Multiple Attacks Research Articles

Contextual Deep Semantic Feature Driven Multi-Types Network Intrusion Detection System for IoT-Edge Networks

Recent years have witnessed an exponential rise in wireless networks and allied interoperable distributed computing frameworks, where the different sensory units transfer real-world event data to the network analyzer for run-time decisions. There exists an array of applications employing edge- internet of things (Edge-IoT) where the edge nodes collect local data to perform real-time decisions. However, the at-hand edge-IoT systems being decentralized, infrastructure-less, and dynamic remain vulnerable to man-in-the-middle attacks, intrusion, denial of service attacks, etc. Though in the past, numerous efforts were made towards intrusion detection in IoT networks, the major approaches focused merely on standalone intrusion detection, and therefore their scalability towards multiple attack detection remains unaddressed. On the contrary, applying a unit intrusion detection system for each type of attack can impose resource exhaustion and delay. Recently authors have used deep learning methods like convolutional neural network (CNN), and long- and short-term memory (LSTM) to perform learning-based intrusion detection. However, being reliant on merely local features its reliability remains suspicious. Such methods ignore long-term dependency problems that limit their efficacy in intrusion detection in temporal Edge-IoT network traffic. With this motivation, in this paper, a contextual deep semantic feature-driven multi-type intrusion detection model (CDS-MNIDS) is proposed for Edge-IoT networks. The proposed CDS-MNIDS model at first performs network traffic segmentation from the temporal network traces obtained from the network gateway. Subsequently, the node’s dynamic features including the node’s address, packet size, transmission behavior, etc., are processed for Word2Vec encoding, followed by a cascaded deep network-based learning and prediction. The CDS-MNIDS model embodied a cascaded deep network encompassing LSTM and bidirectional LSTM networks, where the first extracted local features. At the same time, the latter obtained contextual features from the input local feature vector. The extracted local and contextual features were projected to the global average pooling layer followed by the fully connected layer that in conjunction with the Softmax layer performed multi-class classification.

Collaborative Motivation Framework Leveraging Similar Interest Behavior in Semantic Web Applications

With the growth of web technology, the semantic web offers a promising framework for online knowledge collaboration. However, trust issues can undermine users' willingness to collaborate, reduce the frequency of interaction and collaboration efficiency. This paper introduces a super node-based trust management model designed to enhance semantic networks by linking nodes through trust relationships. The model exploits the synergistic incentives of similar interest behaviours to achieve a steady construction of trust relationships. We propose a similarity filtering algorithm that calculates the similarity to filter out false, misleading, or unfair information effectively. Through simulations, we compare our model with RRGRET, Surework, and community-based approaches, and the results show that our model has good network properties, while also resisting multiple malicious attacks and guaranteeing collaboration success. This research contributes to optimizing node relationships within semantic networks and strengthening network robustness against interference.

Characterization of Destructive Nodes and Analysing their Impact in Wireless Networks

Mobile Ad hoc Networks (MANETs) are being used to meet new requirements for efficiency and coordination in a variety of new public and residential contexts. Certain essential functions, including as resource management among network nodes, trust-based routing, and security for network maintenance, are not performed as well as they should because of the dynamic nature of wireless networks. Ad-hoc networks can also be attacked from different tiers of a network stack, and they are susceptible to secure communications. Destructive nodes have the ability to alter or reject routing parameters. They may also provide bogus routes in an attempt to intercept source data packets and pass them through. To handle the complexity arising from secure data exchange, some protocols have been developed. However, not all attack types can be detected and eliminated by a secure protocol in every scenario. Since security is not a feature that is built into MANETs, new secure wireless protocols need to concentrate on these issues. Thus, the analysis of destructive nodes' characteristics and effects on wireless networks in this research paper examined the behaviour of multiple attacks, their activities through neighbour selection, the establishment of paths from sources to destinations, and the dissemination of attack presence detection information to regular devices during path discovery and data transmission mechanisms. In order to categorize as legitimate, nodes must be constructed with safe transmission knowledge to provide trustworthy communication, validation, honesty, and privacy.

