Types Of Attacks Research Articles

A Survey of Security Challenges in Cloud-Based SCADA Systems

Supervisory control and data acquisition (SCADA) systems enable industrial organizations to control and monitor real-time data and industrial processes. Migrating SCADA systems to cloud environments can enhance the performance of traditional systems by improving storage capacity, reliability, and availability while reducing technical and industrial costs. However, the increasing frequency of cloud cyberattacks poses a significant challenge to such systems. In addition, current research on cloud-based SCADA systems often focuses on a limited range of attack types, with findings scattered across various studies. This research comprehensively surveys the most common cybersecurity vulnerabilities and attacks facing cloud-based SCADA systems. It identifies four primary vulnerability factors: connectivity with cloud services, shared infrastructure, malicious insiders, and the security of SCADA protocols. This study categorizes cyberattacks targeting these systems into five main groups: hardware, software, communication and protocol-specific, control process, and insider attacks. In addition, this study proposes security solutions to mitigate the impact of cyberattacks on these control systems.

Provably Secure ECC-Based Anonymous Authentication and Key Agreement for IoT

With the rise of the Internet of Things (IoT), maintaining data confidentiality and protecting user privacy have become increasingly challenging. End devices in the IoT are often deployed in unattended environments and connected to open networks, making them vulnerable to physical tampering and other security attacks. Different authentication key agreement (AKA) schemes have been used in practice; several of them do not cover the necessary security features or are incompatible with resource-constrained end devices. Their security proofs have been performed under the Random-Oracle model. We present an AKA protocol for end devices and servers. The proposal leverages the ECC-based key exchange mechanism and one-way hash function-based message authentication method to achieve mutual authentication, user anonymity, and forward security. A formal security proof of the proposed scheme is performed under the standard model and the eCK model with the elliptic curve encryption computational assumptions, and formal verification is performed with ProVerif. According to the performance comparison, it is revealed that the proposed scheme offers user anonymity, perfect forward security, and mutual authentication, and resists typical attacks such as ephemeral secret leakage attacks, impersonation attacks, man-in-the-middle attacks, and key compromise impersonation attacks. Moreover, the proposed scheme has the lowest computational and communication overhead compared to existing schemes.

Streamlining detection of input validation attack types through hybrid analysis and machine learning

Streamlining detection of input validation attack types through hybrid analysis and machine learning

Social engineering awareness and resilience in Egypt: a quantitative exploration

PurposeSocial engineering is crucial in today’s digital landscape. As technology advances, malicious individuals exploit human judgment and trust. This study explores how age, education and occupation affect individuals’ awareness, skills and perceptions of social engineering.Design/methodology/approachA quantitative research approach was used to survey a diverse demographic of Egyptian society. The survey was conducted in February 2023, and the participants were sourced from various Egyptian social media pages covering different topics. The collected data was analyzed using descriptive and inferential statistics, including independent samples t-test and ANOVA, to compare awareness and skills across different groups.FindingsThe study revealed that younger individuals and those with higher education tend to research social engineering more frequently. Males display a higher level of awareness but score lower in terms of social and psychological consequences as well as types of attacks when compared to females. The type of attack cannot be predicted based on age. Higher education is linked to greater awareness and ability to defend against attacks. Different occupations have varying levels of awareness, skills, and psychosocial consequences. The study emphasizes the importance of increasing awareness, education and implementing cybersecurity measures.Originality/valueThis study’s originality lies in its focus on diverse Egyptian demographics, innovative recruitment via social media, comprehensive exploration of variables, statistical rigor, practical insights for cybersecurity education and diversity in educational and occupational backgrounds.

A comprehensive survey on DDoS attacks detection & mitigation in SDN-IoT network

The Internet of Things (IoT) has transformed our lives by introducing new services and enhancing productivity. However, the widespread adoption of IoT devices and communication units has posed challenges in network management. In response, there is a growing necessity to rethink and redesign IoT network control. Software-defined networking (SDN) has emerged as a promising solution, leveraging its programmability and centralized management capabilities. SDN can simplify network management, offer network abstraction, facilitate development, and efficiently handle the complexities of IoT networks. Despite these advantages, security concerns, particularly the threat of Distributed Denial of Service (DDoS) attacks, persist in the IoT landscape. This survey focuses on exploring the collaboration between SDN and IoT. It investigates various types of DDoS attacks and highlights different types of defense, detection, and mitigation methods employed to address DDoS threats in SDN-based IoT (SDN-IoT) networks.

