle (UAV) communications are vulnerable to smart attacks, where the attacker can change the attack mode (e.g., eavesdropping and jamming) via smart radio devices. To ensure secure transmissions against hybrid attacks, UAVs may transmit confidential information and misleading information alternately. In this paper, we consider a dynamic anti-hybrid attack framework with trajectory optimization, where both a UAV and an attacker attempt to find their optimal trajectories without knowing the information type and the attack mode of each other. Given the major challenge due to incomplete knowledge (i.e., each agent knows only its own information), we establish an adversarial game with partial-observation feature to formulate an optimization problem, and propose a counterfactual regret minimization learning scheme to achieve the correlated equilibrium for both the UAV and attacker. Simulation results validate the superiority of our scheme over a benchmark in UAV communication scenarios with partial information.

Dynamic face authentication systems: Deep learning verification for camera close-Up and head rotation paradigms

The popularity of face-authentication systems has also generated interest in the study of malicious authentication attempts, such as face spoofing attacks. In this study we investigate two dynamic face-authentication challenges: the camera close-up, and head-rotation paradigms. For each paradigm we developed an ML-based face-authentication system that performs the tasks of liveness detection and face verification. In order to generate structured data-representations from the videos collected in the wild, we designed feature representations that extract three-dimensional and spatial characteristics of a face, while also capturing the particular liveness cues of the requested challenge-based movements. Furthermore, a set of Neural-Network models that employ Convolutional Neural Networks and Siamese Neural Network architectures were proposed. To train and test our models we collected a dataset of 177 live videos recorded by 41 different subjects and a set of 243 attack attempts in uncontrolled scenarios. The resulting NN models yield good performance against multiple types of media-based attacks (printed-attacks, screen-attacks, 2D-masks, videos acquired from public social media, deep fakes). The camera close-up system presented an overall liveness detection accuracy of 97.7% and a face verification accuracy of 97.6%. On the other hand, the evaluation of the head-rotation system resulted in a liveness detection accuracy of 92.4% and face verification accuracy of 98.1%. Face authentication methods based on dynamic user-challenges constitute a scalable approach that does not require specialized hardware to increase the security of face authentication systems in realistic usage scenarios. The proposed methods not only make it harder for attackers to generate spoofing attacks, but they also constitute a practical complement to static biometric authentication systems.

A Lightweight Scheme for Mitigating RPL Version Number Attacks in IoT Networks

Internet of Things (IoT) systems incorporate a multitude of resource-limited devices typically interconnected over Low Power and Lossy Networks (LLNs). Robust IP-based network routing among such constrained IoT devices can be effectively realized using the IPv6 Routing Protocol for LLN (RPL) which is an IETF-standardized protocol. The RPL design features a topology maintenance mechanism based on a version numbering system. However, such a design property makes it easy to initiate Version Number (VN) attacks targeting the stability, lifetime, and performance of RPL networks. Thus the wide deployment of RPL-based IoT networks would be hindered significantly unless internal routing attacks such as the VN attacks are efficiently addressed. In this research work, a lightweight and effective detection and mitigation solution against RPL VN attacks is introduced. With simple modifications to the RPL functionality, a collaborative and distributed security scheme is incorporated into the protocol design (referred to as CDRPL). As the experimental results indicated, it provides a secure and scalable solution enhancing the resilience of the protocol against simple and composite VN attacks in different experimental setups. CDRPL guaranteed fast and accurate attack detection as well as quick topology convergence upon any attack attempt. It also efficiently maintained network stability, control traffic overhead, QoS performance, and energy consumption during different scenarios of the VN attack. Compared to other similar approaches, CDRPL yields better performance results with lightweight node-local processing, no additional entities, and less communication overhead.

