Biblio

The Controller Area Network (CAN), a broadcasting bus for intra-vehicle communication, does not provide any security mechanisms, although it is implemented in almost every vehicle. Attackers can exploit this issue, transmit malicious messages unnoticeably and cause severe harm. As the utilization of Message Authentication Codes (MACs) is only possible to a limited extent in resource-constrained systems, the focus is put on the development of Intrusion Detection Systems (IDSs). Due to their simple idea of operation, current developments are increasingly utilizing physical signal properties like voltages to realize these systems. Although the feasibility for CAN-based networks could be demonstrated, the least approaches consider the constrained resource-availability of vehicular hardware. To close this gap, we present Voltage-Based Lightweight Intrusion Detection (VALID), which provides physics-based intrusion detection with low resource requirements. By utilizing solely the individual voltage levels on the network during communication, the system detects unauthorized message transmissions without any sophisticated sampling approaches and feature calculations. Having performed evaluations on data from two real vehicles, we show that VALID is not only able to detect intrusions with an accuracy of 99.54 %, but additionally is capable of identifying the attack source reliably. These properties make VALID one of the most lightweight intrusion detection approaches that is ready-to-use, as it can be easily implemented on hardware already installed in vehicles and does not require any further components. Additionally, this allows existing platforms to be retrofitted and vehicular security systems to be improved and extended.

There is very significant role of Virtualization in cloud computing. The physical hardware in the cloud computing reside with the host machine and the virtualization software runs on it. The virtualization allows virtual machines to exist. The host machine shares its physical components such as memory, storage, and processor ultimately to handle the needs of the virtual machines. If an attacker effectively compromises one VM, it could outbreak others on the same host on the network over long periods of time. This is an gradually more popular method for cross-virtual-machine attacks, since traffic between VMs cannot be examined by standard IDS/IPS software programs. As we know that the cloud environment is distributed in nature and hence more susceptible to various types of intrusion attacks which include installing malicious software and generating backdoors. In a cloud environment, where organizations have hosted important and critical data, the security of underlying technologies becomes critical. To alleviate the hazard to cloud environments, Intrusion Detection Systems (IDS) are a cover of defense. In this paper, we are proposing an innovative model for Intrusion Detection System for securing Host machines in cloud infrastructure. This proposed IDS has two important features: (1) signature based and (2) prompt alert system.

Cloud computing has become the main backend of the IT infrastructure as it provides ubiquitous and on-demand computing to serve to a wide range of users including end-users and high-performance demanding agencies. The users can allocate and free resources allocated for their Virtual Machines (VMs) as needed. However, with the rapid growth of interest in cloud computing systems, several issues have arisen especially in the domain of cybersecurity. It is a known fact that not only the malicious users can freely allocate VMs, but also they can infect victims' VMs to run their own tools that include cryptocurrency mining, ransomware, or cyberattacks against others. Even though there exist intrusion detection systems (IDS), running an IDS on every VM can be a costly process and it would require fine configuration that only a small subset of the cloud users are knowledgeable about. Therefore, to overcome this challenge, in this paper we present a VM introspection based allowlisting method to be deployed and managed directly by the cloud providers to check if there are any malicious software running on the VMs with minimum user intervention. Our middleware monitors the processes and if it detects unknown events, it will notify the users and/or can take action as needed.

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.

We investigate a prediction model using RNN for network intrusion detection in industrial IoT environments. For intrusion detection, we use anomaly detection methods that estimate the next packet, measure and score the distance measurement in real packets to distinguish whether it is a normal packet or an abnormal packet. When the packet was learned in the LSTM model, two-gram and sliding window of N-gram showed the best performance in terms of errors and the performance of the LSTM model was the highest compared with other data mining regression techniques. Finally, cosine similarity was used as a scoring function, and anomaly detection was performed by setting a boundary for cosine similarity that consider as normal packet.

It is a well-known fact that the use of Cloud Computing is becoming very common all over the world for data storage and analysis. But the proliferation of the threats in cloud is also their; threats like Information breaches, Data thrashing, Cloud account or Service traffic hijacking, Insecure APIs, Denial of Service, Malicious Insiders, Abuse of Cloud services, Insufficient due Diligence and Shared Technology Vulnerable. This paper tries to come up with the solution for the threat (Denial of Service) in cloud. We attempt to give our newly proposed model by the hybridization of Genetic algorithm and extension of Diffie Hellman algorithm and tries to make cloud transmission secure from upcoming intruders.

