Perioperative chemotherapy with docetaxel plus oxaliplatin and S-1 (DOS) versus oxaliplatin plus S-1 (SOX) for the treatment of locally advanced gastric or gastro-esophageal junction adenocarcinoma (MATCH): an open-label, randomized, phase 2 clinical trial.

BACKGROUND: It remains unclear whether addition of docetaxel to the combination of a platinum and fluoropyrimidine could provide more clinical benefits than doublet chemotherapies in the perioperative treatment for locally advanced gastric/gastro-esophageal junction (LAG/GEJ) cancer in Asia. In this randomized, phase 2 study, we assessed the efficacy and safety of perioperative docetaxel plus oxaliplatin and S-1 (DOS) versus oxaliplatin plus S-1 (SOX) in LAG/GEJ adenocarcinoma patients. METHODS: Patients with cT3-4 Nany M0 G/GEJ adenocarcinoma were randomized (1:1) to receive 4 cycles of preoperative DOS or SOX followed by D2 gastrectomy and another 4 cycles of postoperative chemotherapy. The primary endpoint was major pathological response (MPR). RESULTS: From Aug, 2015 to Dec, 2019,154 patients were enrolled and 147 patients included in final analysis, with a median age of 60 (26-73) years. DOS resulted in significantly higher MPR (25.4 vs. 11.8%, P = 0.04). R0 resection rate, the 3-year PFS and 3-year OS rates were 78.9 vs. 61.8% (P = 0.02), 52.3 vs. 35% (HR 0.667, 95% CI: 0.432-1.029, Log rank P = 0.07) and 57.5 vs. 49.2% (HR 0.685, 95% CI: 0.429-1.095, Log rank P = 0.11) in the DOS and SOX groups, respectively. Patients who acquired MPR experienced significantly better survival. DOS had similar tolerance to SOX. CONCLUSIONS: Perioperative DOS improved MPR significantly and tended to produce longer PFS compared to SOX in LAG/GEJ cancer in Asia, and might be considered as a preferred option for perioperative chemotherapy and worth further investigation.

Short-term outcomes of preoperative chemotherapy with docetaxel, oxaliplatin, and S-1 for gastric cancer with extensive lymph node metastasis (JCOG1704).

BACKGROUND: The prognosis for marginally resectable gastric cancer with extensive lymph node metastasis (ELM) remains unfavorable, even after R0 resection. To assess the safety and efficacy of preoperative docetaxel, oxaliplatin, and S-1 (DOS), we conducted a multicenter phase II trial. METHODS: Eligibility criteria included histologically proven HER2-negative gastric adenocarcinoma with bulky nodal (bulky N) involvement around major branched arteries or para-aortic node (PAN) metastases. Patients received three cycles of docetaxel (40 mg/m2, day 1), oxaliplatin (100 mg/m2, day 1), and S-1 (80-120 mg/body, days 1-14), followed by gastrectomy with D2 plus PAN dissection. Subsequently, patients underwent postoperative chemotherapy with S-1 for 1 year. The primary endpoint was major (grade ≥ 2a) pathological response rate (pRR) according to the Japanese Classification of Gastric Carcinoma criteria. RESULTS: Between October 2018 and March 2022, 47 patients (bulky N, 20; PAN, 17; both, 10) were enrolled in the trial. One patient was ineligible. Another declined any protocol treatments before initiation. Among the 45 eligible patients who initiated DOS chemotherapy, 44 (98%) completed 3 cycles and 42 (93%) underwent R0 resection. Major pRR and pathological complete response rates among the 46 eligible patients, including the patient who declined treatment, were 57% (26/46) and 24% (11/46), respectively. Common grade 3 or 4 toxicities were neutropenia (24%), anorexia (16%), febrile neutropenia (9%), and diarrhea (9%). No treatment-related deaths occurred. CONCLUSIONS: Preoperative chemotherapy with DOS yielded favorable pathological responses with an acceptable toxicity profile. This multimodal approach is highly promising for treating gastric cancer with ELM.