Conceptual and categorical apparatus of information security of Ukraine in ensuring national security

The article examines the main concepts of information security of Ukraine, which affect national security, taking into account the existing conditions. The beginning of a full-scale invasion of Russian troops on the territory of Ukraine forced our entire country not only to go into martial law, but also to radically change the tactics of actions in order to preserve its integrity. The insidiousness of the enemy lies not only in the invasion, but also in the peculiarities of the struggle, which is reflected in the spread of false information. Thus, the enemy uses the tactics of a hybrid war, in which, in addition to cruise missiles, armed offensives, and psychological pressure, citizens of not only temporarily occupied territories are subjected. Due to active attempts of information attacks, one of the main threats to information security is disinformation of the entire society. World trends indicate that the active deepening of the spheres of professional activity, education, science, and technology is connected with the dynamic development of social life. The current state of information technology development should correspond to the dynamics of the development of state and private information structures. As a result, Ukraine actively implements all requirements for providing all spheres of life with highly qualified personnel in the field of information provision. However, the enemy's active attempts to attack information resources for their own purposes force us to examine the concept of information security from another angle. Negative informational influence occurs due to the use of the latest technologies of influence through the dissemination of known false information that causes internal conflicts of a national and religious nature, undermines the faith of citizens in state administration, promotes the beginning of internal armed conflicts, violates the sovereignty and territorial integrity of the state. Timely detection of negative information impact requires scientific substantiation of the concept of information security and the ability to assess all possible threats and impacts in order to minimize their consequences. The relevance of using such an approach is due to the search and application of current models for the detection of informational influences.

Guarding a Convex Target Set From an Attacker in Euclidean Spaces

This letter addresses a two-player target defense game in an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -dimensional Euclidean space where an attacker attempts to enter a closed convex target set whereas a defender strives to capture the attacker beforehand. We propose a generalized differential game-based solution model that can not only encompass recent work associated with similar problems whose target sets have simple low-dimensional geometric shapes, but can also address problems that involve nontrivial geometric shapes of high-dimensional convex target sets. The game has two value functions, each of which is derived in a semi-analytical form including an optimization problem that admits a unique solution. If the latter solutions have closed-form expressions, the barrier surface that divides the state space of the game into the winning sets of players can be analytically constructed. For the case where there exists no closed-form solution but the target set has a smooth boundary, the map between the target boundary and the projection of barrier on the game space is derived. By using the Hamilton-Jacobi-Isaacs equation, we show that our semi-analytical state feedback strategies always constitute the unique saddle point of the game. We verify our solutions with related work and provide numerical simulation results.

Security enhanced dynamic bandwidth allocation algorithm against degradation attacks in next generation passive optical networks

The dynamic bandwidth allocation (DBA) algorithm is highly impactful in improving the network performance of gigabit passive optical networks (GPON). However, reports delineating its vulnerability to certain attacks can be found in the literature, thus raising concerns. A degradation attack manipulates the transmission control protocol (TCP) congestion control algorithm, which may impact the received bandwidth of targeted network users. Here, the absence of electronics within the passive splitter renders attack identification in GPONs a significant challenge. Therefore, the current study aimed to propose a secured DBA mechanism known as security enhanced DBA capable of overcoming this particular threat. A detection phase served as a critical component for sensing and subsequently mitigating any abnormal behaviors observed among optical network units (ONUs). Upon identification of the attacker, penalties were imposed to deter the next attack attempt and reestablish the fairness to previously attacked ONUs. The simulation findings revealed throughput improvement of up to 63% due to the security feature offered by the mechanism. Besides, significant improvements for the upstream delay performance recorded at 52%, 60%, and 65% for traffic containers (TCONT) TCONT2, TCONT3, and TCONT4, respectively, were observed in comparison to the non-secure DBA mechanism. Hence, the integration of the security mechanisms in DBA renders it possible to avoid any exploitation of GPON vulnerability in hacking other users’ bandwidth.

Generator of Slow Denial-of-Service Cyber Attacks.

