Biblio

The data of large-scale distributed demand-side iot devices are gradually migrated to the cloud. This cloud deployment mode makes it convenient for IoT devices to participate in the interaction between supply and demand, and at the same time exposes various vulnerabilities of IoT devices to the Internet, which can be easily accessed and manipulated by hackers to launch large-scale DDoS attacks. As an easy-to-understand supervised learning classification algorithm, KNN can obtain more accurate classification results without too many adjustment parameters, and has achieved many research achievements in the field of DDoS detection. However, in the face of high-dimensional data, this method has high operation cost, high cost and not practical. Aiming at this disadvantage, this chapter explores the potential of classical KNN algorithm in data storage structure, K-nearest neighbor search and hyperparameter optimization, and proposes an improved KNN algorithm for DDoS attack detection of demand-side IoT devices.

A successful phishing attack is prelude to various other severe attacks such as login credentials theft, unauthorized access to user’s confidential data, malware and ransomware infestation of victim’s machine etc. This paper proposes a real time lightweight machine learning based security framework for detection of phishing attacks through analysis of Uniform Resource Locators (URLs). The proposed framework initially extracts a set of highly discriminating and uncorrelated features from the URL string corpus. These extracted features are then used to transform the URL strings into their corresponding numeric feature vectors, which are eventually used to train various machine learning based classifier models for identification of malicious phishing URLs. Performance analysis of the proposed security framework on two well known datasets: Kaggle dataset and UNB dataset shows that it is capable of detecting malicious phishing URLs with high precision, while at the same time maintain a very low level of false positive rate. The proposed framework is also shown to outperform other similar security frameworks proposed in the literature.121https://www.kaggle.com/antonyj453/ur1dataset2https://www.unb.ca/cic/datasets/ur1-2016.htm1

With the proliferation of false redundant information on various e-commerce platforms, ineffective recommendations and other untrustworthy behaviors have seriously hindered the healthy development of e-commerce platforms. Modern recommendation systems often use side information to alleviate these problems and also increase prediction accuracy. One such piece of side information, which has been widely investigated, is trust. However, it is difficult to obtain explicit trust relationship data, so researchers infer trust values from other methods, such as the user-to-item relationship. In this paper, addressing the problems, we proposed a novel trust-based recommender model called UITrust, which uses user-item relationship value to improve prediction accuracy. With the improvement the traditional similarity measures by employing the entropies of user and item history ratings to reflect the global rating behavior on both. We evaluate the proposed model using two real-world datasets. The proposed model performs significantly better than the baseline methods. Also, we can use the UITrust to alleviate the sparsity problem associated with correlation-based similarity. In addition to that, the proposed model has a better computational complexity for making predictions than the k-nearest neighbor (kNN) method.

Nowadays, with the continuous increase in oil prices and the worldwide shift towards clean energy, all-electric vehicles are booming. Thence, these vehicles need widespread charging systems operating securely and reliably. Consequently, these charging systems need the most robust cybersecurity measures and strong authentication mechanisms to protect its user. This paper presents a new security scheme leveraging human biometrics in terms of iris recognition to defend against multiple types of cyber-attacks such as fraudulent identities, man-in-the-middle attacks, or unauthorized access to electric vehicle charging stations. Fundamentally, the proposed scheme implements a security mechanism based on the inherently unique characteristics of human eye biometric. The objective of the proposed scheme is to enhance the security of electric vehicle charging stations by using a low-cost and efficient authentication using k-Nearest Neighbours (KNN), which is a lightweight encryption algorithm.We tested our system on high-quality images obtained from the standard IITD iris database to search over the encrypted database and authenticate a legitimate user. The results showed that our proposed technique had minimal communication and computation overhead, which is quite suitable for the resource-limited charging station devices. Furthermore, we proved that our scheme outperforms other existing techniques.

With the rise of Internet of Things (IoT) devices, home network management and security are becoming complex. There is an urgent requirement to make smart home network management more efficient. This work proposes an SDN-based architecture to secure smart home networks through K-Nearest Neighbor (KNN) based device classifications and malicious traffic detection. The efficiency is enhanced by offloading the computation-intensive KNN model to a Field Programmable Gate Arrays (FPGA). Furthermore, we propose a custom KNN solution that exhibits the best performance on an FPGA compared with four alternative KNN instances (i.e., 78% faster than a parallel Bubble Sort-based implementation and 99% faster than three other sorting algorithms). Moreover, with 36,225 training samples, the proposed KNN solution classifies a test query with 95% accuracy in approximately 4 ms on an FPGA compared to 57 seconds on a CPU platform. This highlights the promise of FPGA-based platforms for edge computing applications in the smart home.

With fast turn of events and development of the web, malware is one of major digital dangers nowadays. Henceforth, malware detection is an important factor in the security of computer systems. Nowadays, attackers generally design polymeric malware [1], it is usually a type of malware [2] that continuously changes its recognizable feature to fool detection techniques that uses typical signature based methods [3]. That is why the need for Machine Learning based detection arises. In this work, we are going to obtain behavioral-pattern that may be achieved through static or dynamic analysis, afterward we can apply dissimilar ML techniques to identify whether it's malware or not. Behavioral based Detection methods [4] will be discussed to take advantage from ML algorithms so as to frame social-based malware recognition and classification model.