Protocol digest of a randomized controlled adaptive Phase II/III trial of neoadjuvant chemotherapy for Japanese patients with oesophagogastric junction adenocarcinoma: Japan Clinical Oncology Group Study JCOG2203 (NEO-JPEG).

Treatment strategies for oesophagogastric junction adenocarcinoma have not been standardized despite its poor prognosis due to differences in the incidence rates between Western countries and Asia. This randomized Phase II/III trial was initiated in June 2023 to determine which neoadjuvant chemotherapy regimen, docetaxel, oxaliplatin and S-1 or fluorouracil, oxaliplatin and docetaxel, is a more promising treatment in Phase II and confirm the superiority of neoadjuvant chemotherapy with docetaxel, oxaliplatin and S-1 or fluorouracil, oxaliplatin and docetaxel followed by surgery and postoperative chemotherapy over upfront surgery and postoperative chemotherapy in terms of overall survival in patients with Clinical Stage III or IVA oesophagogastric junction adenocarcinoma in Phase III. A total of 460 patients, including 150 patients in Phase II and 310 patients in Phase III, are planned to be enrolled from 85 hospitals in Japan over 5 years. This trial has been registered in the Japan Registry of Clinical Trials as jRCTs031230182 (https://jrct.niph.go.jp/latest-detail/jRCTs031230182).

Antiangiogenic potential of phytochemicals from Clerodendrum inerme (L.) Gaertn investigated through in silico and quantum computational methods.

Suppressing vascular endothelial growth factor (VEGF), its receptor (VEGFR2), and the VEGF/VEGFR2 signaling cascade system to inhibit angiogenesis has emerged as a possible cancer therapeutic target. The present work was designed to discover and evaluate bioactive phytochemicals from the Clerodendrum inerme (L.) Gaertn plant for their anti-angiogenic potential. Molecular docking of twenty-one phytochemicals against the VEGFR-2 (PDB ID: 3VHE) protein was performed, followed by ADMET profiling and molecular docking simulations. These investigations unveiled two hit compounds, cirsimaritin (- 12.29 kcal/mol) and salvigenin (- 12.14 kcal/mol), with the highest binding energy values when compared to the reference drug, Sorafenib (- 15.14 kcal/mol). Furthermore, only nine phytochemicals (cirsimaritin and salvigenin included) obeyed Lipinski's rule of five and passed ADMET filters. Molecular dynamics simulations run over 100 ns revealed that the protein-ligand complexes remained stable with minimal backbone fluctuations. The binding free energy values of cirsimaritin (- 52.35 kcal/mol) and salvigenin (- 55.89 kcal/mol), deciphered by MM-GBSA analyses, further corroborated the docking interactions. The HOMO-LUMO band energy gap (ΔE) was calculated using density-functional theory (DFT) and substantiated using density of state (DOS) spectra. The chemical reactivity analyses revealed that salvigenin exhibited the highest chemical softness value (6.384 eV), the lowest hardness value (0.07831 eV), and the lowest ΔE value (0.1566 eV), which implies salvigenin was less stable and chemically more reactive than cirsimaritin and sorafenib. These findings provide further evidence that cirsimaritin and salvigenin have the ability to prevent angiogenesis and the development of cancer. Nevertheless, more in vitro and in vivo confirmation is necessary.

DTR-SHIELD: Mutual Synchronization for Protecting against DoS Attacks on the SHIELD Protocol with AES-CTR Mode.

To enhance security in the semiconductor industry's globalized production, the Defense Advanced Research Projects Agency (DARPA) proposed an authentication protocol under the Supply Chain Hardware Integrity for Electronics Defense (SHIELD) program. This protocol integrates a secure hardware root-of-trust, known as a dielet, into integrated circuits (ICs). The SHIELD protocol, combined with the Advanced Encryption Standard (AES) in counter mode, named CTR-SHIELD, targets try-and-check attacks. However, CTR-SHIELD is vulnerable to desynchronization attacks on its counter blocks. To counteract this, we introduce the DTR-SHIELD protocol, where DTR stands for double counters. DTR-SHIELD addresses the desynchronization issue by altering the counter incrementation process, which previously solely relied on truncated serial IDs. Our protocol adds a new AES encryption step and requires the dielet to transmit an additional 100 bits, ensuring more robust security through active server involvement and message verification.