In today’s world, the volume of cyber attacks grows every year. These attacks can cause many people or companies high financial losses or loss of private data. One of the most common types of attack on the Internet is a DoS (denial-of-service) attack, which, despite its simplicity, can cause catastrophic consequences. A slow DoS attack attempts to make the Internet service unavailable to users. Due to the small data flows, these attacks are very similar to legitimate users with a slow Internet connection. Accurate detection of these attacks is one of the biggest challenges in cybersecurity. In this paper, we implemented our proposal of eleven major and most dangerous slow DoS attacks and introduced an advanced attack generator for testing vulnerabilities of protocols, servers, and services. The main motivation for this research was the absence of a similarly comprehensive generator for testing slow DoS vulnerabilities in network systems. We built an experimental environment for testing our generator, and then we performed a security analysis of the five most used web servers. Based on the discovered vulnerabilities, we also discuss preventive and detection techniques to mitigate the attacks. In future research, our generator can be used for testing slow DoS security vulnerabilities and increasing the level of cyber security of various network systems.

Enhanced image encryption algorithm by quantum key distribution

Quantum Key Distribution (QKD) is one type of Quantum Cryptography (QC) which is based on quantum mechanics fundamentals such as Heisenberg’s uncertainty principle and No-cloning theory. The usage of QKD warns the legitimate communicated parties to any attack attempt and this is the most interesting security parameter. Therefore, QKD provides unconditional secure communication method and supports a powerful encryption scheme. The combination between classical communication and QKD creates a new technique called semi quantum key distribution SQKD. Unfortunately, SQKD increases the schemes complexity and requires two steps for ciphering, scramble and encryption. In this paper an enhance image encryption algorithm is proposed based on QKD that eliminates most of the drawbacks of SQKD. The proposed algorithm is simpler than other encryption schemes as it exploits only one encryption step based on the power and the randomness of the generated secret key, which decreases the chance to be cracked. The correctness and efficiency of the proposed algorithm are validated by numerical simulations.

Efficient Implementation of Radon Transform and Encryption Techniques for Cancelable Speaker Identification

This paper introduces three cancelable speaker identification techniques based on the spectrogram estimation of speech signals subjected to either chaotic encryption process, or RSA algorithm in addition to Radon transform to produce cancelable templates instead of the original speech signals. The resulting transformed versions of the voice biometrics are stored in the server instead of the original biometrics. Therefore, the users' privacy can be protected well. It is evident from the obtained results that the proposed techniques are secure, reliable and practical. They have good encryption and ability to generate cancelable templates. These characteristics lead to good performance. The proposed cancelable speaker identification techniques are evaluated under the influence of Additive White Gaussian Noise (AWGN) with different strengths. This makes them more accurate in identifying the users and also more resistant to attack attempts. In addition, security is enhanced through maintaining the confidentiality of the processed data. In the experimental results, evaluation metrics such as Equal Error Rate (EER), False Rejection Rate (FRR), and False Acceptance Rate (FAR) are used to assess the performance of the proposed techniques. In addition, the genuine, impostor distributions, Receiver Operating Characteristic (ROC) curve and area under the ROC curve for the proposed techniques are estimated for better evaluation and comparison.

Denial-of-Service Attack

A denial-of-service (DoS) is any type of attack where the attackers (hackers) attempt to prevent legitimate users from accessing the service. In a DoS attack, the attacker usually sends excessive messages asking the network or server to authenticate requests that have invalid return addresses. The network or server will not be able to find the return address of the attacker when sending the authentication approval, causing the server to wait before closing the connection. When the server closes the connection, the attacker sends more authentication messages with invalid return addresses. Hence, the process of authentication and server wait will begin again, keeping the network or server busy. This paper presents how DoS attacks are performed, the techniques used, the attack tools, the prevention of an attack and the some of the famous DoS attacks.