Data driven classification models are useful to assess quality of manufactured electronics. Because decisions are taken based on the models, their veracity is relevant, covering aspects such as accuracy, transparency and clarity. The proposed BB-Stepwise algorithm aims to improve the classification model transparency and accuracy of black box models. K-Nearest Neighbours (KNN) is a black box model which is easy to implement and has achieved good classification performance in different applications. In this paper KNN-Stepwise is illustrated for fault detection of electronics devices. The results achieved shows that the proposed algorithm was able to improve the accuracy, veracity and transparency of KNN models and achieve higher transparency and clarity, and at least similar accuracy than when using Decision Tree models.

Almost all smart appliances are operated through wireless sensor networks. With the passage of time, due to various applications, the WSN becomes prone to various external attacks. Preventing such attacks, Intrusion Detection strategy (IDS) is very crucial to secure the network from the malicious attackers. The proposed IDS methodology discovers the pattern in large data corpus which works for different types of algorithms to detect four types of Denial of service (DoS) attacks, namely, Grayhole, Blackhole, Flooding, and TDMA. The state-of-the-art detection algorithms, such as KNN, Naïve Bayes, Logistic Regression, Support Vector Machine (SVM), and ANN are applied to the data corpus and analyze the performance in detecting the attacks. The analysis shows that these algorithms are applicable for the detection and prediction of unavoidable attacks and can be recommended for network experts and analysts.

This study proposes a machine learning method based on stream homomorphic encryption computing for improving security and reducing computational time. A case study of mobile positioning based on k nearest neighbors ( kNN) is selected to evaluate the proposed method. The results showed the proposed method can save computational resources than others.

Cross-Site Scripting (XSS) is an attack most often carried out by attackers to attack a website by inserting malicious scripts into a website. This attack will take the user to a webpage that has been specifically designed to retrieve user sessions and cookies. Nearly 68% of websites are vulnerable to XSS attacks. In this study, the authors conducted a study by evaluating several machine learning methods, namely Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Naïve Bayes (NB). The machine learning algorithm is then equipped with the n-gram method to each script feature to improve the detection performance of XSS attacks. The simulation results show that the SVM and n-gram method achieves the highest accuracy with 98%.

Classifying malware programs is a research area attracting great interest for Anti-Malware industry. In this research, we propose a system that visualizes malware programs as images and distinguishes those using Convolutional Neural Networks (CNNs). We study the performance of several well-established CNN based algorithms such as AlexNet, ResNet and VGG16 using transfer learning approaches. We also propose a computationally efficient CNN-based architecture for classification of malware programs. In addition, we study the performance of these CNNs as feature extractors by using Support Vector Machine (SVM) and K-nearest Neighbors (kNN) for classification purposes. We also propose fusion methods to boost the performance further. We make use of the publicly available database provided by Microsoft Malware Classification Challenge (BIG 2015) for this study. Our overall performance is 99.4% for a set of 2174 test samples comprising 9 different classes thereby setting a new benchmark.

This paper introduces a biologically inspired associative data model and structure for finding nearest neighbors and similar patterns. The method can be used as an alternative to the classical approaches to accelerate the search for such patterns using various priorities for attributes according to the Sebestyen measure. The presented structure, together with algorithms developed in this paper can be useful in various computational intelligence tasks like pattern matching, recognition, clustering, classification, multi-criterion search etc. This approach is particularly useful for the on-line operation of associative neural network graphs. Graphs that dynamically develop their structure during learning on training data. The results of experiments show that the associative approach can substantially accelerate the nearest neighbor search and that associative structures can also be used as a model for KNN tasks. Finally, this paper presents how the associative structures can be used to self-organize data and represent knowledge about them in the associative way, which yields new search approaches described in this paper.

Fault detection in a machine at earlier stage can prevent severe damage and loss to the industries. Fault detection techniques are broadly classified into three categories; signature extraction-based, model-based and knowledge-based approach. Model-based techniques are efficient for raising an alarm signal if there is any fault in the machine. This paper focuses on one such model based-technique to identify the internal faults of induction machine. The model developed is deployed in the end to make it feasible to use in real time. K-Nearest Neighbors (KNN) and grid search cross validation (CV) have been used to train and optimize the model to give the best results. The advantage of proposed algorithm is the accuracy in prediction which has been seen to be 80%. Finally, a user friendly interface has been built using Flask, a python web framework.

Keeping Internet users safe from attacks and other threats is one of the biggest security challenges nowadays. Distributed Denial of Service (DDoS) [1] is one of the most common attacks. DDoS makes the system stop working by resource overload. Software Define Networking (SDN) [2] has recently emerged as a new networking technology offering an unprecedented programmability that allows network operators to dynamically configure and manage their infrastructures. The flexible processing and centralized management of SDN controller allow flexibly deploying complex security algorithms and mitigation methods. In this paper, we propose a new TCP-SYN flood attack mitigation in SDN networks using machine learning. By using a testbed, we implement the proposed algorithms, evaluate their accuracy and address the trade-off between the accuracy and capacity of the security device. The results show that the algorithms can mitigate TCP-SYN Flood attack over 96.