Secure Wireless Sensor Networks: A Weighted K-NN and RNN-based Approach for Attack Detection and Localization

In the realm of wireless sensor networks (WSNs), precise localization of sensor nodes and attack detection has emerged as a significant challenge. Accurate localization of sensor nodes and detection of attacks is crucial for enhancing the network lifetime and preventing the data from unauthorized access. However, prior research has encountered various obstacles, such as high localization error, more time consumption, and poor detection performance. To overcome these difficulties, this research proposes a novel Recurrent Crossover-based Dynamic Differential (RC-DD) algorithm for effective localization and attack detection in WSN. The proposed model utilizes a weighted K-Nearest Neighbor (KNN) with Mahalanobis distance for accurately localizing the position of the sensor nodes within the network. Moreover, the Recurrent Neural Network (RNN) is employed for effectively detect multiple attacks, and Dynamic Differential Annealed Optimization (DDAO) with crossover strategy is implemented for fine-tuning the RNN’s hyperparameter, thereby enhancing the attack detection accuracy. The experimental outcome shows that the proposed RC-DD framework effectively localizes the sensor nodes and detects the attacks with a higher accuracy of 98.9% compared to existing methodologies. Additionally, the proposed framework minimizes localization error and energy consumption, underscoring its potential to enhance the security and lifetime of the network.

Multiple PUE Attack Detection in Cooperative Mobile Cognitive Radio Networks

The Mobile Cognitive Radio Network (MCRN) are an alternative to spectrum scarcity. However, like any network, it comes with security issues to analyze. One of the attacks to analyze is the Primary User Emulation (PUE) attack, which leads the system to give the attacker the service as a legitimate user and use the Primary Users’ (PUs) spectrum resources. This problem has been addressed from perspectives like arrival time, position detection, cooperative scenarios, and artificial intelligence techniques (AI). Nevertheless, it has been studied with one PUE attack at once. This paper implements a countermeasure that can be applied when several attacks simultaneously exist in a cooperative network. A deep neural network (DNN) is used with other techniques to determine the PUE’s existence and communicate it with other devices in the cooperative MCRN. An algorithm to detect and share detection information is applied, and the results show that the system can detect multiple PUE attacks with coordination between the secondary users (SUs). Scenarios are implemented on software-defined radio (SDR) with a cognitive protocol to protect the PU. The probability of detection (PD) is measured for some signal-to-noise ratio (SNR) values in the presence of one PUE or more in the network, which shows high detection values above 90% for an SNR of -7dB. A database is also created with the attackers’ data and shared with all the SUs.

Interactomic Analyses and a Reverse Engineering Study Identify Specific Functional Activities of One-to-One Interactions of the S1 Subunit of the SARS-CoV-2 Spike Protein with the Human Proteome

The S1 subunit of SARS-CoV-2 Spike is crucial for ACE2 recognition and viral entry into human cells. It has been found in the blood of COVID-19 patients and vaccinated individuals. Using BioGRID, I identified 146 significant human proteins that interact with S1. I then created an interactome model that made it easier to study functional activities. Through a reverse engineering approach, 27 specific one-to-one interactions of S1 with the human proteome were selected. S1 interacts in this manner independently from the biological context in which it operates, be it infection or vaccination. Instead, when it works together with viral proteins, they carry out multiple attacks on single human proteins, showing a different functional engagement. The functional implications and tropism of the virus for human organs/tissues were studied using Cytoscape. The nervous system, liver, blood, and lungs are among the most affected. As a single protein, S1 operates in a complex metabolic landscape which includes 2557 Biological Processes (GO), much more than the 1430 terms controlled when operating in a group. A Data Merging approach shows that the total proteins involved by S1 in the cell are over 60,000 with an average involvement per single biological process of 26.19. However, many human proteins become entangled in over 100 different biological activities each. Clustering analysis showed significant activations of many molecular mechanisms, like those related to hepatitis B infections. This suggests a potential involvement in carcinogenesis, based on a viral strategy that uses the ubiquitin system to impair the tumor suppressor and antiviral functions of TP53, as well as the role of RPS27A in protein turnover and cellular stress responses.