Blockchain integration for in-vehicle CAN bus intrusion detection systems with ISO/SAE 21434 compliant reporting.

The development of Intrusion Detection Systems (IDS) for in-vehicle buses has gained a lot of momentum in recent years as the number of reported vulnerabilities and the degree of interconnectivity for modern vehicles are on the rise. Since intrusion detection is resource consuming, it can be performed on computationally capable Android head units that are now present inside vehicles. Moreover, these units are connected to the internet, which enables the use of more complex algorithms that run in cloud environments. In this work we develop one such approach: an IDS that consists of a locally installed copy, running on head units, and a centralized instance of it that runs in the cloud and monitors traffic for groups of similar vehicles. Additionally, the centralized instance is part of a cloud service for intrusion detection which is continuously updated with the most recent types of attacks. The classification results of the cloud-based service are further analyzed by an incident response team which confirms the presence of known attacks, analyzes new types of attacks and assesses their impact. The output of this activity is stored on the Blockchain as ISO/SAE 21434 compliant reports, ensuring the transparency and traceability of the reported incidents.

Intrusion Detection using Deep Learning

The focus of cloud computing nowadays has been reshaping the digital epoch, in which clients now face serious concerns about the security and privacy of their data hosted in the cloud, as well as increasingly sophisticated and frequent cyber attacks. Therefore, it has become imperative for both individuals and organizations to implement a robust intrusion detection system (IDS) capable of monitoring packets in the network, distinguishing between benign and malicious behaviour, and detecting the type of attacks. IDS based on ML are efficient and precise in spotting network threats. Yet, for large dimensional data sizes, the performance of these systems decreases. Thus, it is critical to build a suitable feature selection approach that selects necessary features without having an impact on the classification process or causing information loss. Furthermore, training ML models on unbalanced datasets show a rising false positive rate (FPR) and a lowering detection rate (DR). In this paper, we present an improved cloud IDS designed by incorporating the synthetic minority over-sampling technique (SMOTE) to address the imbalanced data issue, and for feature selection, we propose to use a hybrid approach that includes three techniques: information gain (IG), chi-square (CS), and particle swarm optimization (PSO). Finally, the random forest (RF) model is utilized for detecting and classifying various types of attacks. The suggested system has been verified by the UNSW-NB15 and Kyoto datasets, achieving accuracies of over 98% and 99% in the multi-class classification scenario, respectively. It was noticed that an intrusion detection system with fewer informative features would operate more effectively. The simulation results significantly outperform other methodologies proposed in the related work in terms of different evaluation metrics.

Hen maternal care inspired optimization framework for attack detection in wireless smart grid network

In the power grid, communication networks play an important role in exchanging smart grid-based information. In contrast to wired communication, wireless communication offers many benefits in terms of easy setup connections and low-cost high-speed links. Conversely, wireless communications are commonly more vulnerable to security threats than wired ones. All power equipment devices and appliances in the smart distribution grid (SDG) are communicated through wireless networks only. Most security research focuses on keeping the SDG network from different types of attacks. The denial-of-service (DoS) attack is consuming more energy in the network leads to a permanent breakdown of memory. This work proposes a new metaheuristic optimization inspired by maternal care of hen to their children called hen maternal care (HMCO) inspired optimization. The HMCO algorithm mimics the care shown by hen for their children in nature. The mother hen is always watchful and protects its chicks against predators. All chickens utilize different calls to designate flying predators like falcons and owls from ground seekers like foxes and coyotes, showing that they can both survey a danger and advise different chickens how to set themselves up. Our method shows greater performance among other standard algorithms.