Intrusion detection is one of the most prominent and challenging problem faced by cybersecurity organizations. Intrusion Detection System (IDS) plays a vital role in identifying network security threats. It protects the network for vulnerable source code, viruses, worms and unauthorized intruders for many intranet/internet applications. Despite many open source APIs and tools for intrusion detection, there are still many network security problems exist. These problems are handled through the proper pre-processing, normalization, feature selection and ranking on benchmark dataset attributes prior to the enforcement of self-learning-based classification algorithms. In this paper, we have performed a comprehensive comparative analysis of the benchmark datasets NSL-KDD and CIDDS-001. For getting optimal results, we have used the hybrid feature selection and ranking methods before applying self-learning (Machine / Deep Learning) classification algorithmic approaches such as SVM, Naïve Bayes, k-NN, Neural Networks, DNN and DAE. We have analyzed the performance of IDS through some prominent performance indicator metrics such as Accuracy, Precision, Recall and F1-Score. The experimental results show that k-NN, SVM, NN and DNN classifiers perform approx. 100% accuracy regarding performance evaluation metrics on the NSL-KDD dataset whereas k-NN and Naïve Bayes classifiers perform approx. 99% accuracy on the CIDDS-001 dataset.

Existing cyber security solutions have been basically developed using knowledge-based models that often cannot trigger new cyber-attack families. With the boom of Artificial Intelligence (AI), especially Deep Learning (DL) algorithms, those security solutions have been plugged-in with AI models to discover, trace, mitigate or respond to incidents of new security events. The algorithms demand a large number of heterogeneous data sources to train and validate new security systems. This paper presents the description of new datasets, the so-called ToNİoT, which involve federated data sources collected from Telemetry datasets of IoT services, Operating system datasets of Windows and Linux, and datasets of Network traffic. The paper introduces the testbed and description of TONİoT datasets for Windows operating systems. The testbed was implemented in three layers: edge, fog and cloud. The edge layer involves IoT and network devices, the fog layer contains virtual machines and gateways, and the cloud layer involves cloud services, such as data analytics, linked to the other two layers. These layers were dynamically managed using the platforms of software-Defined Network (SDN) and Network-Function Virtualization (NFV) using the VMware NSX and vCloud NFV platform. The Windows datasets were collected from audit traces of memories, processors, networks, processes and hard disks. The datasets would be used to evaluate various AI-based cyber security solutions, including intrusion detection, threat intelligence and hunting, privacy preservation and digital forensics. This is because the datasets have a wide range of recent normal and attack features and observations, as well as authentic ground truth events. The datasets can be publicly accessed from this link [1].

In recent years, smart grid has gradually become the common development trend of the world's power industry, and its security issues are increasingly valued by researchers. Smart grids have applied technologies such as physical control, data encryption, and authentication to improve their security, but there is still a lack of timely and effective detection methods to prevent the grid from being threatened by malicious intrusions. Aiming at this problem, a model based on machine learning to detect smart grid DoS attacks has been proposed. The model first collects network data, secondly selects features and uses PCA for data dimensionality reduction, and finally uses SVM algorithm for abnormality detection. By testing the SVM, Decision Tree and Naive Bayesian Network classification algorithms on the KDD99 dataset, it is found that the SVM model works best.

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%.

The IPv6 over Low-power Wireless Personal Area Network (6LoWPAN) has been standardized to support IP over lossy networks. RPL (Routing Protocol for Low-Power and Lossy Networks) is the common routing protocol for 6LoWPAN. Among various attacks on RPL-based networks, the wormhole attack may cause severe network disruption and is one of the hardest to detect. We have designed and implemented in ContikiOS a wormhole detection technique for 6LoWPAN, that uses round-trip times and hop counts. In addition, the performance of this technique has been evaluated in terms of power, CPU, memory, and communication overhead.

We present ClearTrack, a system that tracks meta-data for each primitive value in Java programs to detect and nullify a range of vulnerabilities such as integer overflow/underflow and SQL/command injection vulnerabilities. Contributions include new techniques for eliminating false positives associated with benign integer overflows and underflows, new metadata-aware techniques for detecting and nullifying SQL/command command injection attacks, and results from an independent evaluation team. These results show that 1) ClearTrack operates successfully on Java programs comprising hundreds of thousands of lines of code (including instrumented jar files and Java system libraries, the majority of the applications comprise over 3 million lines of code), 2) because of computations such as cryptography and hash table calculations, these applications perform millions of benign integer overflows and underflows, and 3) ClearTrack successfully detects and nullifies all tested integer overflow and underflow and SQL/command injection vulnerabilities in the benchmark applications.

