Intrusion detection is one of the important security problems in todays cyber world. A significant number of techniques have been developed which are based on machine learning approaches. However, they are not very successful in identifying all types of intrusions. In this paper, a detailed investigation and analysis of various machine learning techniques have been carried out for finding the cause of problems associated with various machine learning techniques in detecting intrusive activities. Attack classification and mapping of the attack features is provided corresponding to each attack. Issues which are related to detecting low-frequency attacks using network attack dataset are also discussed and viable methods are suggested for improvement. Machine learning techniques have been analyzed and compared in terms of their detection capability for detecting the various category of attacks. Limitations associated with each category of them are also discussed. Various data mining tools for machine learning have also been included in the paper. At the end, future directions are provided for attack detection using machine learning techniques.

A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection

The Internet of Things (IoT) will be ripe for the deployment of novel machine learning algorithm for both network and application management. However, given the presence of massively distributed and private datasets, it is challenging to use classical centralized learning algorithms in the IoT. To overcome this challenge, federated learning can be a promising solution that enables on-device machine learning without the need to migrate the private end-user data to a central cloud. In federated learning, only learning model updates are transferred between end-devices and the aggregation server. Although federated learning can offer better privacy preservation than centralized machine learning, it has still privacy concerns. In this paper, first, we present the recent advances of federated learning towards enabling federated learning-powered IoT applications. A set of metrics such as sparsification, robustness, quantization, scalability, security, and privacy, is delineated in order to rigorously evaluate the recent advances. Second, we devise a taxonomy for federated learning over IoT networks. Finally, we present several open research challenges with their possible solutions.

Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges

The Internet of Things (IoT) will be ripe for the deployment of novel machine learning algorithms for both network and application management. However, given the presence of massively distributed and private datasets, it is challenging to use classical centralized learning algorithms in the IoT. To overcome this challenge, federated learning can be a promising solution that enables on-device machine learning without the need to migrate the private end-user data to a central cloud. In federated learning, only learning model updates are transferred between end-devices and the aggregation server. Although federated learning can offer better privacy preservation than centralized machine learning, it has still privacy concerns. In this paper, first, we present the recent advances of federated learning towards enabling federated learning-powered IoT applications. A set of metrics such as sparsification, robustness, quantization, scalability, security, and privacy, is delineated in order to rigorously evaluate the recent advances. Second, we devise a taxonomy for federated learning over IoT networks. Third, we propose two IoT use cases of dispersed federated learning that can offer better privacy preservation than federated learning. Finally, we present several open research challenges with their possible solutions.

Byte-level malware classification based on markov images and deep learning

A distributed denial of service (DDoS) attack represents a major threat to service providers. More specifically, a DDoS attack aims to disrupt and deny services to legitimate users by overwhelming the target with a massive number of malicious requests. A cyberattack of this kind is likely to result in tremendous economic losses for businesses and service providers due to increasing both operating and financial costs. In recent years, machine learning (ML) techniques have been widely used to prevent DDoS attacks. Indeed, many defense systems have been transformed into smart and intelligent systems through the use of ML techniques, which allow them to defeat DDoS attacks. This paper analyzes recent studies concerning DDoS detection methods that have adapted single and hybrid ML approaches in modern networking environments. Additionally, the paper discusses different DDoS defense systems based on ML techniques that make use of a virtualized environment, including cloud computing, software-defined network, and network functions virtualization environments. As the development of the Internet of Things (IoT) has been the subject of significant research attention in recent years, the paper also discusses ML approaches as security solutions against DDoS attacks in IoT environments. Furthermore, the paper recommends a number of directions for future research. This paper is intended to assist the research community with the design and development of effective defense systems capable of overcoming different types of DDoS attacks.

/pdf/machine-learning-approaches-for-combating-distributed-denial-4br43qv4ja.pdf

Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments

Ciphering algorithms play a main role in this digital era, especially when data are transferred via internet Many algorithms has been developed and used to encrypt and decrypt data for secure storing or transmission At present, both synchronous and asynchronous encryptions are used to achieve the high security and to speed up the encryption time and process Advanced Encryption Standard (AES) plays a prominent role among the synchronous encryption algorithms It is also known as Rijndael Algorithm Because of high performance of AES algorithm, it has been chosen as a standard among symmetric cipher algorithms In this paper, we would like to propose a symmetric encryption algorithm Modification is based on AES and we add an additional or second key Another modification is also done at SubBytes step by adding the transportation operation in the original SubBytes operation To analyze the performance of the modified proposed algorithm, Java language is used to implement the algorithm and then the performance is analyzed After analyzing and verifying the experimental results, the proposed revised algorithm also shows good performance and high security from the cryptographic point of view Based on the results of comparison between modified AES and original AES algorithm, our proposed algorithm can be used as a symmetric encryption algorithm, especially for the applications that share sensitive data files via insecure network

Modification of AES Algorithm by Using Second Key and Modified SubBytes Operation for Text Encryption

Due to the rapid growth of targeted malware attacks, malware analysis and family classification are important for all types of users such as personal, enterprise, and government. Traditional signature-based malware detection and anti-virus systems fail to classify the new variants of unknown malware into their corresponding families. Therefore, we propose malware family classification system for 11 malicious families by extracting their prominent API features from the reports of enhanced and scalable version of cuckoo sandbox. Moreover, the proposed system contributes feature extraction algorithm, feature reduction and representation procedure for identifying and representing the extracted feature attributes. To classify the different types of malicious software Random Forest (RF), K-Nearest Neighbor (KNN), and Decision Table (DT) machine learning multi-class classifiers have been used in this system and RF and KNN classifiers provide 95.8% high accuracy in malware family classification.

Malicious Software Family Classification using Machine Learning Multi-class Classifiers

With the rapidly growing and wide spread use of computer networks, the number of new threats has grown extensively. Automated rule induction procedures for detecting these threats, like machine learning and statistical techniques result in rules that lack generalization and maintainability. In this paper, we focus on detailed study of different types of attacks using NSL KDD dataset by manually developing rules through incorporation of attack signatures. It results in meaningful but weak rules as it is difficult to define thresholds. This paper utilizes a hybrid procedure for developing rules by combining expert knowledge with automated techniques to improve readability, comprehensibility, and maintainability of rules. Through the proposed rule-formation technique, heuristic rules were developed for different attack types included in NSL KDD dataset. Empirical results show that high detection rates with low false alarms are observed for different attack types in the dataset. The utilized techniques also highlighted a mislabeling problem in the NSL KDD dataset for the R2L and U2R attacks considered.

Heuristic Rules for Attack Detection Charged by NSL KDD Dataset

With the rapidly growth of mobile technology, the lifestyle of people is becoming reliant on various mobile devices like smartphones, tablets and so on. By using these mobile devices, users can perform transactions such as purchasing goods, making bank transaction and paying for tickets and coupons. It may bring flexibility and convenience for communication, storing and sharing data, social media networks and entertainments. On the other side, criminals may commit the crimes by using Android devices. Therefore, the investigators need to prepare enough Android forensics tools for investigation and analysis. In this paper, we proposed the applicable tool (ANDROSICS) for Android forensics which emphasizes on evidence data collection process. This tool can support Live Forensics, Dead Forensics, Data Collection, Bypass Screen Lock, Imaging, Android Debug Bridge Utility, Virtual Keys and many forensic features.

Evidence Data Collection with ANDROSICS Tool for Android Forensics

http://www.theijes.com/papers/vol6-issue1/Version-3/A0601030107.pdf

Proposed Effective Solution for Cybercrime Investigation in Myanmar

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