Biblio

Computer networks are exposed to cyber related attacks due to the common usage of internet, as the result of such, several intrusion detection systems (IDSs) were proposed by several researchers. Among key research issues in securing network is detecting intrusions. It helps to recognize unauthorized usage and attacks as a measure to ensure the secure the network's security. Various approaches have been proposed to determine the most effective features and hence enhance the efficiency of intrusion detection systems, the methods include, machine learning-based (ML), Bayesian based algorithm, nature inspired meta-heuristic techniques, swarm smart algorithm, and Markov neural network. Over years, the various works being carried out were evaluated on different datasets. This paper presents a thorough review on various research articles that employed single, hybrid and ensemble classification algorithms. The results metrics, shortcomings and datasets used by the studied articles in the development of IDS were compared. A future direction for potential researches is also given.

Vehicular Ad hoc Networks commonly abbreviated as VANETs, are an important component of MANET. VANET refers to the group of vehicles that are interlinked to one another through wireless network. Along with technology, comes the threats. Like other wireless networks, VANETs also are vulnerable to various security threats. Security in VANETs is a major issue that attracted many researchers and academicians. One small security breach can cause a big damage in case of VANETs as in this case human lives are involved. Intrusion Detection Systems (IDS) are employed in VANETs in order to detect and identify any malicious activity in the network. The IDS works by analysing the network and detecting any intrusions tried or made in the network so that proper steps could be taken timely to prevent damage from such activities. This paper reviews Intrusion Detection systems, classification of IDS based on various factors and then the architecture of IDS. We then reviewed some of the recent and important intrusion detection research works and then compared them with one another.

Wireless sensor networks (WSN) are used to perceive many data such as temperature, vibration, pressure in the environment and to produce results; it is widely used, including in critical fields such as military, intelligence and health. However, because of WSNs have different infrastructure and architecture than traditional networks, different security measures must be taken. In this study, an intrusion detection system (IDS) is modeled to ensure WSN security. Since the signature, misuse and anomaly based detection methods for intrusion detection systems are insufficient to provide security alone, a hybrid model is proposed in which these methods are used together. In the hybrid model, anomaly rules were defined for attack detection, and machine learning algorithms BayesNet, J48 and Random Forest were used to classify normal and abnormal traffic. Unlike the studies in the literature, CSE-CIC-IDS2018, the most up-to-date data set, was used to create attack profiles. Considering both hardware constraints and battery capacities of WSNs; the data was pre-processed in accordance with data mining principles. The results showed that the developed model has high accuracy and low false alarm rate.

Intrusion Detection System (IDS) is one of the most important security tool for many security issues that are prevailing in today's cyber world. Intrusion Detection System is designed to scan the system applications and network traffic to detect suspicious activities and issue an alert if it is discovered. So many techniques are available in machine learning for intrusion detection. The main objective of this project is to apply machine learning algorithms to the data set and to compare and evaluate their performances. The proposed application has used the SVM (Support Vector Machine) and ANN (Artificial Neural Networks) Algorithms to detect the intrusion rates. Each algorithm is used to detect whether the requested data is authorized or contains any anomalies. While IDS scans the requested data if it finds any malicious information it drops that request. These algorithms have used Correlation-Based and Chi-Squared Based feature selection algorithms to reduce the dataset by eliminating the useless data. The preprocessed dataset is trained and tested with the models to obtain the prominent results, which leads to increasing the prediction accuracy. The NSL KDD dataset has been used for the experimentation. Finally, an accuracy of about 48% has been achieved by the SVM algorithm and 97% has been achieved by ANN algorithm. Henceforth, ANN model is working better than the SVM on this dataset.

Bluetooth Low Energy mesh networks are emerging as new standard of short burst communications. While security of the messages is guaranteed thought standard encryption techniques, little has been done in terms of actively protecting the overall network in case of attacks aiming to undermine its integrity. Although many network analysis and risk mitigation techniques are currently available, they require considerable amounts of data coming from both legitimate and attack scenarios to sufficiently discriminate among them, which often turns into the requirement of a complete description of the traffic flowing through the network. Furthermore, there are no publicly available datasets to this extent for BLE mesh networks, due most to the novelty of the standard and to the absence of specific implementation tools. To create a reliable mechanism of network analysis suited for BLE in this paper we propose a machine learning Intrusion Detection System (IDS) based on pattern classification and recognition of the most classical denial of service attacks affecting this kind of networks, working on a single internal node, thus requiring a small amount of information to operate. Moreover, in order to overcome the gap created by the absence of data, we present our data collection system based on ESP32 that allowed the collection of the packets from the Network and the Model layers of the BLE Mesh stack, together with a set of experiments conducted to get the necessary data to train the IDS. In the last part, we describe some preliminary results obtained by the experimental setups, focusing on its strengths, as well as on the aspects where further analysis is required, hence proposing some improvements of the classification model as future work. Index Terms-Bluetooth, BLE Mesh, Intrusion Detection System, IoT, network security.

