Worm Attacks Research Articles

Automated Detection of Plant Diseases and Pests Using Advanced Image Processing and Neural Networks

Pests and viral plant diseases have proliferated in agriculture. Rats spread plant diseases. Hard to detect viral infections or insect infestations that impair plant growth and yield. It decreases crop size, quality, and marketability, incurring major economic losses. Strong, disease-resistant crops are needed for agriculture. Bacterial, fungal, and insect/pest diseases destroy 70% and 15% of crops. Above 85% of disease severity destroys crops. Early detection systems track pests and diseases. High-definition equipment is costly for rural farmers. Correct diagnosis permits quick infection severity and type assessment and control. In precision agriculture, neural networks and image processing eliminate human error. Sustainable agriculture and organic agricultural cultivation must start early. The proposed system classifies diseases and pests for plant safety. A healthy, fast-growing plantation is our goal. Early detection of viral diseases and insect infestations boosts crop productivity. Plant disease diagnostics focuses on leaves and buds. Stem and insect infections are covered. The suggested method classifies plant health and disease automatically. Peanut leaves develop Bacterial Blight from contagious illnesses like Cercospora Leaf Peanut. Boll Weevils, European Corn Borers, and Fall Army Worms attack cotton. The conditions are categorised. Image accuracy depends on lighting, resolution, location, and backdrop complexity. Various low-resolution pictures are enriched to better classification. Complex leaf intensity variations cause the system to misdiagnose illness or insect infestation. To avoid misclassification, the system segments the sample image using various methods to find lesions. Multilevel Segmentation (MS) improves Deep neural network feature extraction using approximate outlines. This method protects important pixels, eliminates unneeded pixels, and amplifies the lesion to control segmentation and reduce insufficient and excessive segmentation.

New computations of the fractional worms transmission model in wireless sensor network in view of new integral transform with statistical analysis; an analysis of information and communication technologies

Wireless sensor networks (WSNs) have attracted a lot of interest due to their enormous potential for both military and civilian uses. Worm attacks can quickly target WSNs because of the network's weak security. The worm can spread throughout the network by interacting with a single unsafe node. Moreover, the analysis of worm spread in WSNs can benefit from the use of mathematical epidemic models. We suggest a five-compartment model to characterize the mechanisms of worm proliferation with respect to time in WSN. Taking into account the ZZ transform convoluted with the Atangana-Baleanu-Caputo (ABC) fractional derivative operator, we employ it to analyze the characteristics and applications of the ZZ transformation using the Mittag-Leffler kernel. Moreover, we construct a new algorithm for the homotopy perturbation method (HPM) in conjunction with the ZZ transform technique to generate analytical solutions for the worm transmission model. Also, we address the qualitative aspects such as positivity, boundness, worm-free state, endemic state, basic reproduction number (R0) and worm-free equilibrium stability. Furthermore, we prove that the virus rate in sensor nodes is extinct if R0<1 and the virus persists if R0>1. In addition, we develop analytical findings to evaluate the series of solutions. Furthermore, a detailed statistical analysis is conducted to verify the nonlinear dynamics of the system by verifying the 0−1 test to determine whether uncertainty exists using approximation entropy and the C0 data. An extensive analysis of the vaccination class with respect to the transmitting class as well as the susceptible class is being used to investigate the effects of stepping up precautions on WP in WSN. Moreover, the modeling of the WSN revealed that reducing the fractional-order from 1 requires that the recommended approach be implemented at the highest rate so that there is no long-lasting immunization; instead, nodes remain briefly defensive before becoming vulnerable to future worm attacks.

Aplikasi Ekstrak Mimosa invisa L. dan Pupuk NPK Dosis Rendah dalam Pengendalian Hama dan Patogen Padi Gogo

This study aimed to determine the effect of Mimosa invisa L. weed extract application and a low dose of NPK fertilizer on the intensity of pest attack and pathogen infection on upland rice. This research has been done using Split Plot Design with 3 replications. The main plot was the dose of NPK fertilizer (50% and 100% NPK). The subplot was the concentration of weed extract (0, 1.25, 1.67, 2.5, and 5% concentrations). The variables observed include the intensity of grasshopper and case worm attack, Cercospora sp. that causes brown spots, and Xanthomonas oryzae pv. oryzae that causes bacterial leaf blight. The results showed that the application of weed extract under a low dose (50% NPK) has not been able to suppress the intensity of grasshopper and case worm attacks, and the intensity of the pathogen causes brown spots and bacterial leaf blight. However, 2,5% and 5% concentrations of weed extract can potentially suppress pathogen infection that causes brown spots on upland rice. Keywords: grasshopper, brown spots, Mimosa invisa L.weed extract, case worm, bacterial leaf blight, NPK fertilizer

