Unknown Attack Detection:Combining Relabeling and Hybrid Intrusion Detection

Detection of unknown attacks like a zero-day attack is a research field that has long been studied.Recently,advances in Machine Learning(ML)and Artificial Intelligence(AI)have led to the emergence of many kinds of attack-generation tools developed using these technologies to evade detection skillfully.Anomaly detection and misuse detection are the most commonly used techniques for detecting intrusion by unknown attacks.Although anomaly detection is adequate for detecting unknown attacks,its disadvantage is the possibility of high false alarms.Misuse detection has low false alarms;its limitation is that it can detect only known attacks.To overcome such limitations,many researchers have proposed a hybrid intrusion detection that integrates these two detection techniques.This method can overcome the limitations of conventional methods and works better in detecting unknown attacks.However,this method does not accurately classify attacks like similar to normal or known attacks.Therefore,we proposed a hybrid intrusion detection to detect unknown attacks similar to normal and known attacks.In anomaly detection,the model was designed to perform normal detection using Fuzzy c-means(FCM)and identify attacks hidden in normal predicted data using relabeling.In misuse detection,the model was designed to detect previously known attacks using Classification and Regression Trees(CART)and apply Isolation Forest(iForest)to classify unknown attacks hidden in known attacks.As an experiment result,the application of relabeling improved attack detection accuracy in anomaly detection by approximately 11%and enhanced the performance of unknown attack detection in misuse detection by approximately 10%.

Highly Sensitive Detection of Deoxyribonucleic Acid Hybridization Using Au-Gated AlInN/GaN High Electron Mobility Transistor-Based Sensors

Gallium nitride- （GaN） based high electron mobility transistors （HEMTs） provide a good platform for biological detection. In this work, both Au-gated AlInN/GaN HEMT and AlGaN/GaN HEMT biosensors are fabricated for the detection of deoxyribonucleic acid （DNA） hybridization. The Au-gated AIInN/GaN HEMT biosensor exhibits higher sensitivity in comparison with the AlGaN/GaN HEMT biosensor. For the former, the drain-source current （VDS = 0.5 V） shows a clear decrease of 69μA upon the introduction of 1μmolL^-1 （μM） complimentary DNA to the probe DNA at the sensor area, while for the latter it is only 38 μA. This current reduction is a notable indication of the hybridization. The high sensitivity can be attributed to the thinner barrier of the AlInN/GaN heterostructure, which makes the two-dimensional electron gas channel more susceptible to a slight change of the surface charge.

YOLOV5s object detection based on Sim SPPF hybrid pooling

Aiming at the problem of low surface defect detection accuracy of industrial products, an object detection method based on simplified spatial pyramid pooling fast(Sim SPPF) hybrid pooling improved you only look once version 5s(YOLOV5s) model is proposed. The algorithm introduces channel attention(CA) module, simplified SPPF feature vector pyramid and efficient intersection over union(EIOU) loss function. Feature vector pyramids fuse high-dimensional and low-dimensional features, which makes semantic information richer. The CA mechanism performs maximum pooling and average pooling operations on the feature map. Hybrid pooling comprehensively improves detection computing efficiency and accurate deployment ability. The results show that the improved YOLOV5s model is better than the original YOLOV5s model. The average test accuracy(mAP) can reach 91.8%, which can be increased by 17.4%, and the detection speed can reach 108 FPS, which can be increased by 18 FPS. The improved model is practicable, and the overall performance is better than other conventional models.

A Novel Radius Adaptive Based on Center-Optimized Hybrid Detector Generation Algorithm

Negative selection algorithm(NSA)is one of the classic artificial immune algorithm widely used in anomaly detection.However,there are still unsolved shortcomings of NSA that limit its further applications.For example,the nonselfdetector generation efficiency is low;a large number of nonselfdetector is needed for precise detection;low detection rate with various application data sets.Aiming at those problems,a novel radius adaptive based on center-optimized hybrid detector generation algorithm(RACO-HDG)is put forward.To our best knowledge,radius adaptive based on center optimization is first time analyzed and proposed as an efficient mechanism to improve both detector generation and detection rate without significant computation complexity.RACO-HDG works efficiently in three phases.At first,a small number of self-detectors are generated,different from typical NSAs with a large number of self-sample are generated.Nonself-detectors will be generated from those initial small number of self-detectors to make hybrid detection of self-detectors and nonself-detectors possible.Secondly,without any prior knowledge of the data sets or manual setting,the nonself-detector radius threshold is self-adaptive by optimizing the nonself-detector center and the generation mechanism.In this way,the number of abnormal detectors is decreased sharply,while the coverage area of the nonself-detector is increased otherwise,leading to higher detection performances of RACOHDG.Finally,hybrid detection algorithm is proposed with both self-detectors and nonself-detectors work together to increase detection rate as expected.Abundant simulations and application results show that the proposed RACO-HDG has higher detection rate,lower false alarm rate and higher detection efficiency compared with other excellent algorithms.

