172 research outputs found
Cyber Security Threats Detection in Internet of Things Using Deep Learning Approach
Get PDFThe IoT (Internet of Things) connect systems, applications, data storage, and services that may be a new gateway for cyber-attacks as they continuously offer services in the organization. Currently, software piracy and malware attacks are high risks to compromise the security of IoT. These threats may steal important information that causes economic and reputational damages. In this paper, we have proposed a combined deep learning approach to detect the pirated software and malware-infected files across the IoT network. The TensorFlow deep neural network is proposed to identify pirated software using source code plagiarism. The tokenization and weighting feature methods are used to filter the noisy data and further, to zoom the importance of each token in terms of source code plagiarism. Then, the deep learning approach is used to detect source code plagiarism. The dataset is collected from Google Code Jam (GCJ) to investigate software piracy. Apart from this, the deep convolutional neural network is used to detect malicious infections in IoT network through color image visualization. The malware samples are obtained from Maling dataset for experimentation. The experimental results indicate that the classification performance of the proposed solution to measure the cybersecurity threats in IoT are better than the state of the art methods
Pyramiding of Four Broad Spectrum Bacterial Blight Resistance Genes in Cross Breeds of Basmati Rice
No full textPyramiding of major resistance (R) genes through marker-assisted selection (MAS) is a useful way to attain durable and broad-spectrum resistance against Xanthomonas oryzae pv. oryzae pathogen, the causal agent of bacterial blight (BB) disease in rice (Oryza sativa L.). The present study was designed to pyramid four broad spectrum BB-R genes (Xa4, xa5, xa13 and Xa21) in the background of Basmati-385, an indica rice cultivar with much sought-after qualitative and quantitative grain traits. The cultivar, however, is susceptible to BB and was therefore, crossed with IRBB59 which possesses R genes xa5, xa13 and Xa21, to attain broad and durable resistance. A total of 19 F1 plants were obtained, some of which were backcrossed with Basmati-385 and large number of BC1F1 plants were obtained. In BC1F2 generation, 31 phenotypically superior genotypes having morphological features of Basmati-385, were selected and advanced up to BC1F6 population. Sequence-tagged site (STS)-based MAS was carried out and phenotypic selection was made in each successive generation. In BC1F6 population, potentially homozygous recombinant inbred lines (RILs) from each line were selected and evaluated on the bases of STS evaluation and resistance to local Xanthomonas oryzae pv. oryzae (Xoo) isolates. Line 23 was found pyramided with all four BB-R genes i.e., Xa4, xa5, xa13 and Xa21. Five genotypes including line 8, line 16, line 21, line 27 and line 28 were identified as pyramided with three R genes, Xa4, xa5 and xa13. Pathological study showed that rice lines pyramided with quadruplet or triplet R genes showed the highest level of resistance compared to doublet or singlet R genes. Thus, line 23 with quadruplet, and lines 8, 16, 21, 27, and 28 with triplet R genes, are recommended for replicated yield and resistance trials before release as new rice varieties. Further, traditional breeding coupled with MAS, is a solid way to attain highly effective BB-resistant rice lines with no yield cost
Influence of Bacterial Secondary Symbionts in Sitobion avenae on Its Survival Fitness against Entomopathogenic Fungi, Beauveria bassiana and Metarhizium brunneum
Get PDFThe research was focused on the ability of wheat aphids Sitobion avenae, harboring bacterial secondary symbionts (BSS) Hamiltonella defensa or Regiella insecticola, to withstand exposure to fungal isolates of Beauveria bassiana and Metarhizium brunneum. In comparison to aphids lacking bacterial secondary symbionts, BSS considerably increased the lifespan of wheat aphids exposed to B. bassiana strains (Bb1022, EABb04/01-Tip) and M. brunneum strains (ART 2825 and BIPESCO 5) and also reduced the aphids’ mortality. The wheat aphid clones lacking bacterial secondary symbionts were shown to be particularly vulnerable to M. brunneum strain BIPESCO 5. As opposed to wheat aphids carrying bacterial symbionts, fungal pathogens infected the wheat aphids lacking H. defensa and R. insecticola more quickly. When treated with fungal pathogens, bacterial endosymbionts had a favorable effect on the fecundity of their host aphids compared to the aphids lacking these symbionts, but there was no change in fungal sporulation on the deceased aphids. By defending their insect hosts against natural enemies, BSS increase the population of their host society and may have a significant impact on the development of their hosts
Prevention of hypotension associated with the induction dose of propofol: A randomized controlled trial comparing equipotent doses of phenylephrine and ephedrine
No full textBackground and Aims: Propofol, the most commonly used intravenous (IV) anesthetic agent is associated with hypotension on induction of anesthesia. Different methods have been used to prevent hypotension but with variable results. The objective of this study was to evaluate efficacy of equipotent doses of phenylpehrine and ephedrine in preventing the hypotensive response to the induction dose of propofol.
