Enhancing the Efficiency of Attack Detection System Using Feature selection and Feature Discretization Methods

Intrusion detection technologies have grown in popularity in recent years using machine learning. The variety of new security attacks are increasing, necessitating the development of effective and intelligent countermeasures. The existing intrusion detection system (IDS) uses Signature or Anomaly based detection systems with machine learning algorithms to detect malicious activities. The Signature-based detection rely only on signatures that have been pre-programmed into the systems, detect known attacks and cannot detect any new or unusual activity. The Anomaly based detection using supervised machine learning algorithm detects only known threats. To address this issue, the proposed model employs an unsupervised machine learning approach for detecting attacks. This approach combines the Sub Space Clustering and One Class Support Vector Machine algorithms and utilizes feature selection methods such as Chi-square, as well as Feature Discretization Methods like Equal Width Discretization to identify both known and undiscovered assaults. The results of the experiments using proposed model outperforms several of the existing system in terms of detection rate and accuracy and decrease in the computational time

Search for unusual objects in the WISE Survey

Automatic source detection and classification tools based on machine learning (ML) algorithms are growing in popularity due to their efficiency when dealing with large amounts of data simultaneously and their ability to work in multidimensional parameter spaces. In this work, we present a new, automated method of outlier selection based on support vector machine (SVM) algorithm called one-class SVM (OCSVM), which uses the training data as one class to construct a model of 'normality' in order to recognize novel points. We test the performance of OCSVM algorithm on \textit{Wide-field Infrared Survey Explorer (WISE)} data trained on the Sloan Digital Sky Survey (SDSS) sources. Among others, we find $\sim 40,000$ sources with abnormal patterns which can be associated with obscured and unobscured active galactic nuclei (AGN) source candidates. We present the preliminary estimation of the clustering properties of these objects and find that the unobscured AGN candidates are preferentially found in less massive dark matter haloes ($M_{DMH}\sim10^{12.4}$) than the obscured candidates ($M_{DMH}\sim 10^{13.2}$). This result contradicts the unification theory of AGN sources and indicates that the obscured and unobscured phases of AGN activity take place in different evolutionary paths defined by different environments.Comment: 4 figures, 6 page

Machine-Learning-Based Detecting of Eyelid Closure and Smiling Using Surface Electromyography of Auricular Muscles in Patients with Postparalytic Facial Synkinesis: A Feasibility Study

Surface electromyography (EMG) allows reliable detection of muscle activity in all nine intrinsic and extrinsic ear muscles during facial muscle movements. The ear muscles are affected by synkinetic EMG activity in patients with postparalytic facial synkinesis (PFS). The aim of the present work was to establish a machine-learning-based algorithm to detect eyelid closure and smiling in patients with PFS by recording sEMG using surface electromyography of the auricular muscles. Sixteen patients (10 female, 6 male) with PFS were included. EMG acquisition of the anterior auricular muscle, superior auricular muscle, posterior auricular muscle, tragicus muscle, orbicularis oculi muscle, and orbicularis oris muscle was performed on both sides of the face during standardized eye closure and smiling tasks. Machine-learning EMG classification with a support vector machine allowed for the reliable detection of eye closure or smiling from the ear muscle recordings with clear distinction to other mimic expressions. These results show that the EMG of the auricular muscles in patients with PFS may contain enough information to detect facial expressions to trigger a future implant in a closed-loop system for electrostimulation to improve insufficient eye closure and smiling in patients with PFS

A Two-stage Flow-based Intrusion Detection Model ForNext-generation Networks

The next-generation network provides state-of-the-art access-independent services over converged mobile and fixed networks. Security in the converged network environment is a major challenge. Traditional packet and protocol-based intrusion detection techniques cannot be used in next-generation networks due to slow throughput, low accuracy and their inability to inspect encrypted payload. An alternative solution for protection of next-generation networks is to use network flow records for detection of malicious activity in the network traffic. The network flow records are independent of access networks and user applications. In this paper, we propose a two-stage flow-based intrusion detection system for next-generation networks. The first stage uses an enhanced unsupervised one-class support vector machine which separates malicious flows from normal network traffic. The second stage uses a self-organizing map which automatically groups malicious flows into different alert clusters. We validated the proposed approach on two flow-based datasets and obtained promising results

Performance Comparisson Human Activity Recognition using Simple Linear Method

Human activity recognition (HAR) with daily activities have become leading problems in human physical analysis. HAR with wide application in several areas of human physical analysis were increased along with several machine learning methods. This topic such as fall detection, medical rehabilitation or other smart appliance in physical analysis application has increase degree of life. Smart wearable devices with inertial sensor accelerometer and gyroscope were popular sensor for physical analysis. The previous research used this sensor with a various position in the human body part. Activities can classify in three class, static activity (SA), transition activity (TA), and dynamic activity (DA). Activity from complexity in activities can be separated in low and high complexity based on daily activity. Daily activity pattern has the same shape and patterns with gathering sensor. Dataset used in this paper have acquired from 30 volunteers. Seven basic machine learning algorithm Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, Gradient Boosted and K-Nearest Neighbor. Confusion activities were solved with a simple linear method. The purposed method Logistic Regression achieves 98% accuracy same as SVM with linear kernel, with same result hyperparameter tuning for both methods have the same accuracy. LR and SVC its better used in SA and DA without TA in each recognizing

Sarcasm Detection and User Behaviour Analysis

Sarcasm is a sort of sentiment where public expresses their negative emotions using positive word within the text. It is very tough for humans to acknowledge. In this way we show the interest in sarcasm detection of social media text, particularly in tweets. In this paper we propose new method pattern based approach for sarcasm detection, and also used behavioral modelling approach for effective sarcasm detection by analyzing the content of tweets however by conjoint exploiting the activity traits of users derived from their past activities. In this way we propose the different method for sarcasm detection such as, Sentiment-related Features, Punctuation-Related Features, Syntactic and Semantic Features, Pattern-Related Features approach for detection of sarcasm in the tweet. We also develop the behavioural modeling approach to check the user emotion and sentiment analysis. By using the various classifiers such as TREE, Support Vector Machine (SVM), BOOST and Maximum Entropy, we check the accuracy and performance. Our proposed approach reaches an accuracy of 94 %

A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection

The digital transformation faces tremendous security challenges. In particular, the growing number of cyber-attacks targeting Internet of Things (IoT) systems restates the need for a reliable detection of malicious network activity. This paper presents a comparative analysis of supervised, unsupervised and reinforcement learning techniques on nine malware captures of the IoT-23 dataset, considering both binary and multi-class classification scenarios. The developed models consisted of Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Isolation Forest (iForest), Local Outlier Factor (LOF) and a Deep Reinforcement Learning (DRL) model based on a Double Deep Q-Network (DDQIN), adapted to the intrusion detection context. The most reliable performance was achieved by LightGBM. Nonetheless, iForest displayed good anomaly detection results and the DRL model demonstrated the possible benefits of employing this methodology to continuously improve the detection. Overall, the obtained results indicate that the analyzed techniques are well suited for IoT intrusion detection.The present work was done and funded in the scope of the European Union’s Horizon 2020 research and innovation program, under project SeCoIIA (grant agreement no. 871967). This work has also received funding from UIDP/00760/2020.info:eu-repo/semantics/publishedVersio