Analisis Cara Kerja Sistem Deteksi Infeksi Worm Pada Komputer

Worms are a type of malware that has the ability to develop and spread automatically to other computers on a network without human interaction. This fast and undetectable infection capability makes worms a serious threat in the world of computer security. This research aims to explore the mechanisms and behavior of worm infection systems on computers. This study involves an in-depth analysis of the functions and methods used by worms to enter, infect, and exploit target computers. This researchalso explain how worms can cause damage to computer systems, steal confidential information, or even create botnet networks to carry out large-scale attacks. Research methods include collecting data from existing worm detection systems, analyzing system logs that occur, as well as simulations to understand how worms work in various scenarios. In addition, this research also consider protection and prevention techniques that can be used to protect computers and networks from worm attacks. Based on observations and experiments, the results of this research can be concluded that the worm infection system spreads through computers connected to the network or through other media on the network and does not require a certain moment to be a trigger to infect the target. It is hoped that the results of this research provide an in-depth understanding of worm infection systems on computers, allowing researchers and computer security practitioners to develop more effective protection strategies. Prevention and early detection efforts will be key in dealing with the growing threat from worms and similar types ofmalware.

Weibull Distributive Feature Scaling Multivariate Censored Extreme Learning Classification for Malicious IoT Network Traffic Detection

Malicious Network Traffic identification is the major problems in Internet of Things (IoT) infrastructure. From fast growth and popularization of IoT, threats and attacks in these networks are also increasing correspondingly. To detect Malicious Network Traffic on IoT surroundings, accurate scheme is significant to reduce protection risks on IoT. In this manuscript, Weibull distributive feature scaling multivariate censored extreme learning classification is proposed for Malicious IoT Network Traffic Detection (WDGMS- MCP-ELM-AD-IoT). Initially, the input datas are taken from UNSW-NB15 dataset. The datas are fed to pre-processing. During pre-processing, the redundancy eradication together with replacement of missing value is done for removing uncertainties based on local least squares. The pre-processed data is given to feature selection for choosing relevant features. The process of feature scaling is carried out by Weibull distributive generalized multidimensional scaling (WDGMS). Classification is done with the help of Multivariate Censored phi correlative Extreme learning machines classifier (MCP-ELM) depending on optimum features, which classifies normal or anomalous IoT Network Traffic. Among these, anomalous IoT Network Traffic involves fuzzers, analysis, backdoors, DoS, exploits, generic, reconnaissance, shell code, worm attacks. The proposed approach is activated in Python. The performance metrics, such as accuracy, F-Measure, precision specificity, sensitivity, and false positive rate is evaluated. The simulation of the proposed Weibull distributive feature scaling multivariate censored extreme learning classification for attack detection in IoT (WDGMS- MCP-ELM-AD-IoT) achieves better accuracy 39.45%, 34.58%, 28.29, better sensitivity 26.34%, 21.23%, 27.25%, better specificity 33.67%, 22.37%, 24.78% analyzed to the existing XGBoost-AD-IoT, DNN-AD-IoT and DRaNN-AD-IoT models respectively.

Critical Analysis of the Law Relating to Cyber Crime

Abstract: The Cyber Crime is most popular Crime in the world the Cyber Criminals Crimes are the Committed day by day .This Crime commit the Computer and Internet in this Crime Criminals are hacking the Computer system, Mobile like that Hacking virus ,Worm attacks, DOS attack etc. The personal data are Theft Like that Bank account detail, e-mail, Mobile messages .The computer as a weapon using the computer system to commit real world Crimes like Cyber terrorism, IPR(Intellectual property infringing) contravention, credit card frauds, EFT (Electronic fund transfer) Frauds, Pornography etc. The Cyber Criminals activities to the internet are termed as Cyber Crimes with the increasing popularity of online activity like that online banking and online Sopping In this term hearing the news day by day. The Cyber Crime law of The Cyber Criminals can involves the Crimes activity like that Theft, fraud, forgery, scandal and mischief all of which are the object to the IPC. The Abuse of the computer has also given birth to a scale of new generation Crimes that are addressed by the information technology .The Cyber law is a term used to explain legal issues affined to use of communications technology, particularly cyberspace the Internet.