Resilient Event-Based Fuzzy Fault Detection for DC Microgrids in Finite-Frequency Domain against DoS Attacks.

This paper addresses the problem of fault detection in DC microgrids in the presence of denial-of-service (DoS) attacks. To deal with the nonlinear term in DC microgrids, a Takagi-Sugeno (T-S) model is employed. In contrast to the conventional approach of utilizing current sampling data in the traditional event-triggered mechanism (ETM), a novel integrated ETM employs historical information from measured data. This innovative strategy mitigates the generation of additional triggering packets resulting from random perturbations, thus reducing redundant transmission data. Under the assumption of faults occurring within a finite-frequency domain, a resilient event-based H-/H∞ fault detection filter (FDF) is designed to withstand DoS attacks. The exponential stability conditions are derived in the form of linear matrix inequalities to ensure the performance of fault detected systems. Finally, the simulation results are presented, demonstrating that the designed FDF effectively detects finite-frequency faults in time even under DoS attacks. Furthermore, the FDF exhibits superior fault detection sensitivity compared to the conventional H∞ method, thus confirming the efficacy of the proposed approach. Additionally, it is observed that a trade-off exists between fault detection performance and the data releasing rate (DRR).

Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms.

Widespread and ever-increasing cybersecurity attacks against Internet of Things (IoT) systems are causing a wide range of problems for individuals and organizations. The IoT is self-configuring and open, making it vulnerable to insider and outsider attacks. In the IoT, devices are designed to self-configure, enabling them to connect to networks autonomously without extensive manual configuration. By using various protocols, technologies, and automated processes, self-configuring IoT devices are able to seamlessly connect to networks, discover services, and adapt their configurations without requiring manual intervention or setup. Users' security and privacy may be compromised by attackers seeking to obtain access to their personal information, create monetary losses, and spy on them. A Denial of Service (DoS) attack is one of the most devastating attacks against IoT systems because it prevents legitimate users from accessing services. A cyberattack of this type can significantly damage IoT services and smart environment applications in an IoT network. As a result, securing IoT systems has become an increasingly significant concern. Therefore, in this study, we propose an IDS defense mechanism to improve the security of IoT networks against DoS attacks using anomaly detection and machine learning (ML). Anomaly detection is used in the proposed IDS to continuously monitor network traffic for deviations from normal profiles. For that purpose, we used four types of supervised classifier algorithms, namely, Decision Tree (DT), Random Forest (RF), K Nearest Neighbor (kNN), and Support Vector Machine (SVM). In addition, we utilized two types of feature selection algorithms, the Correlation-based Feature Selection (CFS) algorithm and the Genetic Algorithm (GA) and compared their performances. We also utilized the IoTID20 dataset, one of the most recent for detecting anomalous activity in IoT networks, to train our model. The best performances were obtained with DT and RF classifiers when they were trained with features selected by GA. However, other metrics, such as training and testing times, showed that DT was superior.

The Guardian Node Slow DoS Detection Model for Real-Time Application in IoT Networks.