A New Time Series Dataset for Cyber-Threat Correlation, Regression and Neural-Network-Based Forecasting

In the face of escalating cyber threats that have contributed significantly to global economic losses, this study presents a comprehensive dataset capturing the multifaceted nature of cyber-attacks across 225 countries over a 14-month period from October 2022 to December 2023. The dataset, comprising 77,623 rows and 18 fields, provides a detailed chronology of cyber-attacks, categorized into eight critical dimensions: spam, ransomware, local infection, exploit, malicious mail, network attack, on-demand scan, and web threat. The dataset also includes ranking data, offering a comparative view of countries’ susceptibility to different cyber threats. The results reveal significant variations in the frequency and intensity of cyber-attacks across different countries and attack types. The data were meticulously compiled using modern AI-based data acquisition techniques, ensuring a high degree of accuracy and comprehensiveness. Correlation tests against the eight types of cyber-attacks resulted in the determination that on-demand scan and local infection are highly correlated, with a correlation coefficient of 0.93. Lastly, neural-network-based forecasting of these highly correlated factors (i.e., on-demand scan and local infection) reveals a similar pattern of prediction, with an MSE and an MAPE of 1.616 and 80.13, respectively. The study’s conclusions provide critical insights into the global landscape of cyber threats, highlighting the urgent need for robust cybersecurity measures.

Machine learning approach for intrusion detection system using dimensionality reduction

As cyberspace has emerged, security in all the domains like networks, cloud, and databases has become a greater concern in real-time distributed systems. Existing systems for detecting intrusions (IDS) are having challenges coping with constantly changing threats. The proposed model, DR-DBMS (dimensionality reduction in database management systems), creates a unique strategy that combines supervised machine learning algorithms, dimensionality reduction approaches and advanced rule-based classifiers to improve intrusion detection accuracy in terms of different types of attacks. According to simulation results, the DR-DBMS system detected the intrusion attack in 0.07 seconds and with a smaller number of features using the dimensionality reduction and feature selection techniques efficiently.

Secure Data Deduplication With Dynamic Access Control for Mobile Cloud Storage

Data deduplication is of vital importance for mobile cloud computing to cope with the explosive growth of outsourced mobile data. In order to ensure the privacy of sensitive mobile data against an untrusted cloud, Message-Locked Encryption (MLE) has been proposed to enable deduplication over ciphertext. However, MLE prohibits data access control since it uses deterministic content-derived encryption keys. Recently, a lightweight rekeying-aware encrypted deduplication system (REED) has been proposed to achieve dynamic access control for secure data deduplication. However, REED is vulnerable to key-retaining attack and stub-retaining attack, which leads to insecure access revocation, and thus cannot support secure dynamic access control. In response, we present AC-Dedup, an encrypted deduplication storage system that supports <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">secure dynamic access control</i> for mobile cloud storage. At the core of AC-Dedup are two novel encryption techniques named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mixed message locked encryption</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">random stub re-encryption</i> to resist the two types of attacks, respectively. To the best of our knowledge, AC-Dedup is the first practical system that achieves secure data deduplication and secure dynamic access control simultaneously. We conduct security analysis and experimental evaluation on mobile device and cloud platform with real-world IoT datasets. The results show that AC-Dedup enables secure and efficient dynamic access control while preserving deduplication effectiveness.

A comprehensive survey on social engineering-based attacks on social networks

Threats based on social engineering in social networks are becoming a more common problem. Social engineering is a type of attack that relies on trickery and exploiting human psychology to gain access to confidential information or resources. It involves deceptive techniques like phishing, pretexting, and baiting, tricking individuals into revealing sensitive information or performing specific actions. These tactics can lead to unauthorized access to user accounts, identity theft, or the distribution of harmful content. This study offers a detailed review of threats related to social engineering on social networks. It explores various social engineering attacks, the methods used to execute these threats, and measures that can be adopted to minimize the risk of becoming a victim. The research aimed to develop a new, broad classification of social engineering attacks and strategies for responding to them. It also examines the challenges that social engineering poses to algorithms on social media platforms and highlights the need for more research. The study concludes by pointing out the shortcomings of current approaches and suggesting future research directions, stressing the importance of standardized protective measures and increasing awareness among potential victims. This thorough examination improves our understanding of social engineering attacks and encourages the development of innovative solutions and ethical practices, contributing to a more secure digital environment.