An unprecedented upsurge in the number of cyberattacks and threats is the corollary of ubiquitous internet connectivity. Among a variety of threats and attacks, Denial of Service (DoS) attacks are crucial and conventional mechanisms currently being used for detection/ identification of these attacks are not adequate. The use of real-time and robust mechanisms is the way to handle this. Machine learning-based techniques have been extensively used for this in the recent past. In this paper, a robust mechanism using Support Vector Machine Based Auto-Encoder is proposed for identifying DoS attacks. The proposed technique is tested on the CICIDS dataset and has given 99.32 % accuracy for DoS attacks. To study the effect of the number of features on the performance of the technique, a discriminant component analysis is deployed for feature reduction and independent experiments, namely SVM with 25 features, SVM with 30 features, SVM with 35 features, and PCA-SVM with 25 features, are conducted. From the experiments, it is observed that AE-SVM has performed better than others.

Probabilistic security metrics estimate the vulnerability of a network in terms of the likelihood of an attacker reaching the goal states (of a network) by exploiting the attack graph paths. The probability computation depends upon several assumptions regarding the possible attack scenarios. In this paper, we extend the existing security metric to model networks with intrusion detection systems and their associated uncertainties and time latencies. We consider learning capabilities of attackers as well as detection systems. Estimation of risk is obtained by using the attack paths that are undetectable owing to the latency of the detection system. Thus, we define the overall vulnerability (of a network) as a function of the time window available to an attacker for repeated exploring (via learning) and exploitation of a network, before the attack is mitigated by the detection system. Finally, we consider the realistic scenario where an attacker explores and abandons various partial paths in the attack graph before the actual exploitation. A dynamic programming formulation of the vulnerability computation methodology is proposed for this scenario. The nature of these metrics are explained using a case study showing the vulnerability spectrum from the case of zero detection latency to a no detection scenario.

The ever-evolving computers has its implications on the data and information and the threats that they are exposed to. With the exponential growth of internet, the chances of data breach are highly likely as unauthorized and ill minded users find new ways to get access to the data that they can use for their plans. Most of the systems today have well designed measures that examine the information for any abnormal behavior (Zero Day Attacks) compared to what has been seen and experienced over the years. These checks are done based on a predefined identity (signature) of information. This is being termed as Intrusion Detection Systems (IDS). The concept of IDS revolves around validation of data and/or information and detecting unauthorized access attempts with an intention of manipulating data. High Performance Soft Computing (HPSC) aims to internalize cumulative adoption of traditional and modern attempts to breach data security and expose it to high scale damage and altercations. Our effort in this paper is to emphasize on the multifaceted tactic and rationalize important functionalities of IDS available at the disposal of HPSC.

Computer Networks fights with a continues issues with attackers and intruders. Attacks on distributed systems becoming more powerful and more frequent day by day. Intrusion detection methods are performing main role to detect intruders and attackers. To identify intrusion on computer or computer networks an intrusion detection system methods are used. Network Intrusion Detection System (NIDS) performs an prime role by presenting the network security. It gives a defense layer by monitoring the traffic on network for predefined distrustful activity or pattern. In this paper we have analyze and compare existing signature based and anomaly based algorithm with Openstack private cloud.

The present-day world has become all dependent on cyberspace for every aspect of daily living. The use of cyberspace is rising with each passing day. The world is spending more time on the Internet than ever before. As a result, the risks of cyber threats and cybercrimes are increasing. The term `cyber threat' is referred to as the illegal activity performed using the Internet. Cybercriminals are changing their techniques with time to pass through the wall of protection. Conventional techniques are not capable of detecting zero-day attacks and sophisticated attacks. Thus far, heaps of machine learning techniques have been developed to detect the cybercrimes and battle against cyber threats. The objective of this research work is to present the evaluation of some of the widely used machine learning techniques used to detect some of the most threatening cyber threats to the cyberspace. Three primary machine learning techniques are mainly investigated, including deep belief network, decision tree and support vector machine. We have presented a brief exploration to gauge the performance of these machine learning techniques in the spam detection, intrusion detection and malware detection based on frequently used and benchmark datasets.