The pernicious impact of malicious Slow DoS (Denial of Service) attacks on the application layer and web-based Open Systems Interconnection model services like Hypertext Transfer Protocol (HTTP) has given impetus to a range of novel detection strategies, many of which use machine learning (ML) for computationally intensive full packet capture and post-event processing. In contrast, existing detection mechanisms, such as those found in various approaches including ML, artificial intelligence, and neural networks neither facilitate real-time detection nor consider the computational overhead within resource-constrained Internet of Things (IoT) networks. Slow DoS attacks are notoriously difficult to reliably identify, as they masquerade as legitimate application layer traffic, often resembling nodes with slow or intermittent connectivity. This means they often evade detection mechanisms because they appear as genuine node activity, which increases the likelihood of mistakenly being granted access by intrusion-detection systems. The original contribution of this paper is an innovative Guardian Node (GN) Slow DoS detection model, which analyses the two key network attributes of packet length and packet delta time in real time within a live IoT network. By designing the GN to operate within a narrow window of packet length and delta time values, accurate detection of all three main Slow DoS variants is achieved, even under the stealthiest malicious attack conditions. A unique feature of the GN model is its ability to reliably discriminate Slow DoS attack traffic from both genuine and slow nodes experiencing high latency or poor connectivity. A rigorous critical evaluation has consistently validated high, real-time detection accuracies of more than 98% for the GN model across a range of demanding traffic profiles. This performance is analogous to existing ML approaches, whilst being significantly more resource efficient, with computational and storage overheads being over 96% lower than full packet capture techniques, so it represents a very attractive alternative for deployment in resource-scarce IoT environments.

Revolutionizing SIEM Security: An Innovative Correlation Engine Design for Multi-Layered Attack Detection.

Advances in connectivity, communication, computation, and algorithms are driving a revolution that will bring economic and social benefits through smart technologies of the Industry 4.0 era. At the same time, attackers are targeting this expanded cyberspace to exploit it. Therefore, many cyberattacks are reported each year at an increasing rate. Traditional security devices such as firewalls, intrusion detection systems (IDSs), intrusion prevention systems (IPSs), anti-viruses, and the like, often cannot detect sophisticated cyberattacks. The security information and event management (SIEM) system has proven to be a very effective security tool for detecting and mitigating such cyberattacks. A SIEM system provides a holistic view of the security status of a corporate network by analyzing log data from various network devices. The correlation engine is the most important module of the SIEM system. In this study, we propose the optimized correlator (OC), a novel correlation engine that replaces the traditional regex matching sub-module with a novel high-performance multiple regex matching library called "Hyperscan" for parallel log data scanning to improve the performance of the SIEM system. Log files of 102 MB, 256 MB, 512 MB, and 1024 MB, generated from log data received from various devices in the network, are input into the OC and simple event correlator (SEC) for applying correlation rules. The results indicate that OC is 21 times faster than SEC in real-time response and 2.5 times more efficient in execution time. Furthermore, OC can detect multi-layered attacks successfully.

σ-Hole intermolecular interactions between carbon oxides and dihalogens: Ab-initio investigations.

Recently, halogen bonding (XB) has received increased attention as a new type of non-covalent interaction widely present in nature. In this work, quantum chemical calculations at DFT level have been carried out to investigate halogen bonding interactions between COn (n = 1 or 2) and dihalogen molecules XY (X = F, Cl, Br, I and Y = Cl, Br, I). Highly accurate all-electron data, estimated by CCSD(T) calculations, were used to benchmark the different levels of computational methods with the objective of finding the best accuracy/computational cost. Molecular electrostatic potential, interaction energy values, charge transfer, UV spectra, and natural bond orbital (NBO) analysis were determined to better understand the nature of the XB interaction. Density of states (DOS) and projected DOS were also computed. Hence, according to these results, the magnitude of the halogen bonding is affected by the halogen polarizability and electronegativity, where for the more polarizable and less electronegative halogen atoms, the σ-hole is bigger. Furthermore, for the halogen-bonded complexes involving CO and XY, the OC∙∙∙XY interaction is stronger than the CO∙∙∙XY interaction. Thus, the results presented here can establish fundamental characteristics of halogen bonding in media, which would be very helpful for applying this noncovalent interaction for the sustainable capture of carbon oxides.

Diversity-Oriented Synthesis (DOS) of On-DNA Peptidomimetics from Acid-Derived Phosphonium Ylides.

