Types Of Cyber-attacks Research Articles

A Review on Major Cyber Threats and Recommended Counter Measures

Abstract: It was predicted that the number of cyber attacks will rise by a factor of two every year. There have been several highprofile cyber attacks on government organizations in India in recent years. Ransomware attacks, Advanced Persistent Threats, Internet of Things, Cloud security risks, Insider threats, Social engineering attacks, Supply chain attacks, Zero-day exploits in cyber threats are some of the significant types of cyber attacks affecting Indian government entities. Security measures, such as regularly backing up important data, installing antivirus software, training employees on how to identify and avoid phishing emails, and maintaining up-to-date software and operating systems are common countermeasures against significant cyber attacks. Preventing cyber attacks requires a multi-layered approach that addresses vulnerabilities in people, processes, and technology. Empowering, the agencies like CERT-In for cyber resilience, strengthening the penalty framework for noncompliance, advisories on information and data security practices are some of the strategies adopted by the government to secure cyberspace. It is necessary to stay informed about the latest threats and continually reassess and update security measures as needed.

Real-time botnet detection on large network bandwidths using machine learning

Botnets are one of the most harmful cyberthreats, that can perform many types of cyberattacks and cause billionaire losses to the global economy. Nowadays, vast amounts of network traffic are generated every second, hence manual analysis is impossible. To be effective, automatic botnet detection should be done as fast as possible, but carrying this out is difficult in large bandwidths. To handle this problem, we propose an approach that is capable of carrying out an ultra-fast network analysis (i.e. on windows of one second), without a significant loss in the F1-score. We compared our model with other three literature proposals, and achieved the best performance: an F1 score of 0.926 with a processing time of 0.007 ms per sample. We also assessed the robustness of our model on saturated networks and on large bandwidths. In particular, our model is capable of working on networks with a saturation of 10% of packet loss, and we estimated the number of CPU cores needed to analyze traffic on three bandwidth sizes. Our results suggest that using commercial-grade cores of 2.4 GHz, our approach would only need four cores for bandwidths of 100 Mbps and 1 Gbps, and 19 cores on 10 Gbps networks.

A Comprehensive Review of Cyber Security Vulnerabilities, Threats, Attacks, and Solutions

Internet usage has grown exponentially, with individuals and companies performing multiple daily transactions in cyberspace rather than in the real world. The coronavirus (COVID-19) pandemic has accelerated this process. As a result of the widespread usage of the digital environment, traditional crimes have also shifted to the digital space. Emerging technologies such as cloud computing, the Internet of Things (IoT), social media, wireless communication, and cryptocurrencies are raising security concerns in cyberspace. Recently, cyber criminals have started to use cyber attacks as a service to automate attacks and leverage their impact. Attackers exploit vulnerabilities that exist in hardware, software, and communication layers. Various types of cyber attacks include distributed denial of service (DDoS), phishing, man-in-the-middle, password, remote, privilege escalation, and malware. Due to new-generation attacks and evasion techniques, traditional protection systems such as firewalls, intrusion detection systems, antivirus software, access control lists, etc., are no longer effective in detecting these sophisticated attacks. Therefore, there is an urgent need to find innovative and more feasible solutions to prevent cyber attacks. The paper first extensively explains the main reasons for cyber attacks. Then, it reviews the most recent attacks, attack patterns, and detection techniques. Thirdly, the article discusses contemporary technical and nontechnical solutions for recognizing attacks in advance. Using trending technologies such as machine learning, deep learning, cloud platforms, big data, and blockchain can be a promising solution for current and future cyber attacks. These technological solutions may assist in detecting malware, intrusion detection, spam identification, DNS attack classification, fraud detection, recognizing hidden channels, and distinguishing advanced persistent threats. However, some promising solutions, especially machine learning and deep learning, are not resistant to evasion techniques, which must be considered when proposing solutions against intelligent cyber attacks.

A Novel Hybrid Edge Detection and LBP Code-Based Robust Image Steganography Method

In digital image processing and steganography, images are often described using edges and local binary pattern (LBP) codes. By combining these two properties, a novel hybrid image steganography method of secret embedding is proposed in this paper. This method only employs edge pixels that influence how well the novel approach embeds data. To increase the quantity of computed edge pixels, several edge detectors are applied and hybridized using a logical OR operation. A morphological dilation procedure in the hybridized edge image is employed to this purpose. The least significant bits (LSB) and all LBP codes are calculated for edge pixels. Afterward, these LBP codes, LSBs, and secret bits using an exclusive-OR operation are merged. These resulting implanted bits are delivered to edge pixels’ LSBs. The experimental results show that the suggested approach outperforms current strategies in terms of measuring perceptual transparency, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSI). The embedding capacity per tempered pixel in the proposed approach is also substantial. Its embedding guidelines protect the privacy of implanted data. The entropy, correlation coefficient, cosine similarity, and pixel difference histogram data show that our proposed method is more resistant to various types of cyber-attacks.

