FL-DSFA: Securing RPL-Based IoT Networks against Selective Forwarding Attacks Using Federated Learning

The Internet of Things (IoT) is a significant technological advancement that allows for seamless device integration and data flow. The development of the IoT has led to the emergence of several solutions in various sectors. However, rapid popularization also has its challenges, and one of the most serious challenges is the security of the IoT. Security is a major concern, particularly routing attacks in the core network, which may cause severe damage due to information loss. Routing Protocol for Low-Power and Lossy Networks (RPL), a routing protocol used for IoT devices, is faced with selective forwarding attacks. In this paper, we present a federated learning-based detection technique for detecting selective forwarding attacks, termed FL-DSFA. A lightweight model involving the IoT Routing Attack Dataset (IRAD), which comprises Hello Flood (HF), Decreased Rank (DR), and Version Number (VN), is used in this technique to increase the detection efficiency. The attacks on IoT threaten the security of the IoT system since they mainly focus on essential elements of RPL. The components include control messages, routing topologies, repair procedures, and resources within sensor networks. Binary classification approaches have been used to assess the training efficiency of the proposed model. The training step includes the implementation of machine learning algorithms, including logistic regression (LR), K-nearest neighbors (KNN), support vector machine (SVM), and naive Bayes (NB). The comparative analysis illustrates that this study, with SVM and KNN classifiers, exhibits the highest accuracy during training and achieves the most efficient runtime performance. The proposed system demonstrates exceptional performance, achieving a prediction precision of 97.50%, an accuracy of 95%, a recall rate of 98.33%, and an F1 score of 97.01%. It outperforms the current leading research in this field, with its classification results, scalability, and enhanced privacy

Design and Performance Analysis of an Anti-Malware System based on Generative Adversarial Network Framework

The cyber realm is overwhelmed with dynamic malware that promptly penetrates all defense mechanisms, operates unapprehended to the user, and covertly causes damage to sensitive data. The current generation of cyber users is being victimized by the interpolation of malware each day due to the pervasive progression of Internet connectivity. Malware is dispersed to infiltrate the security, privacy, and integrity of the system. Conventional malware detection systems do not have the potential to detect novel malware without the accessibility of their signatures, which gives rise to a high False Negative Rate (FNR). Previously, there were numerous attempts to address the issue of malware detection, but none of them effectively combined the capabilities of signature-based and machine learning-based detection engines. To address this issue, we have developed an integrated Anti-Malware System (AMS) architecture that incorporates both conventional signature-based detection and AI-based detection modules. Our approach employs a Generative Adversarial Network (GAN) based Malware Classifier Optimizer (MCOGAN) framework, which can optimize a malware classifier. This framework utilizes GANs to generate fabricated benign files that can be used to train external discriminators for optimization purposes. We describe our proposed framework and anti-malware system in detail to provide a better understanding of how a malware detection system works. We evaluate our approach using the Figshare dataset and state-of-the-art models as discriminators, and our results demonstrate improved malware detection performance compared to existing models

Incidence of Port Site Infection After Laparoscopic Cholecystectomy: Our Experience at Hayatabad Medical Complex

OBJECTIVES This study aimed to assess the factors that affect post-laparoscopic cholecystectomies PSI and determine which characteristics can be changed to prevent PSI in a trial to maximize the benefits of laparoscopic surgery.METHODOLOGY The study included all patients who experienced port site infection following laparoscopic cholecystectomy. All patients received Inj Ceftriaxone 1gm pre-operatively & then twice a day postoperatively for 03 days. In all operations, the gallbladder is removed from the epigastric port without using a retrieval bag by skilled surgeons employing four-port methods and reusable equipment. Most patients had the sub-hepatic tube drain placed and were discharged the day after surgery.RESULTSAcute cholecystitis was the most common operative finding with port-site infection, i.e. 6(42.8%), second being empyema that was seen in 3(21.4%) patients, 2(14.3%) patients had bad adhesions, mucocele in 2(14.3%) patients and thick walled gall bladder with stones was found in 1(7.1%) patients respectively, indicating that the relationship between infection and acute cholecystitis is significant. Regarding the spills of bile, stones, or pus, 3(21.4%) patients had infections despite there being no spillage, while 11(78.6%) patients developed an infection while the spillage happened during their procedures. The p-value was 0.0001, meaning that the spillage might be considered a risk factor for the development of port site infection.CONCLUSIONThe spilling of bile, stones, or pus, the port of gallbladder removal, and acute cholecystitis are all strongly associated with port site infection. Given that Mycobacterium tuberculosis may be the source of chronic deep surgical site infections, more care should be exercised. The majority of PSIs are superficial and more prevalent in men