Održivost demokratskog sustava naspram viralnih kampanja dezinformacija

Democracy as an experience of political organization is today undergoing multiple external attacks, and is the object of many internal criticisms. In this regard, one can imagine a City besieged by centrifugal forces, not least of which is the rise of the phenomenon of disinformation. There are many cities, like Kyiv or Dubrovnik, which are or have in the past been the target of disinformation campaigns that preceded and accompanied the assault of which they were the victims. Therefore, it seems necessary to review who are the actors and the vectors of disinformation today, to then examine who are the main targets and victims. Ultimately, this communication, in addition to the description of the phenomenon itself, also aims to review the responses that are provided by public actors to combat this scourge threatening the proper functioning of democratic deliberation.

[formula omitted] predictive control for 2-D Roesser model under multiple denial-of-service attacks

In this paper, we concentrate on H∞ predictive control issue for Roesser-type two-dimensional (2-D) system under multiple denial of service attacks (DoSs). The research will be realized through the following considerations. Firstly, a general multi-DOSs description is formulated under Roesser-type 2-D framework. Then the event-based 2-D triggered strategy is constructed to compensate the occurred multiple DoSs network attacks, which helps to save network resources and improve the network utilization. On this basis, some innovative multi-steps 2-D predictive controller is designed, and then, Roesser-type 2-D predictive control system can be obtained accordingly. For the predictive control system, we propose the detailed analysis scheme for considering H∞ predictive control issue. Furthermore, we derive the 2-D controller gain design algorithm, and the H∞ robustness analysis criterion in solvable matrix constraints. To conclude the paper, a chemical process example is given to illustrate the effectiveness and practicability of the provided method and controller design.

Graph protection under multiple simultaneous attacks: A heuristic approach

This work focuses on developing a meta-heuristic approach to protect network nodes from simultaneous attacks, specifically addressing the k-strong Roman domination problem. The objective is to assign integer weights to the nodes, representing the number of stationed armies, to meet protection constraints while minimizing the total number of armies. A network is protected if it can repel any simultaneous attack on k nodes. A node is protected if it can defend itself or if a neighboring node provides an army while retaining at least one army for self-defense. This problem formulation can be used in practical scenarios, e.g. developing counter-terrorism strategies or in coping with supply chain disruptions. The problem is difficult as even verifying the feasibility of a single solution generally requires an exponential time. Two exact approaches are proposed in the literature but applicable to small random graphs. For larger graphs, we propose an effective variable neighborhood search, where the feasibility of a solution is verified by introducing the concept of relaxed feasibility. Experiments are conducted with random networks from the literature and two introduced ad-hoc wireless and real-world networks. Extensive experimental evaluations show the robustness of the proposed approach compared to the existing approaches from the literature by significantly outperforming them in all three benchmark sets. Furthermore, we demonstrate the practical application of the proposed variable neighborhood search approach, where its solution is used to position fire stations within the city so that simultaneous fires can be extinguished efficiently while reducing the number of required fire trucks.

REHAS: Robust and Efficient Hyperelliptic Curve‐Based Authentication Scheme for Internet of Drones

ABSTRACTInternet of Drones (IoD) is one of the most beneficial and has many versatile applications like Surveillance and Security, Delivery and Logistics, Environmental Monitoring, Agriculture, and so forth. The IoD network is crucial for collecting sensitive data like geo‐coordinates, vehicle traffic data, and property details while surveying the various deployment locations in smart cities. The communication between users and drones can be compromised over insecure wireless channels by multiple attacks such as Man‐in‐the‐middle‐attack, Denial of Service, and so forth. Many schemes have already been propounded in the field of IoD. Still, many of them cannot address the resource constraints problem of drones, and existing protocols have higher computation and communication costs. Therefore, this paper has proposed a robust and efficient Hyper‐Elliptic Curve‐based authentication scheme (REHAS), which provides a session key for secure communication. Artificial Identities are generated using a hash function and random numbers. Fuzzy Extractor is used for user biometric authentication, which makes the smart device secure when lost. HECC is used with a smaller bit size of 80 bits rather than ECC of 160 bits. The security of the REHAS has been ensured using Scyther simulation. Furthermore, the resilience, safety, and robustness of REHAS are ensured by Informal security analysis. Lastly, a comparative study of the REHAS has been performed with other related Authentication and key agreement (AKA) protocols regarding communication cost, Computation cost, and security features, demonstrating that REHAS incurred less computation cost (6.7171 ms), communication overhead (1696 bits), and energy consumption (22.5 mJ) than other existing AKA schemes.