Fasciolosis in Ruminants

Fasciolosis is disease caused by a liver worm called Fasciola is one of the dangerous parasites that affects the livestock specially sheep are wide spread in many countries of the world including some Arab countries. This worm attacks animals such as cows, buffaloes, camels, horses, donkeys, sheep, goats and rabbits that feed on grass and plants. The worms inhabit the bile ducts in the liver of humans and animals and they multiply and spread in all tissues causing many damages and risks to public health and great losses in animal production Therefore we will present the detailed aspects of this disease its classification and description of species symptoms methods of infection life cycle, epidemiology pathological diagnosis of prevention

Design of Graph-Based Layered Learning-Driven Model for Anomaly Detection in Distributed Cloud IoT Network

Within numerous IoT domains, recent IoT proliferation has influenced organizational activities and business procedures. The number of connected edge devices has increased dramatically, resulting in a vast stream of data that is prone to leakage and manipulation, decreasing the security level of the attacked IoT ecosystem. Anomaly detection systems based on graphs have been widely used to prevent network malfunction while considering the mergers of organizations involved, modeling their relationships, and integrating their structural features, content, and temporary features. We propose a multi-agent system that uses the collaborative environment of smart agents to find anomalies. Each agent implements a Graph-based Layered Learning-Driven (GLLD) network. To this end, we design an anomaly detection technique that attempts to efficiently monitor the entire network infrastructure to combat the spreading nature of cyber-attacks. BoT IoT dataset and Code Red worm attack dataset are used for training and testing various traffic distributions to demonstrate the outperformance of the model.

Detecting Zero-day Polymorphic Worms with Jaccard Similarity Algorithm

Zero-day polymorphic worms pose a serious threat to the security of Mobile systems and Internet infrastructure. In many cases, it is difficult to detect worm attacks at an early stage. There is typically little or no time to develop a well-constructed solution during such a worm outbreak. This is because the worms act only to spread from node to node and they bring security concerns to everyone using Internet via any static or mobile node. No system is safe from an aggressive worm crisis. However, many of the characteristics of a worm can be used to defeat it, including its predictable behavior and shared signatures. In this paper, we propose an efficient signature generation method based on string similarity algorithms to generate signatures for Zero-day polymorphic worms. Then, these signatures are practically applied to an Intrusion Detection System (IDS) to prevent the network from such attacks. The experimental results show the efficiency of the proposed approach compared to other existing mechanisms.

Dynamics of the worm transmission in wireless sensor network in the framework of fractional derivatives

Wireless sensor networks (WSNs) are subject to cyber attacks. Security of such networks is a significant priority for everyone. Due to the network's frail defense mechanisms, WSNs are easy targets for worm attacks. A single unsecured node via contact can effectively propagate the worm across the entire network. Mathematical epidemic models are helpful in analyzing worm propagation in WSNs. To understand the attacking and spreading dynamics of worms in WSNs, a fractional‐order compartmental epidemic model is investigated with susceptible, exposed, infected, recovered, and vaccinated nodes. Dynamical aspects such as boundedness, existence, and uniqueness of the solutions are presented with the help of fractional calculus theory. Global stability of the points of equilibrium are established. The projected nonlinear structure is examined numerically via the generalized Adams–Bashforth–Moulton method. This study demonstrates the influence of the fractional operator on WSNs dynamics and the efficiency of the numerical method.

Improved Model for the Stability Analysis of Wireless Sensor Network Against Malware Attacks

Ensuring security in a communication network is one of the underlying challenges of wireless sensor network due to critical operational constraints. Wireless sensor network (WSN) is an easy target of malware (worm, virus, malicious code, etc.) attacks due to weak security mechanism. Malware propagation outsets from a compromised sensor node and spreads in the whole WSN using wireless communication. Owing to epidemic nature of worm transmission in the network, it is essential to implement a defence mechanism against worm attacks. Motivated by malware quarantine, we propose an improved mathematical model which aggregates quarantine and vaccination techniques. We obtain the equilibrium points and other crucial parameters of the proposed model. We analyse the system stability under different conditions. Basic reproduction number determines whether malware is extinct in the system or not. It helps in the calculation of cutoff limit of the node density and communication radius. The impingement of various parameters in this model is analysed. The performance is observed to be significantly ameliorated than existing models and verified by extensive simulation results in terms of reducing the number of infectious nodes and decreasing the rate of malware propagation.