Internet of Things (IoT) development and the addition of billions of computationally limited devices prohibit the use of classical security measures such as Intrusion Detection Systems (IDS). In this paper, we study the influence of the implementation of different feed-forward type of Neural Networks (NNs) on the detection Rate of the Negative Selection Neural Network (NSNN) algorithm. Feed-forward and cascade forward NN structures with different number of neurons and different number of hidden layers are tested. For training and testing the NSNN algorithm the labeled KDD NSL dataset is applied. The detection rates provided by the algorithm with several NN structures to determine the optimal solution are calculated and compared. The results show how these different feed-forward based NN architectures impact the performance of the NSNN algorithm.

Internet of Things (IoT) combines the internet and physical objects to transfer information among the objects. In the emerging IoT networks, providing security is the major issue. IoT device is exposed to various security issues due to its low computational efficiency. In recent years, the Intrusion Detection System valuable tool deployed to secure the information in the network. This article exposes the Intrusion Detection System (IDS) based on deep learning and machine learning to overcome the security attacks in IoT networks. Long Short-Term Memory (LSTM) and K-Nearest Neighbor (KNN) are used in the attack detection model and performances of those algorithms are compared with each other based on detection time, kappa statistic, geometric mean, and sensitivity. The effectiveness of the developed IDS is evaluated by using Bot-IoT datasets.

In today's world computer communication is used almost everywhere and majority of them are connected to the world's largest network, the Internet. There is danger in using internet due to numerous cyber-attacks which are designed to attack Confidentiality, Integrity and Availability of systems connected to the internet. One of the most prominent threats to computer networking is Distributed Denial of Service (DDoS) Attack. They are designed to attack availability of the systems. Many users and ISPs are targeted and affected regularly by these attacks. Even though new protection technologies are continuously proposed, this immense threat continues to grow rapidly. Most of the DDoS attacks are undetectable because they act as legitimate traffic. This situation can be partially overcome by using Intrusion Detection Systems (IDSs). There are advanced attacks where there is no proper documented way to detect. In this paper authors present a Machine Learning (ML) based DDoS detection mechanism with improved accuracy and low false positive rates. The proposed approach gives inductions based on signatures previously extracted from samples of network traffic. Authors perform the experiments using four distinct benchmark datasets, four machine learning algorithms to address four of the most harmful DDoS attack vectors. Authors achieved maximum accuracy and compared the results with other applicable machine learning algorithms.

The Controller Area Network (CAN) bus system works inside connected cars as a central system for communication between electronic control units (ECUs). Despite its central importance, the CAN does not support an authentication mechanism, i.e., CAN messages are broadcast without basic security features. As a result, it is easy for attackers to launch attacks at the CAN bus network system. Attackers can compromise the CAN bus system in several ways: denial of service, fuzzing, spoofing, etc. It is imperative to devise methodologies to protect modern cars against the aforementioned attacks. In this paper, we propose a Long Short-Term Memory (LSTM)-based Intrusion Detection System (IDS) to detect and mitigate the CAN bus network attacks. We first inject attacks at the CAN bus system in a car that we have at our disposal to generate the attack dataset, which we use to test and train our model. Our results demonstrate that our classifier is efficient in detecting the CAN attacks. We achieved a detection accuracy of 99.9949%.

Parallel with the developing world, transportation technologies have started to expand and change significantly year by year. This change brings with it some inevitable problems. Increasing human population and growing transportation-needs result many accidents in urban and rural areas, and this recursively results extra traffic problems and fuel consumption. It is obvious that the issues brought by this spiral loop needed to be solved with the use of some new technological achievements. In this context, self-driving cars or automated vehicles concepts are seen as a good solution. However, this also brings some additional problems with it. Currently many cars are provided with some digital security systems, which are examined in two phases, internal and external. These systems are constructed in the car by using some type of embedded system (such as the Controller Area Network (CAN)) which are needed to be protected form outsider cyberattacks. These attack can be detected by several ways such as rule based system, anomaly based systems, list based systems, etc. The current literature showed that researchers focused on the use of some artificial intelligence techniques for the detection of this type of attack. In this study, an intrusion detection system based on machine learning is proposed for the CAN security, which is the in-vehicle communication structure. As a result of the study, it has been observed that the decision tree-based ensemble learning models results the best performance in the tested models. Additionally, all models have a very good accuracy levels.