The DNA-encoded library (DEL) technology represents a revolutionary drug-discovery tool with unprecedented screening power originating from the association of combinatorial chemistry and DNA barcoding. The chemical diversity of DELs and its chemical space will be further expanded as new DNA-compatible reactions are introduced. This work introduces the use of DOS in the context of on-DNA peptidomimetics. Wittig olefination of aspartic acid-derived on-DNA Wittig ylide, combined with a broad substrate scope of aldehydes, led to formation of on-DNA α ${\alpha }$ , ß ${\beta }$ -unsaturated ketones. The synthesis of on-DNA multi-peptidyl-ylides was performed by incorporating sequential amino acids onto a monomeric ylide. Di-, tri- and tetrameric peptidyl-ylides were validated for Wittig olefination and led to on-DNA α ${\alpha }$ , ß ${\beta }$ -unsaturated-based peptidomimetics, an important class of intermediates. One on-DNA aryl Wittig ylide was also developed and applied to Wittig olefination for synthesis of on-DNA chalcone-based molecules. Furthermore, DOS was used successfully with electron-deficient peptidomimetics and led to the development of different heterocyclic cores containing on-DNA peptidomimetics.

Randomized phase II study comparing neoadjuvant 5-fluorouracil/oxaliplatin/docetaxel versus docetaxel/oxaliplatin/S-1 for patients with type 4 or large type 3 gastric cancer.

Macroscopic type 4 and large type 3 gastric cancer, mostly overlapping with scirrhous or linitis plastica type, exhibit a highly invasive nature and show unfavorable prognosis after curative surgery, even with adjuvant chemotherapy. A randomized phase III trial (JCOG0501) failed to demonstrate a survival advantage of neoadjuvant chemotherapy with S-1 plus cisplatin for this population. The current authors initiated a randomized phase II study comparing neoadjuvant chemotherapy with 5-fluorouracil/oxaliplatin/docetaxel versus docetaxel/oxaliplatin/S-1 for type 4 and large type 3 gastric cancer. 76 patients are planned to be enrolled over two years. The primary end point is the proportion of patients with a pathological response (grade 1b or higher) and secondary end points include overall survival and adverse events. Clinical Trial Registration: jRCTs031230231 (rctportal.niph.go.jp).

Discovery of novel pyrazole and pyrazolo[1,5-a]pyrimidine derivatives as cyclooxygenase inhibitors (COX-1 and COX-2) using molecular modeling simulation.

Searching for effective and selective anti-inflammatory agents, our study involved designing and synthesizing new pyrazole and pyrazolo[1,5-a]pyrimidine derivatives 4-11. The structures of the synthesized derivatives were confirmed using different spectroscopic techniques. Virtual screening was achieved for the newly designed derivatives using in silico docking simulation inside the active sites of four proteins classified as two cyclooxygenases (COX)-1 (PDB: 3KK6 and 4OIZ) and two COX-2 (PBD: 1CX2 and 3LN1). Among them, six derivatives 4c, 5b, 6a, 7a, 7b, and 10b displayed the highest binding energy. These derivatives were evaluated for their in vitro COX-1 and COX-2 inhibitory activities and their selectivity indexes were calculated. Additionally, these derivatives displayed IC50 values ranging between 4.909 ± 0.25 and 57.53 ± 2.91 µM, and 3.289 ± 0.14 and 124 ± 5.32 µM, against COX-1 and COX-2, respectively. Furthermore, the tested derivatives were found to have selective inhibitory activity on the COX-2 enzyme. Surprisingly, the two pyrazole derivatives 4c and 5b were found to be the most active, with IC50 values of 9.835 ± 0.50 and 4.909 ± 0.25 µM and 4.597 ± 0.20 and 3.289 ± 0.14 µM compared with meloxicam (1.879 ± 0.1 and 5.409 ± 0.23 µM) and celecoxib (5.439 ± 0.28 and 2.164 ± 0.09 µM) against COX-1/-2, respectively. Besides, two pyrazole derivatives, 4c and 5b, displayed a COX-1/COX-2 SI of 2.14 and 1.49. Computational techniques such as molecular docking, density function theory (DFT) calculation, and chemical absorption, distribution, metabolism, excretion, and toxicity evaluation were applied to explain the molecules' binding mode, chemical nature, drug likeness, and toxicity prediction.