Material and Methods: One hundred thirty five adult patients were randomised to one of the study groups: "propofol-saline (PS)," "propofol-phenylephrine (PP)" or "propofol-ephedrine (PE)" by adding study drugs to propofol. Anesthesia was induced with a mixture of propofol and the study drug. Patients were manually mask-ventilated for 5 min using 40% oxygen in nitrous oxide and isoflurane at 1%. A baseline mean arterial pressure (MAP) was recorded prior to induction of anesthesia. Systolic, diastolic and mean blood pressure and heart rate were recorded every minute for up to 5 min after induction. Hypotension was defined as a 20% decrease from the baseline MAP.
Results: There were no significant demographic differences between the groups. Overall incidence of hypotension in this study was 38.5% (52/135). Rate of hypotension was significantly higher in group PS than group PP (60% vs. 24.4% P = 0.001) and group PE (60% vs. 31.1% P = 0.005). In contrast, a significant difference in rate of hypotension was not observed between groups PP and group PE.
Conclusion: In equipotent doses, phenylephrine is as good as ephedrine in preventing the hypotensive response to an induction dose of propofol
Combined Effect of Biochar and Plant Growth-Promoting Rhizbacteria on Physiological Responses of Canola (Brassica napus L.) Subjected to Drought Stress
No full textBiochar (BC) and plant growth-promoting microbes (PGPR) could represent a suitable agronomical strategy to mitigate the impacts of drought in arid agro-environmental conditions. However, there is currently little understanding of the synergistic benefit of combining BC and PGPR to increase drought tolerance in oilseeds. In this study, the physiological response of two water-stressed canola (Brassica napus L.) plants subjected to the application of BC obtained from waste wood of Morus alba applied solely or in combination with PGPR strains (Pseudomonas sp.) was evaluated. The experiment consists of two genotypes and nine treatments [(C-Control, T1-15 days drought (15DD), T2-30 days drought (30DD), T3-15 days of drought + PG (15DD + PG), T4-30 days of drought + PG (30DD + PG), T5-15 days drought + biochar (15DD + BC), T6-30 days drought + biochar (30DD + BC), T7-15 days drought + biochar + PG (15DD + BC + PG), T8-30 days drought + biochar + PG (30DD + BC + PG)]. Drought stress decreased emergence energy (EE), leaf area index (LAI), leaf area ratio (LAR), root shoot ratio (RSR), moisture content of leaves (MCL), percent moisture content (%MC), moisture content of shoot (MCS) and moisture content of root (MCR), and relative water content (RWC) in both varieties of Brassica napus L., which in contrast, it is increased by the collective application of both biochar and PGPR. In both varieties, N, P, K, Mg, and Ca concentrations were highest in all the biochar and PGPRs separate and combined treatments, while lowest in 15 and 30 days drought treatments. Osmolyte contents like Glycine betaine (GB) and sugar remarkably increased in the stress condition and then reduced due to the synergistic application of biochar and PGPR. Drought stress has a repressive effect on the antioxidant enzymatic system like Peroxidase (POD), Superoxide dismutase (SOD), and glutathione reductase (GR) as well as total flavonoids, phenolics, and protein content. The antioxidant enzymes and phenolic compounds were dramatically increased by the combined action of biochar and PGPRs. A significant increase in EE, LAR, RSR, and RWC under 15 and 30 days drought conditions, evidently highlighting the synergistic effect of BC and PGPR. The results conclude a substantial and positive effect of the combined use of BC and PGPR strains on canola's response to induced drought stress, by regulating the physiological, biochemical, and agronomic traits of the plants.[GRAPHICS]
Cyber-Threat Detection System Using a Hybrid Approach of Transfer Learning and Multi-Model Image Representation
Get PDFCurrently, Android apps are easily targeted by malicious network traffic because of their constant network access. These threats have the potential to steal vital information and disrupt the commerce, social system, and banking markets. In this paper, we present a malware detection system based on word2vec-based transfer learning and multi-model image representation. The proposed method combines the textual and texture features of network traffic to leverage the advantages of both types. Initially, the transfer learning method is used to extract trained vocab from network traffic. Then, the malware-to-image algorithm visualizes network bytes for visual analysis of data traffic. Next, the texture features are extracted from malware images using a combination of scale-invariant feature transforms (SIFTs) and oriented fast and rotated brief transforms (ORBs). Moreover, a convolutional neural network (CNN) is designed to extract deep features from a set of trained vocab and texture features. Finally, an ensemble model is designed to classify and detect malware based on the combination of textual and texture features. The proposed method is tested using two standard datasets, CIC-AAGM2017 and CICMalDroid 2020, which comprise a total of 10.2K malware and 3.2K benign samples. Furthermore, an explainable AI experiment is performed to interpret the proposed approach
Correction: Ullah et al. Enhancing Brain Tumor Segmentation Accuracy through Scalable Federated Learning with Advanced Data Privacy and Security Measures. Mathematics 2023, 11, 4189
No full textThe author’s contribution is incomplete in the original publication [...