In this digital era, technology is upgrading day by day and becoming more agile and intelligent. Smart devices and gadgets are now being used to find solutions to complex problems in various domains such as health care, industries, entertainment, education, etc. The Transport system, which is the biggest challenge for any governing authority of a state, is also not untouched with this development. There are numerous challenges and issues with the existing transport system, which can be addressed by developing intelligent and autonomous vehicles. The existing vehicles can be upgraded to use sensors and the latest communication techniques. The advancements in the Internet of Things (IoT) have the potential to completely transform the existing transport system to a more advanced and intelligent transport system that is the Internet of Vehicles (IoV). Due to the connectivity with the Internet, the Internet of Vehicles (IoV) is exposed to various security threats. Security is the primary issue, which requires to be addressed for success and adoption of the IoV. In this paper, the applicability of machine learning based solutions to address the security issue of IoV is analyzed. The performance of six machine-learning algorithms to detect Bot threats is validated by the k-fold cross-validation method in python.

Nowadays, the networks attacks became very sophisticated and hard to be recognized, The traditional types of intrusion detection systems became inefficient in predicting new types of attacks. As the IDS is an important factor in securing the network in the real time, many new effective IDS approaches have been proposed. In this paper, we intend to discuss different Artificial Neural Networks based IDS approaches, also we are going to categorize them in four categories (normal ANN, DNN, CNN, RNN) and make a comparison between them depending on different performance parameters (accuracy, FNR, FPR, training time, epochs and the learning rate) and other factors like the network structure, the classification type, the used dataset. At the end of the survey, we will mention the merits and demerits of each approach and suggest some enhancements to avoid the noticed drawbacks.

Despite the increased accuracy of intrusion detection systems (IDS) in identifying cyberattacks in computer networks and devices connected to the internet, distributed or coordinated attacks can still go undetected or not detected on time. The single vantage point limits the ability of these IDSs to detect such attacks. Due to this reason, there is a need for attack characteristics' exchange among different IDS nodes. Researchers proposed a cooperative intrusion detection system to share these attack characteristics effectively. This approach was useful; however, the security of the shared data cannot be guaranteed. More specifically, maintaining the integrity and consistency of shared data becomes a significant concern. In this paper, we propose a blockchain-based solution that ensures the integrity and consistency of attack characteristics shared in a cooperative intrusion detection system. The proposed architecture achieves this by detecting and preventing fake features injection and compromised IDS nodes. It also facilitates scalable attack features exchange among IDS nodes, ensures heterogeneous IDS nodes participation, and it is robust to public IDS nodes joining and leaving the network. We evaluate the security analysis and latency. The result shows that the proposed approach detects and prevents compromised IDS nodes, malicious features injection, manipulation, or deletion, and it is also scalable with low latency.

Cyber-attacks are becoming more complex & increasing tasks in accurate intrusion detection (ID). Failure to avoid intrusion can reduce the reliability of security services, for example, integrity, Privacy & availability of data. The rapid proliferation of computer networks (CNs) has reformed the perception of network security. Easily accessible circumstances affect computer networks from many threats by hackers. Threats to a network are many & hypothetically devastating. Researchers have recognized an Intrusion Detection System (IDS) up to identifying attacks into a wide variety of environments. Several approaches to intrusion detection, usually identified as Signature-based Intrusion Detection Systems (SIDS) & Anomaly-based Intrusion Detection Systems (AIDS), were proposed in the literature to address computer safety hazards. This survey paper grants a review of current IDS, complete analysis of prominent new works & generally utilized dataset to evaluation determinations. It also introduces avoidance techniques utilized by attackers to avoid detection. This paper delivers a description of AIDS for attack detection. IDS is an applied research area in artificial intelligence (AI) that uses multiple machine learning algorithms.

Intrusion Detection System (IDS) is a network security software and hardware widely used to detect anomaly network traffics by comparing the traffics against rules specified beforehand. Snort is one of the most famous open-source IDS system. To write a rule, Snort specifies structure and values in Snort manual. This specification is expressive enough to write in different way with the same meaning. If there are rule redundancy, it could distract performance. We, thus, propose a proof of semantical issues for Snort rule and found four pairs of Snort rule combinations that can cause redundancy. In addition, we create a tool to verify such redundancy between two rules on the public rulesets from Snort community and Emerging threat. As a result of our test, we found several redundancy issues in public rulesets if the user enables commented rules.