Monte Carlo Simulation of Diffuse Optical Spectroscopy for 3D Modeling of Dental Tissues.

Three-dimensional precise models of teeth are critical for a variety of dental procedures, including orthodontics, prosthodontics, and implantology. While X-ray-based imaging devices are commonly used to obtain anatomical information about teeth, optical devices offer a promising alternative for acquiring 3D data of teeth without exposing patients to harmful radiation. Previous research has not examined the optical interactions with all dental tissue compartments nor provided a thorough analysis of detected signals at various boundary conditions for both transmittance and reflectance modes. To address this gap, a GPU-based Monte Carlo (MC) method has been utilized to assess the feasibility of diffuse optical spectroscopy (DOS) systems operating at 633 nm and 1310 nm wavelengths for simulating light-tissue interactions in a 3D tooth model. The results show that the system's sensitivity to detect pulp signals at both 633 nm and 1310 nm wavelengths is higher in the transmittance compared with that in the reflectance mode. Analyzing the recorded absorbance, reflectance, and transmittance data verified that surface reflection at boundaries can improve the detected signal, especially from the pulp region in both reflectance and transmittance DOS systems. These findings could ultimately lead to more accurate and effective dental diagnosis and treatment.

Resilient Consensus Control for Multi-Agent Systems: A Comparative Survey.

Due to the openness of communication network and the complexity of system structures, multi-agent systems are vulnerable to malicious network attacks, which can cause intense instability to these systems. This article provides a survey of state-of-the-art results of network attacks on multi-agent systems. Recent advances on three types of attacks, i.e., those on DoS attacks, spoofing attacks and Byzantine attacks, the three main network attacks, are reviewed. Their attack mechanisms are introduced, and the attack model and the resilient consensus control structure are discussed, respectively, in detail, in terms of the theoretical innovation, the critical limitations and the change of the application. Moreover, some of the existing results along this line are given in a tutorial-like fashion. In the end, some challenges and open issues are indicated to guide future development directions of the resilient consensus of multi-agent system under network attacks.

An Analysis into Physical and Virtual Power Draw Characteristics of Embedded Wireless Sensor Network Devices under DoS and RPL-Based Attacks.

Currently, within the world, cybercrime is becoming increasingly rampant-often targeting civil infrastructure like power stations and other critical systems. A trend that is being noticed with these attacks is their increased use of embedded devices in denial-of-service (DoS) attacks. This creates a substantial risk to systems and infrastructures worldwide. Threats to embedded devices can be significant, and network stability and reliability can suffer, mainly through the risk of battery draining or complete system hang. This paper investigates such consequences through simulations of excessive loads, by staging attacks on embedded devices. Experimentation within Contiki OS focused on loads placed on physical and virtualised wireless sensor network (WSN) embedded devices by launching DoS attacks and by exploiting the Routing Protocol for Low Power and Lossy Networks (RPL). Results from these experiments were based on the metric of power draw, mainly the percentage increase over baseline and the pattern of it. The physical study relied on the output of the inline power analyser and the virtual study relied on the output of a Cooja plugin called PowerTracker. This involved experiments on both physical and virtual devices, and analysis of the power draws characteristics of WSN devices with a focus on embedded Linux platforms and Contiki OS. Experimental results provide evidence that peak power draining occurs with a malicious-node-to-sensor device ratio of 13-to-1. Results show a decline in power usage with a more expansive 16-sensor network after modelling and simulating a growing sensor network within the Cooja simulator.

Robust Stabilization of Linear Time-Delay Systems under Denial-of-Service Attacks.