A Cloud-based Healthcare Framework for Security and Patients’ Data Privacy Using Wireless Body Area Networks

AbstractThe recent developments in remote healthcare systems have witnessed significant interests from IT industry (Microsoft, Google, VMware etc) that provide ubiquitous and easily deployable healthcare systems. These systems provide a platform to share medical information, applications, and infrastructure in a ubiquitous and fully automated manner. Communication security and patients’ data privacy are the aspects that would increase the confidence of users in such remote healthcare systems. This paper presents a secure cloud-based mobile healthcare framework using wireless body area networks (WBANs). The research work presented here is twofold: first, it attempts to secure the inter-sensor communication by multi-biometric based key generation scheme in WBANs; and secondly, the electronic medical records (EMRs) are securely stored in the hospital community cloud and privacy of the patients’ data is preserved. The evaluation and analysis shows that the proposed multi-biometric based mechanism provides significant security measures due to its highly efficient key generation mechanism

Synthesis and characterization of some new Schiff base derivatives of gabapentin, and assessment of their antibacterial, antioxidant and anticonvulsant activities

Purpose: To synthesize and characterize some new gabapentin Schiff base derivatives, and to assess their antibacterial, antioxidant and antiepileptic activities.Methods: Four Schiff base derivatives of gabapentin, termed G1, G2, G3 and G4, were synthesized by condensation with benzoin, vanillin, acetophenone, and benzophenone, respectively. Their chemical identities were established by FTIR, 1 H NMR and 13C NMR techniques. The new compounds were screened for antibacterial activity using agar well method, antioxidant activity by DPPH assay, and anticonvulsant activity against pentylenetetrazole (PTZ) induced seizures in mice.Results: All the compounds showed antibacterial activity against the test strains to variable degrees, while the parent drug did not exhibit antibacterial activity. The zones of inhibition of compound G2 against Micrococcus luteus (36.2 ± 1.0 mm) and Serratia marcescens (28.2 ± 1.0 mm), and of compound G4 against Stenotrophomonas maltophilia (36.8 ± 1.0 mm) were larger compared to thestandard drug, doxycycline, exhibiting zones of inhibition 28.2 ± 1.3, 28.2 ± 0.9 and 20.0 ± 0.9 mm, respectively. In addition, compounds G1 and G2 possessed significantly greater (p < 0.05) radical scavenging activity (82.3 ± 1.8 and 92.3 ± 2.2 %, respectively) than the precursor drug, gabapentin (63.2± 2.6 %). The seizure scores for compounds G1 (0.7 ± 0.06) and G2 (0.9 ± 0.07) were comparable(p ˃ 0.05) with gabapentin (0.8 ± 0.06), while compounds G3 and G4 were less active (p < 0.05) than gabapentin.Conclusion: Compounds G1 and G2 exhibit good antibacterial and antioxidant activities while retaining the anticonvulsant activity of the parent drug, gabapentin, thus making them suitable candidates for further development for the treatment of neurodegenerative pathologies associated with bacterial infections. Keywords: Gabapentin, Antibacterial, Seizures, Antioxidant, Anticonvulsan

A Novel Two-Stage Deep Learning Model for Efficient Network Intrusion Detection

The network intrusion detection system is an important tool for protecting computer networks against threats and malicious attacks. Many techniques have recently been proposed; however, these techniques face significant challenges due to the continuous emergence of new threats that are not recognized by the existing detection systems. In this paper, we propose a novel two-stage deep learning model based on a stacked auto-encoder with a soft-max classifier for efficient network intrusion detection. The model comprises two decision stages: an initial stage responsible for classifying network traffic as normal or abnormal using a probability score value. This is then used in the final decision stage as an additional feature for detecting the normal state and other classes of attacks. The proposed model is able to learn useful feature representations from large amounts of unlabeled data and classifies them automatically and efficiently. To evaluate and test the effectiveness of the proposed model, several experiments are conducted on two public datasets: an older benchmark dataset, the KDD99, and a newer one, the UNSW-NB15. The comparative experimental results demonstrate that our proposed model significantly outperforms the existing models and methods and achieves high recognition rates, up to 99.996% and 89.134%, for the KDD99 and UNSW-NB15 datasets, respectively. We conclude that our model has the potential to serve as a future benchmark for deep learning and network security research communities