A new framework for APT attack detection based on network traffic

Advanced Persistent Threat (APT) attack detection and monitoring has attracted a lot of attention recently when this type of cyber-attacks is growing in both number and dangerous levels. In this paper, a new APT attack model, which is the combination of three different neural network layers including: Multi-layer Perceptron (MLP), Inference (I), and Graph Convolutional Networks (GCN) is proposed. The new model is named MIG for short. In this model, the MLP layer is in charge of aggregating and extracting properties of the IPs based on flow network in Network traffic, while the Inference layer is responsible for building IP information profiles by grouping and concatenating flow networks generated from the same IP. Finally, the GCN layer is used for analyzing and reconstructing IP features based on the behavior extraction process from IP information records. The APT attacks detection method based on network traffic using this MIG model is new, and has yet been proposed and applied anywhere. The novelty and uniqueness of this method is the combination of many different data mining techniques in order to calculate, extract and represent the relationship and the correlation between APT attack behaviors based on Network traffic. In MIG model, many meaningful anomalous properties and behaviors of APT attacks are synthesized and extracted, which help improve the performance of APT attack detection. The experimental results showed that the proposed method is meaningful in both theory and practice since the MIG model not only improves the ability to correctly detect APT attacks in network traffic but also minimizes false alarms.

Early detection of cyber–physical attacks on fast charging stations using machine learning considering vehicle-to-grid operation in microgrids

The fast-charging stations play an important role in increasing the adoption of the electric vehicles. The “vehicle-to-grid” concept allows the electric vehicles to provide ancillary services to support the distribution system operation. The implementation of such a concept dictates the integration of communication protocols, which leads to cyber vulnerability issues. Such cyberattacks to the fast-charging stations may lead to increased stress on the aging assets as well as denial of the ancillary service provision, leading to financial losses and power outages. This work looks into the development of a novel approach that uses machine learning to early detect such attacks. Three types of cyber attacks are considered under the denial-of-service category. The study investigated the effectiveness of the proposed approach when considering different time resolutions of metering data. The proposed approach has been tested on a microgrid equipped with renewable energy resources as well as electric vehicles in vehicle-to-grid-mode. The results have shown that the detection accuracy increases in case of the high-time resolution compared to the low-time resolution metering data. The results have also shown that the proposed approach was successful in early detecting all three types of cyberattacks at an average accuracy of nearly 98%.

Adaptive Neural Event-Triggered Control of Networked Markov Jump Systems Under Hybrid Cyberattacks.

This article is concerned with the neural network (NN)-based event-triggered control problem for discrete-time networked Markov jump systems with hybrid cyberattacks and unmeasured states. The event-triggered mechanism (ETM) is used to reduce the communication load, and a Luenberger observer is introduced to estimate the unmeasured states. Two kinds of cyberattacks, denial-of-service (DoS) attacks and deception attacks, are investigated due to the vulnerability of cyberlayer. For the sake of mitigating the impact of these two types of cyberattacks on system performance, the ETM under DoS jamming attacks is discussed first, and a new estimation of such mechanism is given. Then, the NN technique is applied to approximate the injected false information. Some sufficient conditions are derived to guarantee the boundedness of the closed-loop system, and the observer and controller gains are presented by solving a set of matrix inequalities. The effectiveness of the presented control method is demonstrated by a numerical example.

Network Security Concepts, Dangers, and Defense Best Practical

In today's highly interconnected world, network security has become a critical aspect of protecting organizations from cyber-attacks. The increasing sophistication of attackers and their ability to exploit software and firmware vulnerabilities pose significant dangers to the security of networks. However, many organizations often neglect the essential steps required to secure their networks, leading to an increased risk of security breaches. In this research article, we aim to address this issue by investigating network security concepts, potential dangers, and practical defense strategies. We begin by exploring the different types of cyber-attacks and their sources, highlighting the various ways attackers exploit network vulnerabilities. We also examine the reasons why organizations often overlook network security and the consequences of not prioritizing it. To better understand the complexity of network security, we categorize the different security concerns using the CIA (confidentiality, integrity, and availability) triangle. This approach allows us to identify the various areas of vulnerability and their potential impact on network security. Next, we focus on the most crucial basic concepts and steps involved in various network security operations. We outline the best practices and practical approaches organizations can take to improve their network security, including implementing security policies and procedures, using encryption and authentication methods, and conducting regular security assessments. By highlighting the importance of network security and providing practical guidance on how organizations can defend against cyber-attacks, we hope to raise awareness and help prevent security breaches. Keywords: Network, Internet, Security, Security Threats, IP Address, Network Attack, Attackers DOI: 10.7176/CEIS/14-2-03 Publication date: March 31 st 2023