Multi-sensor Hybrid Fusion Algorithm Based on Adaptive Square-root Cubature Kalman Filter

In the normal operation condition, a conventional square-root cubature Kalman filter （SRCKF） gives sufficiently good estimation results. However, if the measurements are not reliable, the SRCKF may give inaccurate results and diverges by time. This study introduces an adaptive SRCKF algorithm with the filter gain correction for the case of measurement malfunctions. By proposing a switching criterion, an optimal filter is selected from the adaptive and conventional SRCKF according to the measurement quality. A subsystem soft fault detection algorithm is built with the filter residual. Utilizing a clear subsystem fault coefficient, the faulty subsystem is isolated as a result of the system reconstruction. In order to improve the performance of the multi-sensor system, a hybrid fusion algorithm is presented based on the adaptive SRCKF. The state and error covariance matrix are also predicted by the priori fusion estimates, and are updated by the predicted and estimated information of subsystems. The proposed algorithms were applied to the vessel dynamic positioning system simulation. They were compared with normal SRCKF and local estimation weighted fusion algorithm. The simulation results show that the presented adaptive SRCKF improves the robustness of subsystem filtering, and the hybrid fusion algorithm has the better performance. The simulation verifies the effectiveness of the proposed algorithms.

An efficient hybrid system for anomaly detection in social networks

Anomaly detection has been an essential and dynamic research area in the data mining.A wide range of applications including different social medias have adopted different state-of-the-art methods to identify anomaly for ensuring user’s security and privacy.The social network refers to a forum used by different groups of people to express their thoughts,communicate with each other,and share the content needed.This social networks also facilitate abnormal activities,spread fake news,rumours,misinformation,unsolicited messages,and propaganda post malicious links.Therefore,detection of abnormalities is one of the important data analysis activities for the identification of normal or abnormal users on the social networks.In this paper,we have developed a hybrid anomaly detection method named DT-SVMNB that cascades several machine learning algorithms including decision tree(C5.0),Support Vector Machine(SVM)and Naïve Bayesian classifier(NBC)for classifying normal and abnormal users in social networks.We have extracted a list of unique features derived from users’profile and contents.Using two kinds of dataset with the selected features,the proposed machine learning model called DT-SVMNB is trained.Our model classifies users as depressed one or suicidal one in the social network.We have conducted an experiment of our model using synthetic and real datasets from social network.The performance analysis demonstrates around 98%accuracy which proves the effectiveness and efficiency of our proposed system.

Direct Electrochemical Detection of Oligonucleotide Hybridization on Poly（thionine） Film

In this work, the application of a conducting polymer, poly（thionine）, modified electrode as matrix to DNA immobilization as well as transducer to label-free DNA hybridization detection was introduced. The electropolymerization of thionine onto electrode surface was carried out by a simple two-step method, which involved a preanodization of glassy carbon electrode at a constant positive potential in thionine solution following cyclic voltammetry scans in the solution. Electrochemical detection was performed by differential pulse voltammetry in the electroactivity potential domain of poly（thionine）. The resulting poly（thionine） modified electrode showed a good stability and electroactivity in aqueous media during a near neutral pH range. Additionally, the pendant amino groups on the poly（thionine） chains enabled poly（thionine） modified electrode to immobilize phosphate group terminated DNA probe via covalent linkage. Hybridization process induced a clear decrease in poly（thionine） redox current, which was corresponding to the decrease in poly（thionine） electroactivity after double stranded DNA was formed on the polymer film. The detection limit of this electrochemical DNA hybridization sensor was 1.0 × 10^-10mol/L. Compared with complementary sequence, the hybridization signal values of 1-base mismatched and 3-base mismatched samples were 63.9% and 9.2%, respectively.

DETECTION OF CYTOMEGALOVIRUS GENOME BY IN SITU HYBRIDIZATION IN PARAFFIN EMBEDDED ENDOMYOCARDIAL BIOPSY SPECIMENS OF VIRAL MYOCARDITIS

Cytomegalovirus (CMV) genes were detected by in situ hybridization in 25 Chinese patients with viral myocarditis (VMC). The positive hybridization signals werre found in cardiomyocytes (6 cases, 24%), capillary endothelial cells (4 cases, 16%) and interstitial cells (7 cases, 28%). The difference between VMC and control group (16 cases died of brain trauma and 10 cases of congenital heart diseases was statistically significant. There was no definite pathomorphological relationship between the detection of CMV genes and myocardial lesions. The results suggest that CMV infection may be one of the causes of myocarditis and chronic stimulation of the immune system induced by CMV may be a possible pathogenesis of this disease.

Developing a Nano-Biosensor for DNA Hybridization Using a New Electroactive Label

Development of electrochemical DNA hybridization biosensors based on carbon paste electrode （CPE） and gold nanoparticle modified carbon paste electrode （NGMCPE） as transducers and ethyl green （EG） as a new electroac- tive label is described. Electrochemical impedance spectroscopy and cyclic voltammetry techniques were applied for the investigation and comparison of bare CPE and NGMCPE surfaces. Our voltammetric and spectroscopic studies showed gold nanoparticles are enable to facilitate electron transfer between the accumulated label on DNA probe modified electrode and electrode surface and enhance the electrical signals and lead to an improved detection limit. The immobilization of a 15-mer single strand oligonucleotide probe on the working electrodes and hybridiza- tion event between the probe and its complementary sequence as a target were investigated by differential pulse voltammetry （DPV） responses of the EG accumulated on the electrodes. The effects of some experimental variables on the performance of the biosensors were investigated and optimum conditions were suggested. The selectivity of the biosensors was studied using some non-complementary oligonucleotides. Finally the detection limits were calculated as 1.35×10^-10 mol/L and 5.16×10^-11 mol/L on the CPE and NEGCPE, respectively. In addition, the bio-sensors exhibited a good selectivity, reproducibility and stability for the determination of DNA sequences.