A dual-mode successive approximation register analog to digital converter to detect malicious off-chip power noise measurement attacks

An on-chip noise monitor using a dual-mode analog to digital converter (ADC) is developed to detect the insertion of off-chip components as malicious attempts of power noise measurement attacks. A two-step sampling scheme selecting either synchronous or asynchronous clocking enables both real-time monitoring and high-resolution diagnosis of power supply noise, respectively. The monitor detects the change in power-line impedance and assumes the happening of physical contacts by an attacker’s device. A wide-band ADC with the bandwidth of 1 GHz facilitates the analysis of on-chip captured waveforms and the recognition of potentially inserted devices. Fabricated in 65 nm CMOS, the on-chip noise monitor is examined for the detectability of series resistors as well as parallel capacitors that are intentionally inserted on power lines. The experiments demonstrated the detection of a power current sensor in series to power lines, and also the attachment of an oscilloscope probe, through the analysis of on-chip captured power noise waveforms.

Food web conservation vs. strategic threats: A security game approach

Multi-species conservation is of critical concern in ecosystem management science. In this context, modeling the effect of strategic threats on decision-making is a challenging problem that has not been sufficiently addressed. Using a security game approach, this paper investigates the optimal conservation of a food web against a strategic threat. The model builds upon the non-cooperative Stackelberg game, wherein conservator (defender) and adversary (attacker) play as leader and follower, respectively. The objective of the defender is to preventively maximize the entire web reliability, under financial and ecological constraints. The defender optimally manipulates the populations of an optimal subset of species to achieve this. In contrast, the attacker attempts to maximize web unreliability by decreasing the population of selected species, using limited resources. A meta-heuristic algorithm is developed to compute the equilibrium strategy, and the model is validated through numerical examples. Additionally, in a scenario-based approach, it is examined how the defense and attack strategies, as well as food web reliability, change as the population of keystone species change. The results also show that the combinational use of mathematical optimization and food web-specific conservation prioritization indices yields a practical tool for food web conservation prioritization. The results specifically yield theoretical insights into how to optimally control trophic cascade effects due to changing keystone species populations. A step-wise methodology is proposed to implement the model.

Deep Learning Algorithms for Detecting Denial of Service Attacks in Software-Defined Networks

In Software-Defined Networking (SDN) the controller is the only entity that has the complete view on the network, and it acts as the brain, which is responsible for traffic management based on its global knowledge of the network. Therefore, an attacker attempts to direct malicious traffic towards the controller, which could lead to paralyze the entire network. In this work, Deep Learning algorithms are used to protect the controller by applying high-security measures, which is essential for the continuous availability and connectivity in the network. Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are proposed to recognize and prevent the intrusion attacks. We evaluate our models using a recently released dataset (InSDN dataset). Finally, our experiments manifest that our models achieve very high accuracy for the detection of Denial of Service (DoS) attacks. Thus, a significant improvement in attack detection can be shown compared to one of the benchmarking state of the art approaches.

Effective Controlling Scheme to Mitigate Flood Attack in Delay Tolerant Network

Conventional routing protocols breaks down in opportunistic networks due to long delays, frequent disconnectivity and resource scarcity. Delay Tolerant Network (DTN) has been developed to cope with these mentioned features. In the absence of connected link between the sender and the receiver, in DTN mobile nodes replicate bundles and work cooperatively to improve the delivery probability. Malicious nodes may flood the network as possible by a huge number of unwanted bundles (messages) or bundle replicas which waste the limited resources. DOS (Denial of Service) attack especially Flooding attack attempt to compromise the availability service of the network. Traditional congestion control strategies are not suitable for DTN, so developing new mechanisms to detect and to control flooding attack is a major challenge in DTN network. In this paper, we presented a comprehensive overview of the existing solutions for dealing with flooding attack in delay tolerant network, and we proposed an effective controlling mechanism to mitigate this threat. The main goal of this mechanism is first to detect malicious nodes that flood the network by unwanted messages, and then to limit the damage caused by this attack. We also ran a large number of simulations with the ONE simulator to investigate how changing buffer capacity, message lifetime, message size, and message replicas affect DTN network performance metrics.