Towards Intelligent Attack Detection Using DNA Computing

In recent years, frequent network attacks have seriously threatened the interests and security of humankind. To address this threat, many detection methods have been studied, some of which have achieved good results. However, with the development of network interconnection technology, massive amounts of network data have been produced, and considerable redundant information has been generated. At the same time, the frequently changing types of cyberattacks result in great difficulty collecting samples, resulting in a serious imbalance in the sample size of each attack type in the dataset. These two problems seriously reduce the robustness of existing detection methods, and existing research methods do not provide a good solution. To address these two problems, we define an unbalanced index and an optimal feature index to directly reflect the performance of a detection method in terms of overall accuracy, feature subset optimization, and detection balance. Inspired by DNA computing, we propose intelligent attack detection based on DNA computing (ADDC). First, we design a set of regular encoding and decoding features based on DNA sequences and obtain a better subset of features through biochemical reactions. Second, nondominated ranking based on reference points is used to select individuals to form a new population to optimize the detection balance. Finally, a large number of experiments are carried out on four datasets to reflect real-world cyberattack situations. Experimental results show that compared with the most recent detection methods, our method can improve the overall accuracy of multiclass classification by up to 10%; the imbalance index decreased by 0.5, and 1.5 more attack types were detected on average; and the optimal index of the feature subset increased by 83.8%.

Anomaly based network intrusion detection for IoT attacks using deep learning technique

Internet of Things (IoT) applications are growing in popularity for being widely used in many real-world services. In an IoT ecosystem, many devices are connected with each other via internet, making IoT networks more vulnerable to various types of cyber attacks, thus a major concern in its deployment is network security and user privacy. To protect IoT networks against various attacks, an efficient and practical Intrusion Detection System (IDS) could be an effective solution. In this paper, a novel anomaly-based IDS system for IoT networks is proposed using Deep Learning technique. Particularly, a filter-based feature selection Deep Neural Network (DNN) model where highly correlated features are dropped has been presented. Further, the model is tuned with various parameters and hyper parameters. The UNSW-NB15 dataset comprising of four attack classes is utilized for this purpose. The proposed model achieved an accuracy of 84%. Generative Adversarial Networks (GANs) were used to generate synthetic data of minority attacks to resolve class imbalance issues in the dataset and achieved 91% accuracy with balanced class dataset.

Deep-Learning Based Detection for Cyber-Attacks in IoT Networks: A Distributed Attack Detection Framework

The widespread use of smart devices and the numerous security weaknesses of networks has dramatically increased the number of cyber-attacks in the internet of things (IoT). Detecting and classifying malicious traffic is key to ensure the security of those systems. This paper implements a distributed framework based on deep learning (DL) to prevent many different sources of vulnerability at once, all under the same protection system. Two different DL models are evaluated: feed forward neural network and long short-term memory. The models are evaluated with two different datasets (i.e.NSL-KDD and BoT-IoT) in terms of performance and identification of different kinds of attacks. The results demonstrate that the proposed distributed framework is effective in the detection of several types of cyber-attacks, achieving an accuracy up to 99.95% across the different setups.

A Comparative Study on Deep Learning-Based Algorithms For Intruder Detection Systems and Cyber Security

For data protection, the most vital factors are the statistics' safety, use of cryptographic controls during data transmission, an effective access management system, and powerful tracking. This paper seeks to provide a committed evaluation of the very current studies works on using Deep studying strategies to remedy computer security demanding situations. In this study, we analyzed and reviewed using deep learning algorithms for the Intruder detection system and Cybersecurity programs. Deep learning consists of system-mastering strategies that permit the network to learn from unsupervised data and solve complicated problems. Deep learning approaches such as Convolutional Neural Network (CNN), Auto Encoder (AE), Deep Belief Network (DBN), Recurrent Neural Network (RNN), Generative Adversal Network (GAN), and Deep Reinforcement Learning (DIL) are used to categorize the papers referred. This paper discusses various challenges, issues, and types of cyber-attacks and security measures