Asynchronous thruster fault detection for unmanned marine vehicles under multiple attacks

Asynchronous thruster fault detection for unmanned marine vehicles under multiple attacks

Replay attack detection based on smooth watermarking and encoding and decoding

The proposed detection method in this study, which involves smooth watermarking, encoding, and decoding, aims to address replay attacks in time-delay systems during communication. Implementing an event-triggered technique helps save communication resources and cut down on unnecessary information transmission. A weighted sliding average method is employed to smooth the system’s watermark influenced by time delay, decreasing unnecessary fluctuations and boosting anti-attack resilience. The encoding and decoding systems were ingeniously designed to increase the invisibility of the smooth watermarking, making it more sensitive in detecting replay attacks. Multiple attack scenarios are meticulously examined, after which rigorous simulation tests are carried out to prove the proposed scheme’s reliability, effectiveness, and substantial superiority.

Crypto-Spatial: A New Direction in Geospatial Data

Abstract. Geospatial technology involves the use of maps, satellite imagery, and spatial databases to collect, analyze, and visualize geographic data. Understanding the link between blockchain and geospatial technology is crucial as it opens the door to innovation and progress in both fields. “The use of Blockchain as a system for managing and verifying geospatial data necessitates accuracy, integrity, and trustworthiness”(Pengxiang et al 2022). Improving data integrity, promoting collaboration, and revolutionizing applications in the geospatial domain are all possible through the combination of these two technologies. The World Wide Web (WWW) has expanded connections worldwide, but it also makes networks susceptible to multiple attacks from various anonymous sources. Packets of fixed or variable sizes are used for transmitting data across nodes. Data is encrypted using secure algorithms at the application level, then packetized and sent at lower levels in the OSI architecture. The encrypted data can be obtained by an intruder by organizing the data contents of each packet once they have access to all the packets. Intruders may also attempt to break the secured algorithm used by the sender. This sophisticated approach to Data Security is the content and order of data, ensuring that unauthorized individuals are unable to discern sensitive information during data transmission. The primary objective of this paper is to introduce a novel spatial encryption technique designed to enhance the security of data transfers within networks. This paper, founded on a comprehensive literature review, endeavors to propose a research project focused on exploring futuristic solutions, particularly within the post-quantum era.

An evacuation model considering pedestrian group behavior under violent attacks

In recent years, violent attacks have garnered significant attention from the international community and gradually evolved into a global issue. In responding to such emergencies, effective emergency evacuation is an essential means to ensure public safety. In order to explore the pedestrian evacuation dynamics in violent attacks and understand the behavioral characteristics of individuals and the dynamic interactions of groups during the evacuation process, this paper proposes an agent-based cellular automata evacuation model. First, each agent is assigned several internal attributes such as life value, panic value, and movement speed, and the assignments fully take into account the heterogeneity of individuals. Next, by setting the attack strategy of attackers and the evacuation mechanism of pedestrians, the complex system is modeled from a microscopic perspective. Finally, the effects of critical factors on the pedestrian evacuation process, such as panic coefficient, crowd structure, group ratio, attack distance and intensity, attacker location and number, and counterattack probability, are discussed through simulation. The results show that the tension caused by moderate panic is beneficial, but excessive panic will lead to increased evacuation time and fatalities. Meanwhile, the group behavior among pedestrians causes them to move relatively lagging, and the evacuation efficiency decreases as the group scale increases. Additionally, increases in attack intensity, attack distance, and attacker number all result in more casualties, and the consequences are most severe when multiple attackers operate separately, but counterattacks by pedestrians can significantly improve the overall safety of evacuation. Our study can provide some reference and basis for emergency management under violent attacks.