A SEIQR Model considering the Effects of Different Quarantined Rates on Worm Propagation in Mobile Internet

At present, Wi-Fi is a common medium for connecting smart devices to networks in factories. The application of mobile Internet in smart manufacturing system (SMIS) speeds up the process of smart manufacturing but also increases SMIS vulnerability to worm attack from mobile networks. In this paper, we propose a new SLBQR (susceptible-latent-breaking out-quarantined-recovered) model considering vaccination strategies with temporary immunity and quarantined strategies. Based on basic reproduction number, we give expression of quarantined rate φ and obtain the threshold ϕ∗ of quarantined rate φ such that the worm-free equilibrium is asymptotically stable when ϕ≥ϕ∗, implying that the worm dies out eventually and its attack remains under control; the endemic equilibrium is asymptotically stable when φ&lt;φ∗, namely, the worm is always persistent and spreading within a population. Hence, we give the quarantined strategy ϕ≥ϕ∗ to suppress the spread of virus. In addition, by theoretical analysis, we can conclude that even if the immunity time is unlimited, endemic equilibrium will not become worm-free. In other words, there is a mutation in the virus, which proves that there is no vaccination strategy with permanent immunity. Finally, we simulate our model with different temporary immune time and quarantine rates, and the results verify our theorem.

Modeling Worm Proliferation in Wireless Sensor Networks with Discrete Fractional Order System

Wireless sensor networks (WSNs) are at risk to cyber attacks and thus security is of vital concern. WSN is a soft target for worm attacks due to fragile defence mechanism in the network . A single unsecured node can essentially propogate the worm in the complete network via communication. Mathematical epidemic models are useful in the study of propagation of worms in WSNs. This work considers a fractional order discrete model of attacking and spreading dynamics of worms in WSNs of the form The proposed epidemic model is probed with the assistance of stability theory. Basic reproduction number (R0)is determined for the analysis of the dynamics of worm propagation in WSNs. The equilibrium states are computed and analyzed the stability. Basic reproduction number R0 enables to discover the threshold values for communication radius and node density distribution. If reproduction number is less than one, the worm free equilibrium state (WFE) is locally asymptotically stable (LAS) and if reproduction number is more than one then the endemic equilibrium state (EE) is asymptotically stable. Numerical illustrations affirm the consistency of the theoretical analysis and stimulating dynamical behavior of the system is observed.

Bioecology, damage and management of bud worm, Hendecasis duplifascialisis Hampson: An overview

Jasminum multiflorum, a non-fragrant flower species is mainly grown by small and marginal farmers in southern districts of Karnataka. The budworm, Hendecasis duplifascialisis Hampson has been posing a serious threat to jasmine cultivation in recent years. The species J. multiflorum is more prone to the bud worm attack. The total life cycle of the bud worm on J. multiflorum was found to be 27 ± 1.82 days. The natural enemy complex of bud worm includes the coccinellid and neuropteran predators, parasitoids like Bracon sp., Phanerotoma hendecasiella, Brachymeria sp. and Xanthopimpla sp. The green labled chemicals like chlorantraniliprole 18.5 SC, flubendiamide 480 SC, and spinosad 45 SC were more effective against bud worm and at the same time these chemicals are comparatively safe for those working every day in jasmine gardens.

Stability analysis and control strategies for worm attack in mobile networks via a VEIQS propagation model

Mobile devices are considerably pervasive in society, but also increase their vulnerability to worm attacks from mobile networks. In this paper, we propose a new Vulnerable-Exposed-Infectious-Quarantined-Secured worm propagation model with saturated incidence and strategies of both vaccination and quarantine. We obtain that the basic reproduction number R0 is a sharp threshold parameter such that the worm-free equilibrium is asymptotically stable for R0 ≤ 1, implying that the worm dies out eventually and its attack remains under control; the worm-existence equilibrium is asymptotically stable when R0 > 1, namely, the worm is always persistent and spreading within a population. This paper provides some novel insights to cyber security by that (a) the stability of worm-free equilibrium establishes the control strategies to reduce the intensity of worm attacks, and the optimal control strategy is proposed by using Pontryagins Minimum Principle; (b) the stability of worm-existence equilibrium predicts the tendency of worm propagation in a long run and assesses the level of the worm popularity by the final scale of infected devices. Numerical simulations are implemented to illustrate the feasibility of the theoretical results and the effectiveness of the control strategies.