OOA-modified Bi-LSTM network: An effective intrusion detection framework for IoT systems

Currently, the Internet of Things (IoT) generates a huge amount of traffic data in communication and information technology. The diversification and integration of IoT applications and terminals make IoT vulnerable to intrusion attacks. Therefore, it is necessary to develop an efficient Intrusion Detection System (IDS) that guarantees the reliability, integrity, and security of IoT systems. The detection of intrusion is considered a challenging task because of inappropriate features existing in the input data and the slow training process. In order to address these issues, an effective meta heuristic based feature selection and deep learning techniques are developed for enhancing the IDS. The Osprey Optimization Algorithm (OOA) based feature selection is proposed for selecting the highly informative features from the input which leads to an effective differentiation among the normal and attack traffic of network. Moreover, the traditional sigmoid and tangent activation functions are replaced with the Exponential Linear Unit (ELU) activation function to propose the modified Bi-directional Long Short Term Memory (Bi-LSTM). The modified Bi-LSTM is used for classifying the types of intrusion attacks. The ELU activation function makes gradients extremely large during back-propagation and leads to faster learning. This research is analysed in three different datasets such as N-BaIoT, Canadian Institute for Cybersecurity Intrusion Detection Dataset 2017 (CICIDS-2017), and ToN-IoT datasets. The empirical investigation states that the proposed framework obtains impressive detection accuracy of 99.98 %, 99.97 % and 99.88 % on the N-BaIoT, CICIDS-2017, and ToN-IoT datasets, respectively. Compared to peer frameworks, this framework obtains high detection accuracy with better interpretability and reduced processing time.

Proposed approach to evaluate the deterrence of limited nuclear attacks

Abstract A counterforce strategy intends to destroy an adversary’s nuclear arsenal. However, hardening, defences and mobility have made nuclear-delivery systems less vulnerable. Nations may consider limited attacks with one or a few weapons that would not significantly deplete their weapon stockpile or alter the overall nuclear balance. We propose an approach to evaluate limited strikes with regard to military, political, economic and societal aspects. We discuss potential responses along with their compliance to the Law of Armed Conflict (LOAC). We contend that a response should be generally proportional, meaning sufficient to achieve deterrence in the hope of avoiding escalation. Responses that greatly exceed the attacker’s inflicted damage will be perceived as unjust and, if executed, may escalate the conflict. Similarly, declared responses that inflict considerably less damage may not be sufficient to deter since the attacker would end the initial exchange in a better relative position between the adversaries. We conclude that deterrence is improved with a range of nuclear capabilities. Countries limiting responses to only military targets based on a strict interpretation of the LOAC may not have a viable response to deter some types of limited nuclear attacks.

A profiled side‐channel attack detection using deep learning model with capsule auto‐encoder network

AbstractSide‐channel analysis (SCA) is a type of cryptanalytic attack that uses unintended ‘side‐channel’ leakage through the real‐world execution of the cryptographic algorithm to crack a secret key of an embedded system. These side‐channel errors can be discovered through tracking the energy usage of the device performing the technique, electromagnetic radiations while the encryption process, execution time, cache hits/misses, and others. Nowadays, deep learning‐based detection techniques are considered as emerging techniques that have been proposed for attack detection. Deep learning architectures have the ability to learn autonomously and concentrate on difficult features, in contrast to machine learning models. In light of these factors, the work's motive is thought to be the proposal of a deep learning‐based attack detection method. Many methods are used to decrease these assaults, however, the majority of them are inefficient and time‐demanding. In order to address these challenges, this study employs a novel deep learning‐based methodology. Pre‐processing, feature extraction, and SCA classification are the three stages of the approach proposed in this work. First, pre‐processing is used to remove unnecessary information and improve the quality of the input using data cleaning and min‐max normalization. The previously processed data are then fed as input into the proposed hybrid deep learning architecture. A Deep Residual Capsule Auto‐Encoder (DR_CAE) model is introduced in the proposed study. The deep residual neural network‐50 (DRNN‐50) is utilized to extract relevant features in this case, while the side channel analysis is done by using capsule auto‐encoder (CAE). The parameters of the proposed model are adjusted using the modified white shark optimization (MWSO) technique to improve its performance. In the results section, the proposed model is compared to various existing models in terms of accuracy, precision, recall, F‐measures, time, and so on. The proposed framework has an accuracy of 98.802%, F‐measures of 98.801%, kappa coefficient of 97.6%, the precision value of 98.81%, and recall value of 98.80%.