Understanding the origin of Cambrian marine oncoids: A petrologic, geochemical, and isotopic perspective
No full textFolio
No full textPlatinum EditionGod Bless Them! pp. 1; In Memory of Prof. Khurshid A. Gill. pp. 2; Sandhu, M. Y.-Poetry-Memories of the Bye-gones. pp. 2; Arif Qureshi-In dulcet memory... pp. 3-4; Arif Qureshi-Poetry-Reminiscences. pp. 4; Ravia Shabeen-The Folio (Registering its History). pp. 5-6; Folio (Year by Year). pp. 7-8; Interview-The Living Legend (F.E. Chaudhry is the oldest Formanite alive, to the best of our knowledge). pp. 9-10; Interview-An Hour with Mian Muhammad Somroo. pp. 11-12; Zainab Mohsin-Buildings. pp. 13-14; Agha Mohsin Sohail-Formanites Are Great. pp. 15-17; Once A Formanite... Always A Formanite. pp. 18-20; Haque Nawaz Cheema-Love for my Institution. pp. 21; Agha Saeed-Old is Gold, New is Silver. pp. 22; Lamia Islam Khan-My Favourite Place. pp. 23; Sara Kanwal-Motivation Forever. pp. 24-25; Bilkis Hussain-Before Sunset. pp. 26; Farid A. Malik-Back to 1958. pp. 27-28; The Excellers. pp. 29-30; Position Holders (Convocation 2008). pp. 31; Survey. pp. 33-34; Donald Alfred Smith-Poetry-A Valentine. pp. 35; Easha Farooq-Love - A Human Need. pp. 36; Fatima Majeed-Defining Love. pp. 37; Bahawal Shehryar-A Fundamentalist. pp. 38; Farhat Aziz-Essay-Concept of Love in Christianity and Islam. pp. 39-40; Fatima Tahir-Mother's Love. pp. 41; Rabiya Khawar-A Blessing for the Two Worlds. pp. 42; Anam Khalid-Etymology of Love. pp. 43; Noor Nisha-Beauty of Love. pp. 44; Ayesha Rana-The Ultimate Aim. pp. 45; Madiha Sundas Rana-I Love thee for a Heart that is Kind. pp. 46; Rabiya Khawar-True Love Never Dies. pp. 47; Amina Jamil-Love - A Timeless Essence. pp. 48; Farhan Hashmi-Love Its Immortal. pp. 49; Kashifa Khalid-A Quest. pp. 50; Daud Aziz Khokher-Love!!! pp. 51-52; Bilkis Hussain-The Truth about Love!!! pp. 53; Alvi, M. 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Clone detection in 5G-enabled social IoT system using graph semantics and deep learning model
No full textThe protection and privacy of the 5G-IoT framework is a major challenge due to the vast number of mobile devices. Specialized applications running these 5G-IoT systems may be vulnerable to clone attacks. Cloning applications can be achieved by stealing or distributing commercial Android apps to harm the advanced services of the 5G-IoT framework. Meanwhile, most Android app stores run and manage Android apps that developers have submitted separately without any central verification systems. Android scammers sell pirated versions of commercial software to other app stores under different names. Android applications are typically stored on cloud servers, while API access services may be used to detect and prevent cloned applications from being released. In this paper, we proposed a hybrid approach to the Control Flow Graph (CFG) and a deep learning model to secure the smart services of the 5G-IoT framework. First, the newly submitted APK file is extracted and the JDEX decompiler is used to retrieve Java source files from possibly original and cloned applications. Second, the source files are broken down into various android-based components. After generating Control-Flow Graphs (CFGs), the weighted features are stripped from each component. Finally, the Recurrent Neural Network (RNN) is designed to predict potential cloned applications by training features from different components of android applications. Experimental results have shown that the proposed approach can achieve an average accuracy of 96.24% for cloned applications selected from different android application stores.</p
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