This research examines new methods for stabilizing linear time-delay systems that are subject to denial-of-service (DoS) attacks. The study takes into account the different effects that a DoS attack can have on the system, specifically delay-independent and -dependent behaviour. The traditional proportional-integral-derivative (PID) acts on the error signal, which is the difference between the reference input and the measured output. The approach in this paper uses what we call the PID state feedback strategy, where the controller acts on the state signal. Our proposed strategy uses the Lyapunov-Krasovskii functional (LKF) to develop new linear matrix inequalities (LMIs). The study considers two scenarios where the time delay is either a continuous bounded function or a differentiable and time-varying function that falls within certain bounds. In both cases, new LMIs are derived to find the PID-like state feedback gains that will ensure robust stabilization. The findings are illustrated with numerical examples.

Improved Dynamic Event-Triggered Robust Control for Flexible Robotic Arm Systems with Semi-Markov Jump Process.

In this paper, we investigate the problem of a dynamic event-triggered robust controller design for flexible robotic arm systems with continuous-time phase-type semi-Markov jump process. In particular, the change in moment of inertia is first considered in the flexible robotic arm system, which is necessary for ensuring the security and stability control of special robots employed under special circumstances, such as surgical robots and assisted-living robots which have strict lightweight requirements. To handle this problem, a semi-Markov chain is conducted to model this process. Furthermore, the dynamic event-triggered scheme is used to solve the problem of limited bandwidth in the network transmission environment, while considering the impact of DoS attacks. With regard to the challenging circumstances and negative elements previously mentioned, the adequate criteria for the existence of the resilient H∞ controller are obtained using the Lyapunov function approach, and the controller gains, Lyapunov parameters and event-triggered parameters are co-designed. Finally, the effectiveness of the designed controller is demonstrated via numerical simulation using the LMI toolbox in MATLAB.

Detection of Management-Frames-Based Denial-of-Service Attack in Wireless LAN Network Using Artificial Neural Network.

Wireless Local Area Networks (WLANs) have become an increasingly popular mode of communication and networking, with a wide range of applications in various fields. However, the increasing popularity of WLANs has also led to an increase in security threats, including denial of service (DoS) attacks. In this study, management-frames-based DoS attacks, in which the attacker floods the network with management frames, are particularly concerning as they can cause widespread disruptions in the network. Attacks known as denial of service (DoS) can target wireless LANs. None of the wireless security mechanisms in use today contemplate defence against them. At the MAC layer, there are multiple vulnerabilities that can be exploited to launch DoS attacks. This paper focuses on designing and developing an artificial neural network (NN) scheme for detecting management-frames-based DoS attacks. The proposed scheme aims to effectively detect fake de-authentication/disassociation frames and improve network performance by avoiding communication interruption caused by such attacks. The proposed NN scheme leverages machine learning techniques to analyse patterns and features in the management frames exchanged between wireless devices. By training the NN, the system can learn to accurately detect potential DoS attacks. This approach offers a more sophisticated and effective solution to the problem of DoS attacks in wireless LANs and has the potential to significantly enhance the security and reliability of these networks. According to the experimental results, the proposed technique exhibits higher effectiveness in detection compared to existing methods, as evidenced by a significantly increased true positive rate and a decreased false positive rate.

CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment.

Nowadays, the Internet of Things (IoT) concept plays a pivotal role in society and brings new capabilities to different industries. The number of IoT solutions in areas such as transportation and healthcare is increasing and new services are under development. In the last decade, society has experienced a drastic increase in IoT connections. In fact, IoT connections will increase in the next few years across different areas. Conversely, several challenges still need to be faced to enable efficient and secure operations (e.g., interoperability, security, and standards). Furthermore, although efforts have been made to produce datasets composed of attacks against IoT devices, several possible attacks are not considered. Most existing efforts do not consider an extensive network topology with real IoT devices. The main goal of this research is to propose a novel and extensive IoT attack dataset to foster the development of security analytics applications in real IoT operations. To accomplish this, 33 attacks are executed in an IoT topology composed of 105 devices. These attacks are classified into seven categories, namely DDoS, DoS, Recon, Web-based, brute force, spoofing, and Mirai. Finally, all attacks are executed by malicious IoT devices targeting other IoT devices. The dataset is available on the CIC Dataset website.