The rapid development of technology makes it possible for a physical machine to be converted into a virtual machine, which can operate multiple operating systems that are running simultaneously and connected to the internet. DoS/DDoS attacks are cyber-attacks that can threaten the telecommunications sector because these attacks cause services to be disrupted and be difficult to access. There are several software tools for monitoring abnormal activities on the network, such as IDS Snort and IDS Suricata. From previous studies, IDS Suricata is superior to IDS Snort version 2 because IDS Suricata already supports multi-threading, while IDS Snort version 2 still only supports single-threading. This paper aims to conduct tests on IDS Snort version 3.0 which already supports multi-threading and IDS Suricata. This research was carried out on a virtual machine with 1 core, 2 core, and 4 core processor settings for CPU, memory, and capture packet attacks on IDS Snort version 3.0 and IDS Suricata. The attack scenario is divided into 2 parts: DoS attack scenario using 1 physical computer, and DDoS attack scenario using 5 physical computers. Based on overall testing, the results are: In general, IDS Snort version 3.0 is better than IDS Suricata. This is based on the results when using a maximum of 4 core processor, in which IDS Snort version 3.0 CPU usage is stable at 55% - 58%, a maximum memory of 3,000 MB, can detect DoS attacks with 27,034,751 packets, and DDoS attacks with 36,919,395 packets. Meanwhile, different results were obtained by IDS Suricata, in which CPU usage is better compared to IDS Snort version 3.0 with only 10% - 40% usage, and a maximum memory of 1,800 MB. However, the capabilities of detecting DoS attacks are smaller with 3,671,305 packets, and DDoS attacks with a total of 7,619,317 packets on a TCP Flood attack test.

This article is focused on industrial networks and their security. An industrial network typically works with older devices that do not provide security at the level of today's requirements. Even protocols often do not support security at a sufficient level. It is necessary to deal with these security issues due to digitization. It is therefore required to provide other techniques that will help with security. For this reason, it is possible to deploy additional elements that will provide additional security and ensure the monitoring of the network, such as the Intrusion Detection System. These systems recognize identified signatures and anomalies. Methods of detecting security incidents by detecting anomalies in network traffic are described. The proposed methods are focused on detecting DoS attacks in the industrial Modbus protocol and operations performed outside the standard interval in the Distributed Network Protocol 3. The functionality of the performed methods is tested in the IDS system Zeek.

Plenary Talk Our everyday life is more and more dependent on electronic communication and network connectivity. However, the threats of attacks and different types of misuse increase exponentially with the expansion of computer networks. In order to alleviate the problem and to identify malicious activities as early as possible Network Intrusion Detection Systems (NIDSs) have been developed and intensively investigated. Several approaches have been proposed and applied so far for these systems. It is a common challenge in this field that often there are no crisp boundaries between normal and abnormal network traffic, there are noisy or inaccurate data and therefore the investigated traffic could represent both attack and normal communication. Fuzzy logic based solutions could be advantageous owing to their capability to define membership levels in different classes and to do different operations with results ensuring reduced false positive and false negative classification compared to other approaches. In this presentation, after a short introduction of NIDSs a survey will be done on typical fuzzy logic based solutions followed by a detailed description of a fuzzy rule interpolation based IDS. The whole development process, i.e. data preprocessing, feature extraction, rule base generation steps are covered as well.

Technology advancement also increases the risk of a computer's security. As we can have various mechanisms to ensure safety but still there have flaws. The main concerned area is user authentication. For authentication, various biometric applications are used but once authentication is done in the begging there was no guarantee that the computer system is used by the authentic user or not. The intrusion detection system (IDS) is a particular procedure that is used to identify intruders by analyzing user behavior in the system after the user logged in. Host-based IDS monitors user behavior in the computer and identify user suspicious behavior as an intrusion or normal behavior. This paper discusses how an expert system detects intrusions using a set of rules as a pattern recognized engine. We propose a PIDE (Pattern Based Intrusion Detection) model, which is verified previously implemented SBID (Statistical Based Intrusion Detection) model. Experiment results indicate that integration of SBID and PBID approach provides an extensive system to detect intrusion.

In this paper, we consider a novel method of mining biometric data for user authentication by replacing traditional captchas with game-like captchas. The game-like captchas present the user with a short game in which they attempt to get a high score. The data produced from a user's game play will be used to produce a behavior biometric based on user interactions, such as mouse movement, click patterns and game choices. The baseline expectation of interactive behavior will be used as a single factor in an intrusion detection system providing continuous authentication, considering the factors such as IP address, location, time of use, website interactions, and behavior anomalies. In addition to acting as a source of data, game-like captchas are expected to deter bots and automated systems from accessing web-based services and improving the user experience for the end-users who have become accustomed to monotonous alternatives, such as Google's re-captcha.