Pioneering Innovations in Biological Weed Control: A Comprehensive Review

The biological method of weed control is an effective option. Before implementing bio-agents, there is a need for a better understanding of the mechanisms involved in interactions by multiple agents against weeds. This covers knowledge of how plants integrate and prioritize their responses to multiple attackers. Across the globe, terrestrial ecosystems are known to be characterized by a huge diversity of species and a corresponding diversity of interactions between these species. These include species of different trophic levels of habitats on the same food chain. Plants undoubtedly play significant role as intermediaries in interactions between the insect and microbial populations with which they are involved. Plant interactions brought forth by one species can have cascading impacts on other species, influencing their abundances and the composition of communities. Several research efforts are underway to develop highly effective biological control agents to reliably control a problematic broad spectrum of weeds. Sustainable and ecosystem-compatible weed management for controlling weeds by biological means, such as insects, bio-agents, myco-herbicides and harmful Rhizobacteria. Their interaction with cultural practices and allelochemicals are some biological components of sustainable agriculture. Hence, biological agents enhance efficiency and restrict the detrimental effect of weeds compared to weeds being left alone. The challenge is to identify appropriate microbe/bioagents, which reduce weed growth. Logically, biological weed management is the only way to control weeds in eco-friendly weed management.

Kinetic Modeling of Brilliant Blue Discoloration by Ozonation

This paper presents the results of investigations on the kinetic modeling of Brilliant Blue FCF (BB) discoloration reactions in aqueous solutions with different ozone concentrations and pH conditions. Kinetic studies involve knowledge of the structure and properties of dye and ozone, as well as of the experimental conditions. In general, scientists admit that the predominant oxidation pathway is direct (by free oxygen atoms) or indirect (by free hydroxyl radicals); this will depend on influencing factors such as the physicochemical properties of the dye, the pH of the aqueous solution, ozone concentration, reaction time, and the contact mode with/without stirring. In this experimental research, two pathways were chosen following CBB = f(t)—1. a constant dye concentration and different ozone concentrations, in the concentration range of 100–250 mg/L, in three pH media (acidic, neutral, and basic), with and without stirring; 2. a constant concentration of ozone and different dyes in the concentration range of 2.5–10 mg/L, under the conditions of point 1. With the obtained experimental data, the curves CBB = f(t) were drawn and processed according to the integral method of classical kinetics, based on first- and second-order equations. Unfortunately, this simple procedure did not give any results for the pH values studied. The rate constants were negative, and/or the reaction order depended on the initial conditions. Due to its structure, the BB dye has several chromophore groups, and thus multiple attack centers, resulting in several oxidation by-products, which is why the 1H-NMR spectrum was recorded for the discoloration of BB with ozone. Since the stoichiometry of the overall oxidation reaction, as well as the relationship between the rate constant and the reaction conditions mentioned above, is not known, a kinetic model based on mass transfer coupled with a chain reaction in the bulk liquid phase was proposed and successfully tested at pH = 7. This research approach also involves the consolidation of the theoretical bases of the ozonation process through the kinetic study carried out, as well as the proposal of a kinetic model. These systematics lead to results that are applicable to other aqueous systems that are impure with dyes, allowing for generalizations and the development of the field, ensuring the sustainability of the research.

DP-Poison: Poisoning Federated Learning under the Cover of Differential Privacy

Federated learning (FL) enables resource-constrained node devices to learn a shared model while keeping the training data local. Since recent research has demonstrated multiple privacy leakage attacks in FL, e.g., gradient inference attacks and membership inference attacks, differential privacy (DP) is applied to serve as one of the most effective privacy protection mechanisms. Despite the benefit DP brings, we observe that the introduction of DP also brings random changes to client updates, which will affect the robust aggregation algorithms. We reveal a novel poisoning attack under the cover of DP, named the DP-Poison attack in FL. Specifically, the DP-Poison attack is designed to achieve four goals: 1) maintaining the main task performance; 2) launching a successful attack; 3) escaping the robust aggregation algorithms in FL; and 4) keeping the effectiveness of DP privacy protection. To achieve these goals, we design multiple optimization goals to generate DP noise through a genetic algorithm. The optimization ensures that while the benign updates change randomly, the malicious updates can change towards the global model after adding the DP noise, so that it is easier to be accepted by the robust aggregation algorithms. Extensive experiments show that DP-Poison achieves a nearly 100% attack success rate while maintaining the proposed four goals.