Developing and Evaluating an Artificial Intelligence Model for Malicious URL Detection

Today, the increased use of the internet has become important in our lives and new communication technologies, social networks, e-commerce, online banking, and among other applications have a significant impact on the promotion and growth of business. In our study, we aimed to work with a large dataset and to achieve the best results in detecting malicious URL addresses using an artificial intelligence model. A 7-layer RNN model was used in the study, and two similar national and international datasets were combined and merged to create a big new dataset consisting of 579,112 URL addresses. Then, this new data set is divided into training and test sets. first data set was trained at the model and then the second data set was processed test. When this data set was processed in our model, we achieved a success rate of over 91%. This rate is a very good result of detecting malicious url addresses. Your contribution with this work is significant in developing more effective methods for detecting harmful sites as internet usage increases, parallel use of artificial intelligence models makes detection of such sites more effective and potentially assist users in protecting from various types of cyber-attacks is targeted.

2DF-IDS: Decentralized and differentially private federated learning-based intrusion detection system for industrial IoT

Advanced technologies, such as the Internet of Things (IoT) and Artificial Intelligence (AI), underpin many of the innovations in Industry 4.0. However, the interconnectivity and open nature of such systems in smart industrial facilities can also be targeted and abused by malicious actors, which reinforces the importance of cyber security. In this paper, we present a secure, decentralized, and Differentially Private (DP) Federated Learning (FL)-based IDS (2DF-IDS), for securing smart industrial facilities. The proposed 2DF-IDS comprises three building blocks, namely: a key exchange protocol (for securing the communicated weights among all peers in the system), a differentially private gradient exchange scheme (achieve improved privacy of the FL approach), and a decentralized FL approach (that mitigates the single point of failure/attack risk associated with the aggregation server in the conventional FL approach). We evaluate our proposed system through detailed experiments using a real-world IoT/IIoT dataset, and the results show that the proposed 2DF-IDS system can identify different types of cyber attacks in an Industrial IoT system with high performance. For instance, the proposed system achieves comparable performance (94.37%) with the centralized learning approach (94.37%) and outperforms the FL-based approach (93.91%) in terms of accuracy. The proposed system is also shown to improve the overall performance by 12%, 13%, and 9% in terms of F1-score, recall, and precision, respectively, under strict privacy settings when compared to other competing FL-based IDS solutions.

Predictive Taxonomy Analytics (LASSO): Predicting Outcome Types of Cyber Breach

Abstract Cyber breaches are costly for the global economy and extensive efforts have gone into improving the cybersecurity infrastructure. There are numerous types of cyber breaches that vary greatly in terms of cause and impact, resulting in an extensive literature for individual cyber breach type. Our paper seeks to provide a general framework that can be easily applied to analyze different types of cyber breaches. Our framework is inspired by the taxonomy approach in the cybersecurity literature, where it was proposed that an effective set of taxonomy can provide a direction on supporting improved decision-making in cyber risk management and selecting relevant cybersecurity controls. Our paper extends upon the current approach by using this taxonomy to model and predict the associated breach outcomes, given the occurrence of a cyber breach. Specifically, our paper applies least absolute shrinkage and selection operator (LASSO) within a taxonomy framework. Using a proprietary database of known cyber breaches, we show that this analytical tool performs well in out-of-sample predictions and a stable model that generates consistent predictions. For each cyber breach outcome type, we also provide the list of keywords that are useful in predicting the outcome type. We envision researchers, insurers, underwriters, and cybersecurity professionals can use (or expand on) our list of keywords, or use our method to yield their own set of keywords. Practitioners who seek to mitigate their cyber risk may use these keywords as a guide towards the specific attack surfaces that might be most susceptible to the corresponding breach. Our paper lays the groundwork for researchers to better apply the taxonomy approach within cybersecurity research. We also perform regression analysis to identify industries that are most susceptible to various cyber breach events. Our results corroborate with the literature, where some industries are indeed more likely to be impacted by certain types of cyberattacks.