A probability distribution function for investigating node infection and removal times

AbstractConsidering the important issue of computer infections by worms spread via networks, the theme of source identification has been a prominent research field that aims at investigating infection propagation including acquiring knowledge about the infection and the node removal times when a worm infection happens. This information helps in identifying the patient zero in the worm attack and may be used by computer forensic investigators and network administrators to spot the culprits and to identify related network vulnerabilities. In this paper, we tackle this problem by developing new probabilistic models based on Bayesian networks. We learn a probability distribution to calculate, at every time step, the probability that each node is infected by a scanning worm, using historical data and features extracted from the network and application layers. With the mentioned probability distribution, the node infection status can be inferred using feature values at each time step. We propose a four‐step method to investigate the time of infection and removal of each node probabilistically. First, features are extracted and derived from network traffic data. There are no suitable training and test datasets publicly available for our tests; therefore, we developed the training and test datasets using simulations of the Code Red II worm. Second, a prior model is built using training data. Third, the probabilistic model is built by the estimation of distribution algorithm. Fourth, the infection probability of nodes is inferred given the probability distribution and feature values at each time step. It has already been shown that the number of infectious nodes can be probabilistically approximated backward in time through the stochastic Back‐to‐Origin Markov model. We combine our first model with the prior stochastic Back‐to‐Origin Markov model to develop our second model. To evaluate our first and second models, we conducted experiments that show that these models can pinpoint the source node and the infection time of nodes with acceptable accuracy. It should be noted that our method could be employed with other propagating worm types including ransomware worms.

Optimizing Computer Worm Detection Using Ensembles

The scope of this research is computer worm detection. Computer worm has been defined as a process that can cause a possibly evolved copy of it to execute on a remote computer. It does not require human intervention to propagate neither does it attach itself to an existing computer file. It spreads very rapidly. Modern computer worm authors obfuscate the code to make it difficult to detect the computer worm. This research proposes to use machine learning methodology for the detection of computer worms. More specifically, ensembles are used. The research deviates from existing detection approaches by using dark space network traffic attributed to an actual worm attack to train and validate the machine learning algorithms. It is also obtained that the various ensembles perform comparatively well. Each of them is therefore a candidate for the final model. The algorithms also perform just as well as similar studies reported in the literature.

Zero-Day Signature Extraction for High-Volume Attacks

We present a basic tool for zero day attack signature extraction. Given two large sets of messages, $P$ the messages captured in the network at peacetime (i.e., mostly legitimate traffic) and $A$ the messages captured during attack time (i.e., contains many attack messages), we present a tool for extracting a set $S$ of strings that are frequently found in $A$ and not in $P$ , thus allowing the identification of the attack packets. This is an important tool in protecting sites on the Internet from worm attacks and distributed denial of service attacks and may also be useful for other problems, including command and control identification and the DNA-sequences analysis. The main contributions of this paper are the system we developed to extract the required signatures together with the string-heavy hitters problem definition and the algorithm for solving this problem. This algorithm finds popular strings of variable length in a set of messages, using, in a tricky way, the classic heavy-hitter algorithm as a building block. The algorithm runs in linear time requiring one-pass over the input. Our system makes use of this algorithm to extract the desired signatures. Furthermore, we provide an extended algorithm which is able to identify groups of signatures, often found together in the same packets, which further improves the quality of signatures generated by our system. Using our system, a yet unknown attack can be detected and stopped within minutes from attack start time.

Security model against worms attack in wireless sensor network

The wireless sensor network is an innovative category of communication network, which has earned universal attention due to its great potential in application of various areas. This is one of the insecure systems due to attack of worms. In order to efficaciously defend wireless sensor network against worms, we have proposed an epidemic model with two latent periods and vaccination. We have formulated ODE of the model and studied the dynamic behaviour of worm propagation as well as designed a model to secure the system from worm attack. The model has been simulated by MATLAB. In this proposed study, we have determined the basic reproduction number for the study of dynamic performance of worms in the wireless sensor network. The global stability of worm free equilibrium has been established using a Lyapunov function, while the simulation results helped in the validation of the theoretical analysis.

Security model against worms attack in wireless sensor network

Security model against worms attack in wireless sensor network