A Bio-Inspired Optimal Feature with Convolutional Ghostnet based Squeeze Excited Deep-Scale Capsule Network for Intrusion Detection

Intrusion detection plays a significant part in ensuring information security, and its primary purpose is to correctly detect multiple attacks on the network. Due to the high Internet usage, the vulnerability in the network is also increasing. The messages sent over the network are attacked by intruders, and therefore, it is necessary to identify the intrusion in the network. The proposed study introduces a bio-inspired feature selection-based technique for intrusion detection using a convolutional Ghost-Net-based deep-scale capsule network. First, inputs are collected from three different datasets: CICIDS, NBIoT, and UNSW NB15. After data collection, the data is pre-processed to improve classification accuracy. The pre-processing phase includes various phases such as data cleaning, min-max normalization, and imputation of missing values. Then, the pre-processed data is provided for the next step of feature selection to reduce the feature dimensionality problem and computational complexity. Feature selection is performed utilizing a modified Gazelle optimization algorithm (Mod-GO). Finally, the network intruders are classified based on the selected characteristics using a hybridized network called Convolutional GhostNet-based Squeeze Excited Deep-Scale Capsule Network (CGN_SEDSCapsNet). Then, to improve the efficiency of the proposed classifier, the Enhanced Artificial Humming Bird (EAH) algorithm is used to optimize the parameters. Thus, the output of the network detects the intrusion. The proposed method is verified experimentally, and the performance metrics are analyzed. The simulation is performed with the Python tool. Experimental verification proves the proposed CGN_SEDSCapsNet model offers better accuracy than existing techniques.

SERAV Deep-MAD: Deep Learning-Based Security–Reliability–Availability Aware Multiple D2D Environment

The tremendous growth of internet-connected devices can lead to network congestion, making it essential to incorporate new technologies to optimize performance. Thus, Device to Device(D2D) communication has been deemed as an emerging technology that can be used to communicate efficiently. However, establishing a secure and reliable mechanism for Device-to-Device (D2D) communication that ensures security, reliability, and availability poses a significant challenge. To tackle these issues a novel deep learning-based security-reliability-availability aware multiple D2D environment (SERAV Deep-MAD) has been proposed for Secure D-2-D Communication in a Fog environment. The proposed method utilizes a Fully Homomorphic Quantum Diffie Hellman Encryption (FHQDHE) to secure the data while transmitting. The proposed method utilizes the novel secure Quantum Diffie Hellman key exchange (QDHKE) technique for sharing the key to transform the plain data into cipher data, then applies FH operations on the encrypted data before transferring it to the Fog environment. Whenever a client requests data from the Fog, the Fog provider verifies the client's access rights. Moreover, the Attention-based Bi-GRU model is utilized to categorize whether the device is non-attack or attack, if the data is attacked then the proposed model classifies multiple attacks. The NS2 Simulator is used to verify and validate the proposed SERAV Deep-MAD model, and resiliency analysis is done to assess performance. The proposed techniques attain a higher accuracy of 98.18% which is 1.58%, 2.02%, and 2.41% better than MECC, MIMO, andAAKA-D2D respectively.

Gradient correlation based detection of adversarial attacks on vehicular networks

The controller area network (CAN) bus, which controls real-time communication and data transmission among vehicle electronic control units, lacks security mechanisms and is highly vulnerable to attacks. Existing in-vehicle network intrusion detection systems (IDSs) typically rely on deep learning models for detection, which are susceptible to interference from adversarial attacks owing to the vulnerability of the models themselves, thereby compromising the detection performance. In this study, we propose an adversarial attack detection method based on gradient correlation that achieves a high accuracy rate using a linear approach to detect adversarial samples. The experimental results show that the proposed model does not require retraining of the original detection model and demonstrates better detection performance for multiple adversarial attacks.