BLOKÇEYN ŞӘBӘKӘSİNİN KOMPONENTLӘRİNİN İŞLӘNMӘSİ İLӘ İNFORMASİYA MÜBADİLӘSİNİN TӘHLÜKӘSİZLİYİ

Blockchain technology is an emerging technology that has the potential to change the way information is stored and transmitted. Its main components are the concepts of decentralization, transparency and distribution, which, in short, are the absence of any central control system between the source and the destination, the interdependence of the blocks used by the participating parties as a chain, and continuously, the registration and storage, and finally, authorization processes after authentication of the information it needs by means of a key belonging to the original owner, as in asymmetric encryption, with the possibility of any loss and at the time of need, stored at millions of points. The purpose of this summary is to discuss the application of information security for data transmission against cyber attacks in blockchain technology. Different methods of implementing security measures such as encryption, digital signatures and multi-signature transactions, different types of cyber-attacks that blockchain technology is vulnerable to and how to prevent these attacks has also been discussed. Also, the role of smart contracts in ensuring secure data transfer and the importance of developing secure protocols has been emphasized. In terms of security, it has also considered the future of blockchain technology and how it can be used to further increase the security of data transmission.

Compensation-Based Distributed Model-Free Adaptive Control for Cyber-Attacks

In this work, three different types of cyber-attacks are considered together to develop a unified data-driven control method for a nonlinear networked multi-agent system bypassing any modeling processes. To this end, a distributed output is defined for every agent to show its relationship with the adjacent agents. Then, the nonlinear dynamics of the distributed output is transformed into a linear data model by using a dynamic linearization method, which is further used to predict the distributed output of the agent if the unconfined cyber-attack occurs. By introducing a stochastic variable, the compensated distributed output is reformulated to build a relationship between the actually measured one and the virtually predicted one. In the sequence, a compensation-based distribute model-free adaptive control (cDMFAC) is proposed to resist the unconfined cyberattacks. The convergence is proved rigorously in the sense of mathematical expectation. The simulation study further confirms the effectiveness of the proposed cDMFAC method.

Sajber bezbednost tokom pandemije kovid-19

The covid-19 pandemic has had a profound impact on the world, including exacerbating cybersecurity threats. This paper explores the various types of cyberattacks that emerged during the pandemic, the organizations that were targeted, and the methods employed by attackers. A particular focus is placed on attacks directed at healthcare organizations, which were particularly vulnerable due to the increased demand for their services. The paper also discusses defensive strategies and preventive measures that can be taken to mitigate cyberattacks, as well as the importance of organizational resilience in the face of crises. Finally, the paper examines the World Bank's response to the pandemic in terms of its ability to adapt to the new business environment and maintain resilience.

An Online Approach to Covert Attack Detection and Identification in Power Systems

The development of information technology and digital control systems has posed an inevitable trend of interconnectivity and wirelessness of data communications in power systems. As a result, the cybersecurity of power systems has been raising concerns over the past decades. Yet the complexity of power systems and their conventional designs with local and wide area network communications has posed increasing vulnerability to cyberattacks. Among the types of cyberattacks, data integrity attacks can be more harmful to power systems, especially power transmission systems, through malicious manipulation of measurement and control data to cause damage to the physical system. As the sophistication of cyberattacks increases, certain cyberattacks can successfully bypass traditional intrusion detection systems, which calls for better design of detection schemes. On the other hand, in a large-scale network, attack identification for a power system is as important as attack detection. In this paper, we develop an online approach based on the Sparse Group Lasso to detect and identify the location of a typical type of data integrity attack named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">covert attacks</i> on power transmission systems. We provide the theoretical foundation of the proposed method with a simplified linear system model and extend the method to nonlinear systems. We validate the performance of our method on both linear and nonlinear system settings with a comprehensive numerical study.

WAMS-Based HVDC Damping Control for Cyber Attack Defense

Owing to the fast and large power regulating the capacity of the HVDC system, the wide-area measurement system (WAMS) based high voltage direct current (HVDC) system has been regarded as a prospective solution to deal with the low-frequency oscillation issue. However, due to the vulnerability of the WAMS communication, WAMS based HVDC system control can be a prime target of malicious penetrations that could lead to disastrous events. To remediate this adverse effect, an improved WAMS based HVDC damping control framework is proposed. First, a lightweight network named Attack Shuffle convolutional neural Networks (ASNet) is proposed to learn the characteristics of cyber attacks. Then, a model-free-based cyber attack defense framework is introduced to quickly identify the attack types based on the continuous wavelet transform and ASNet. Additionally, an improved control framework of the WAMS and HVDC-based wide-area power oscillation damping control (WH-PODC) is developed to provide different response control for mitigation of the impact of cyber attacks. Finally, the performance of the proposed WH-PODC control framework is evaluated with real PMU data in multiple scenarios in RTDS, where the results indicate that the response intensity can be kept under multiple types of cyber attacks while providing similar effectiveness in oscillation suppression to conventional controls.