The Internet of Things (IoT) is rapidly evolving, while introducing several new challenges regarding security, resilience and operational assurance. In the face of an increasing attack landscape, it is necessary to cater for the provision of efficient mechanisms to collectively detect sophisticated malware resulting in undesirable (run-time) device and network modifications. This is not an easy task considering the dynamic and heterogeneous nature of IoT environments; i.e., different operating systems, varied connected networks and a wide gamut of underlying protocols and devices. Malicious IoT nodes or gateways can potentially lead to the compromise of the whole IoT network infrastructure. On the other hand, the SDN control plane has the capability to be orchestrated towards providing enhanced security services to all layers of the IoT networking stack. In this paper, we propose an SDN-enabled control plane based orchestration that leverages emerging Long Short-Term Memory (LSTM) classification models; a Deep Learning (DL) based architecture to combat malicious IoT nodes. It is a first step towards a new line of security mechanisms that enables the provision of scalable AI-based intrusion detection focusing on the operational assurance of only those specific, critical infrastructure components,thus, allowing for a much more efficient security solution. The proposed mechanism has been evaluated with current state of the art datasets (i.e., N\_BaIoT 2018) using standard performance evaluation metrics. Our preliminary results show an outstanding detection accuracy (i.e., 99.9%) which significantly outperforms state-of-the-art approaches. Based on our findings, we posit open issues and challenges, and discuss possible ways to address them, so that security does not hinder the deployment of intelligent IoT-based computing systems.

Modern critical infrastructures are increasingly turning into distributed, complex Cyber-Physical systems that need proactive protection and fast restoration to mitigate physical or cyber incidents or attacks. Addressing the need for early stage threat detection against physical intrusion, the paper presents two physical security sensors developed within the DEFENDER project for detecting the intrusion of drones and humans using video analytics. The continuous stream of media data obtained from the region of vulnerability and proximity is processed using Region based Fully Connected Neural Network deep-learning model. The novelty of the pro-posed system relies in the processing of multi-threaded media input streams for achieving real-time threat identification. The video analytics solution has been validated using NVIDIA GeForce GTX 1080 for drone detection and NVIDIA GeForce RTX 2070 Max-Q Design for detecting human intruders. The experimental test bed for the validation of the proposed system has been constructed to include environments and situations that are commonly faced by critical infrastructure operators such as the area of protection, tradeoff between angle of coverage against distance of coverage.

With the advent of networking technologies and increasing network attacks, Intrusion Detection systems are apparently needed to stop attacks and malicious activities. Various frameworks and techniques have been developed to solve the problem of intrusion detection, still there is need for new frameworks as per the challenging scenario of enormous scale in data size and nature of attacks. Current IDS systems pose challenges on the throughput to work with high speed networks. In this paper we address the issue of high computational overhead of anomaly based IDS and propose the solution using discretization as a data preprocessing step which can drastically reduce the computation overhead. We propose method to provide near real time detection of attacks using only basic flow level features that can easily be extracted from network packets.

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.

The security of Industrial Control system (ICS) of cybersecurity networks ensures that control equipment fails and that regular procedures are available at its control facilities and internal industrial network. For this reason, it is essential to improve the security of industrial control facility networks continuously. Since network security is threatening, industrial installations are irreparable and perhaps environmentally hazardous. In this study, the industrialized Early Intrusion Detection System (EIDS) was used to modify the Intrusion Detection System (IDS) method. The industrial EIDS was implemented using routers, IDS Snort, Industrial honeypot, and Iptables MikroTik. EIDS successfully simulated and implemented instructions written in IDS, Iptables router, and Honeypots. Accordingly, the attacker's information was displayed on the monitoring page, which had been designed for the ICS. The EIDS provides cybersecurity and industrial network systems against vulnerabilities and alerts industrial network security heads in the shortest possible time.

We introduce a lightweight architecture of Intrusion Detection Systems (IDS) for ad-hoc IoT networks. Current state-of-the-art IDS have been designed based on assumptions holding from conventional computer networks, and therefore, do not properly address the nature of IoT networks. In this work, we first identify the correlation between the communication overheads and the placement of an IDS (as captured by proper placement of active IDS agents in the network). We model such networks as Random Geometric Graphs. We then introduce a novel IDS architectural approach by having only a minimum subset of the nodes acting as IDS agents. These nodes are able to monitor the network and detect attacks at the networking layer in a collaborative manner by monitoring 1-hop network information provided by routing protocols such as RPL. Conducted experiments show that our proposed IDS architecture is resilient and robust against frequent topology changes due to node failures. Our detailed experimental evaluation demonstrates significant performance gains in terms of communication overhead and energy dissipation while maintaining high detection rates.