In today's time Software Defined Network (SDN) gives the complete control to get the data flow in the network. SDN works as a central point to which data is administered centrally and traffic is also managed. SDN being open source product is more prone to security threats. The security policies are also to be enforced as it would otherwise let the controller be attacked the most. The attacks like DDOS and DOS attacks are more commonly found in SDN controller. DDOS is destructive attack that normally diverts the normal flow of traffic and starts the over flow of flooded packets halting the system. Machine Learning techniques helps to identify the hidden and unexpected pattern of the network and hence helps in analyzing the network flow. All the classified and unclassified techniques can help detect the malicious flow based on certain parameters like packet flow, time duration, accuracy and precision rate. Researchers have used Bayesian Network, Wavelets, Support Vector Machine and KNN to detect DDOS attacks. As per the review it's been analyzed that KNN produces better result as per the higher precision and giving a lower falser rate for detection. This paper produces better approach of hybrid Machine Learning techniques rather than existing KNN on the same data set giving more accuracy of detecting DDOS attacks on higher precision rate. The result of the traffic with both normal and abnormal behavior is shown and as per the result the proposed algorithm is designed which is suited for giving better approach than KNN and will be implemented later on for future.

Understanding how to group a set of binary files into the piece of software they belong to is highly desirable for software profiling, malware detection, or enterprise audits, among many other applications. Unfortunately, it is also extremely challenging: there is absolutely no uniformity in the ways different applications rely on different files, in how binaries are signed, or in the versioning schemes used across different pieces of software. In this paper, we show that, by combining information gleaned from a large number of endpoints (millions of computers), we can accomplish large-scale application identification automatically and reliably. Our approach relies on collecting metadata on billions of files every day, summarizing it into much smaller "sketches", and performing approximate k-nearest neighbor clustering on non-metric space representations derived from these sketches. We design and implement our proposed system using Apache Spark, show that it can process billions of files in a matter of hours, and thus could be used for daily processing. We further show our system manages to successfully identify which files belong to which application with very high precision, and adequate recall.

Intrusion detection systems need to be both accurate and fast. Speed is important especially when operating at the network level. Additionally, many intrusion detection systems rely on signature based detection approaches. However, machine learning can also be helpful for intrusion detection. One key challenge when using machine learning, aside from the detection accuracy, is using machine learning algorithms that are fast. In this paper, several processing architectures are considered for use in machine learning based intrusion detection systems. These architectures include standard CPUs, GPUs, and cognitive processors. Results of their processing speeds are compared and discussed.

Protecting the privacy of user-identification data is fundamental to protect the information systems from attacks and vulnerabilities. Providing access to such data only to the limited and legitimate users is the key motivation for `Biometrics'. In `Biometric Systems' confirming a user's claim of his/her identity reliably, is more important than focusing on `what he/she really possesses' or `what he/she remembers'. In this paper the use of face image for biometric access is proposed using two multistage face recognition algorithms that employ biometric facial features to validate the user's claim. The proposed algorithms use standard algorithms and classifiers such as EigenFaces, PCA and LDA in stages. Performance evaluation of both proposed algorithms is carried out using two standard datasets, the Extended Yale database and AT&T database. Results using the proposed multi-stage algorithms are better than those using other standard algorithms. Current limitations and possible applications of the proposed algorithms are also discussed along, with further scope of making these robust to pose, illumination and noise variations.

Effective Personalized Mobile Search Using KNN, implements an architecture to improve user's personalization effectiveness over large set of data maintaining security of the data. User preferences are gathered through clickthrough data. Clickthrough data obtained is sent to the server in encrypted form. Clickthrough data obtained is classified into content concepts and location concepts. To improve classification and minimize processing time, KNN(K Nearest Neighborhood) algorithm is used. Preferences identified(location and content) are merged to provide effective preferences to the user. System make use of four entropies to balance weight between content concepts and location concepts. System implements client server architecture. Role of client is to collect user queries and to maintain them in files for future reference. User preference privacy is ensured through privacy parameters and also through encryption techniques. Server is responsible to carry out the tasks like training, reranking of the search results obtained and the concept extraction. Experiments are carried out on Android based mobile. Results obtained through experiments show that system significantly gives improved results over previous algorithm for the large set of data maintaining security.

This article presents results of the recognition process of acoustic fingerprints from a noise source using spectral characteristics of the signal. Principal Components Analysis (PCA) is applied to reduce the dimensionality of extracted features and then a classifier is implemented using the method of the k-nearest neighbors (KNN) to identify the pattern of the audio signal. This classifier is compared with an Artificial Neural Network (ANN) implementation. It is necessary to implement a filtering system to the acquired signals for 60Hz noise reduction generated by imperfections in the acquisition system. The methods described in this paper were used for vessel recognition.