A novel intrusion detection framework for wireless sensor networks

Abstract Vehicle cloud is a new idea that uses the benefits of wireless sensor networks (WSNs) and the concept of cloud computing to provide better services to the community. It is important to secure a sensor network to achieve better performance of the vehicle cloud. Wireless sensor networks are a soft target for intruders or adversaries to launch lethal attacks in its present configuration. In this paper, a novel intrusion detection framework is proposed for securing wireless sensor networks from routing attacks. The proposed system works in a distributed environment to detect intrusions by collaborating with the neighboring nodes. It works in two modes: online prevention allows safeguarding from those abnormal nodes that are already declared as malicious while offline detection finds those nodes that are being compromised by an adversary during the next epoch of time. Simulation results show that the proposed specification-based detection scheme performs extremely well and achieves high intrusion detection rate and low false positive rate

Two-Hop Monitoring Mechanism Based on Relaxed Flow Conservation Constraints against Selective Routing Attacks in Wireless Sensor Networks

In this paper, we investigate the problem of selective routing attack in wireless sensor networks by considering a novel threat, named the upstream-node effect, which limits the accuracy of the monitoring functions in deciding whether a monitored node is legitimate or malicious. To address this limitation, we propose a one-dimensional one-class classifier, named relaxed flow conservation constraint, as an intrusion detection scheme to counter the upstream node attack. Each node uses four types of relaxed flow conservation constraints to monitor all of its neighbors. Three constraints are applied by using one-hop knowledge, and the fourth one is calculated by monitoring two-hop information. The latter is obtained by proposing two-hop energy-efficient and secure reporting scheme. We theoretically analyze the security and performance of the proposed intrusion detection method. We also show the superiority of relaxed flow conservation constraint in defending against upstream node attack compared to other schemes. The simulation results show that the proposed intrusion detection system achieves good results in terms of detection effectiveness

Evaluation of Different Tomato Hybrids for Phenotypic Variation and Yield Contributing Attributes at Mansehra

The experiment was carried out for the evaluation of different tomato hybrids line for the phenotypic variation and yield contributing parameters at Mansehra during the cropping season 2016. Results showed that days to flowering, days to maturity, plant height, fruit length, fruit width, single fruit weight, fruits plant-1, fruit yield plant-1 and fruit yield ha-1 significantly influenced by various tomato hybrid lines. Maximum number of days to flowering (44.00) were counted for line T-1359, followed by NBH-13 (41.00 days), while line NBH-4 gave the earliest flowering (34.00 days). Maximum number of days to fruit maturity (62.00 days) were observed for the line T-1359, whereas earliest fruit maturity (52.00 days) observed for the line NBH-4. Tallest plant height (51.00 cm) was measured for tomato line NBH-13, followed by NBH-206, NBH-4 with 48.26cm, 47.77cm, respectively while smallest (41.46 cm) measured for line NBH-13. Longest fruit length (74.29 mm) was measured for line NBH-13, whereas shortest fruit length (54.00 mm) measured for NBH-4. Tomato line NBH-4 and NBH-6 Showed the maximum fruit width of 60.33 mm and 60.25 mm, however minimum fruit width 46.82 mm was recorded for the line T-1359. Maximum number of fruits (62.26) plant-1 were recorded for line T-1359, however minimum (35.46) fruits plant-1 recorded for line NBH-206. Maximum single fruit weight (118.00 g), Fruit yield plant-1 (4.15 kg) and fruit yield (83.11 t ha-1) were recorded for tomato line NBH-6, conversely minimum single fruit weight (69.44 g) was recorded for line T-1359, Fruit yield plant-1 (2.45 kg) and fruit yield ha-1 (49.03 t ha-1) were recorded for Line NBH-206. On the basis of results it was determined that the tomato line NBH-06 show the promising results for most of the growth and yield contributing parameters, however potential of these lines is obligatory to be further tested under the agro climatic condition of Mansehra

Anaerobic membrane bioreactors for biohydrogen production: recent developments, challenges and perspectives

Biohydrogen as one of the most appealing energy vector for the future represents attractive avenue in alternative energy research. Recently, variety of biohydrogen production pathways has been suggested to improve the key features of the process. Nevertheless, researches are still needed to overcome remaining barriers to practical applications such as low yields and production rates. Considering practicality aspects, this review emphasized on anaerobic membrane bioreactors (AnMBRs) for biological hydrogen production. Recent advances and emerging issues associated with biohydrogen generation in AnMBR technology are critically discussed. Several techniques are highlighted that are aimed at overcoming these barriers. Moreover, environmental and economical potentials along with future research perspectives are addressed to drive biohydrogen technology towards practicality and economical-feasibility