The Software Defined Network (SDN) provides higher programmable functionality for network configuration and management dynamically. Moreover, SDN introduces a centralized management approach by dividing the network into control and data planes. In this paper, we introduce a deep learning enabled intrusion detection and prevention system (DL-IDPS) to prevent secure shell (SSH) brute-force attacks and distributed denial-of-service (DDoS) attacks in SDN. The packet length in SDN switch has been collected as a sequence for deep learning models to identify anomalous and malicious packets. Four deep learning models, including Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Stacked Auto-encoder (SAE), are implemented and compared for the proposed DL-IDPS. The experimental results show that the proposed MLP based DL-IDPS has the highest accuracy which can achieve nearly 99% and 100% accuracy to prevent SSH Brute-force and DDoS attacks, respectively.

Network Intrusion Detection System (NIDS) can help administrators of a server in detecting attacks by analyzing packet data traffic on the network in real-time. If an attack occurs, an alert to the administrator is provided by NIDS so that the attack can be known and responded immediately. On the other hand, the alerts cannot be monitored by administrators all the time. Therefore, a system that automatically sends notifications to administrators in real-time by utilizing social media platforms is needed. This paper provides an analysis of the notification system built using Snort as NIDS with WhatsApp and Telegram as a notification platform. There are three types of attacks that are simulated and must be detected by Snort, which are Ping of Death attacks, SYN flood attacks, and SSH brute force attacks. The results obtained indicate that the system successfully provided notification in the form of attack time, IP source of the attack, source of attack port and type of attack in real-time.

Cloud computing provides so many groundbreaking advantages over native computing servers like to improve capacity and decrease costs, but meanwhile, it carries many security issues also. In this paper, we find the feasible security attacks made about cloud computing, including Wrapping, Browser Malware-Injection and Flooding attacks, and also problems caused by accountability checking. We have also analyzed the honey pot attack and its procedural intrusion way into the system. This paper on overall deals with the most common security breaches in cloud computing and finally honey pot, in particular, to analyze its intrusion way. Our major scope is to do overall security, analyze in the cloud and then to take up with a particular attack to deal with granular level. Honey pot is the one such attack that is taken into account and its intrusion policies are analyzed. The specific honey pot algorithm is in the queue as the extension of this project in the future.

Robot Operating System (ROS) is becoming more and more important and is used widely by developers and researchers in various domains. One of the most important fields where it is being used is the self-driving cars industry. However, this framework is far from being totally secure, and the existing security breaches do not have robust solutions. In this paper we focus on the camera vulnerabilities, as it is often the most important source for the environment discovery and the decision-making process. We propose an unsupervised anomaly detection tool for detecting suspicious frames incoming from camera flows. Our solution is based on spatio-temporal autoencoders used to truthfully reconstruct the camera frames and detect abnormal ones by measuring the difference with the input. We test our approach on a real-word dataset, i.e. flows coming from embedded cameras of self-driving cars. Our solution outperforms the existing works on different scenarios.

Network traffic anomaly detection is of critical importance in cybersecurity due to the massive and rapid growth of sophisticated computer network attacks. Indeed, the more new Internet-related technologies are created, the more elaborate the attacks become. Among all the contemporary high-level attacks, dictionary-based brute-force attacks (BFA) present one of the most unsurmountable challenges. We need to develop effective methods to detect and mitigate such brute-force attacks in realtime. In this paper, we investigate SSH and FTP brute-force attack detection by using the Long Short-Term Memory (LSTM) deep learning approach. Additionally, we made use of machine learning (ML) classifiers: J48, naive Bayes (NB), decision table (DT), random forest (RF) and k-nearest-neighbor (k-NN), for additional detection purposes. We used the well-known labelled dataset CICIDS2017. We evaluated the effectiveness of the LSTM and ML algorithms, and compared their performance. Our results show that the LSTM model outperforms the ML algorithms, with an accuracy of 99.88%.

The objective of this paper is to propose a model of a distributed intrusion detection system based on the multi-agent paradigm and the distributed file system (HDFS). Multi-agent systems (MAS) are very suitable to intrusion detection systems as they can address the issue of geographic data security in terms of autonomy, distribution and performance. The proposed system is based on a set of autonomous agents that cooperate and collaborate with each other to effectively detect intrusions and suspicious activities that may impact geographic information systems. Our system allows the detection of known and unknown computer attacks without any human intervention (Security Experts) unlike traditional intrusion detection systems that rely on knowledge bases as a mechanism to detect known attacks. The proposed model allows a real time detection of known and unknown attacks within large networks hosting geographic data.

Intrusion Detection Systems (IDS) are the systems that detect and block any potential threats (e.g. DDoS attacks) in the network. In this project, we explore the performance of several machine learning techniques when used as parts of an IDS. We experiment with the CICIDS2017 dataset, one of the biggest and most complete IDS datasets in terms of having a realistic background traffic and incorporating a variety of cyber attacks. The techniques we present are applicable to any IDS dataset and can be used as a basis for deploying a real time IDS in complex environments.