Three layer hybrid learning to improve intrusion detection system performance

In imbalanced network traffic, malicious cyberattacks can be hidden in a large amount of normal traffic, making it difficult for intrusion detection systems (IDS) to detect them. Therefore, anomaly-based IDS with machine learning is the solution. However, a single machine learning cannot accurately detect all types of attacks. Therefore, a hybrid model that combines long short-term memory (LSTM) and random forest (RF) in three layers is proposed. Building the hybrid model starts with Nearmiss-2 class balancing, which reduces normal samples without increasing minority samples. Then, feature selection is performed using chi-square and RF. Next, hyperparameter tuning is performed to obtain the optimal model. In the first and second layers, LSTM and RF are used for binary classification to detect normal data and attack data. While the third layer model uses RF for multiclass classification. The hybrid model verified using the CSE-CIC-IDS2018 dataset, showed better performance compared to the single algorithm. For multiclass classification, the hybrid model achieved 99.76% accuracy, 99.76% precision, 99.76% recall, and 99.75% F1-score.

IoT Attack Detection using Machine Learning and Deep Learning in Smart Home

The Internet of Things (IoT) has revolutionized the traditional Internet, pushing past its former boundaries by implementing smart linked gadgets. The IoT is steadily becoming a staple of everyday life, having been implemented into various diverse applications, such as cities, smart homes, and transportation. However, despite the technological advancements that the IoT brings, various new security risks have also been introduced due to the development of new types of attacks. This prevents current intelligent IoT applications from adaptively learning from other intelligent IoT applications, which leaves them in a volatile state. In this paper, we conducted a structured literature review (SLR) on Smart Home's IoT attack detection using machine learning and deep learning. Four journal databases were used to perform this review: IEEE, Science Direct, Association for Computing Machinery (ACM), and SpringerLink. Sixty articles were selected and studied, where we noted the various patterns and techniques present in the framework of the selected research. We also took note of the different machine learning and deep learning methods, the types of attacks, and the Network layers present in Smart Home. By conducting an SLR, we analyzed the numerous techniques of IoT attack detection for smart homes proposed by various theoretical studies. We enhanced the studied literature by proposing a new solution for better IoT attack detection in smart homes.

Application of Robust Software Modelling Tool for Web Attacks Detection

Abstract: The current inquiry as an examination of web attacks where proliferation of web-based applications has brought about a concurrent rise in cyber threats, particularly the form of web attacks targeting vulnerable systems. Approaches to web attack detection often rely on rule-based or signature-based methods, which struggle to change with the increasing landscape of attacks. In response, this study proposes an innovative approach leveraging DL techniques for web attacks. By harnessing the capability of DL, especially CNN and recurrent neural networks (RNNs), our proposed system learns directly from raw web traffic data, eliminating the need for manual feature engineering. This end-to-end approach not only streamlines the detection process but also enhances the system's ability to generalize across different types of attacks and adapt to new threats. To evaluate the effectiveness of our approach, we conducted extensive experiments on diverse datasets containing both benign and malicious web traffic. Our results demonstrate the superiority of end-to-end deep learning over traditional methods, achieving higher detection accuracy and robustness against adversarial attacks. In conclusion, our study highlights the promise of end-to-end deep learning as a viable approach related to web attacks, offering enhanced detection capabilities in the phase of evolving cyber threats.

Network Packet Status-Aware and Ping-Integrated Attack Classification Along with Alert Generation Using Esw-Mlp and S3-Fuzzy

Recently, there has been a rapid increase in attacks along with network data, thereby posing a significant threat to network security. During attack prediction, none of the traditional systems concentrated on packet status identification. Thus, by using Entropy Softsin Wrapper based Multi-Layer Perceptron (ESWMLP) and Standard S Shaped Fuzzy (S3-Fuzzy), this paper proposes a packet status-aware attack prediction and ping-enabled Alert Generation (AG) in a network. Initially, the Canadian Institute for Cybersecurity Android Malware 2017 (CICAndMal2017) dataset is gathered and pre-processed. Then, the features are extracted, and optimal features are selected employing the Tent Chaotic-Chicken Swarm Optimization Algorithm (TC-CSOA). Next, the selected features are subjected to ESW-MLP, where the attack types are classified. Similarly, from the traffic dataset, the features are extracted, followed by feature selection. Thus, by using ESW-MLP, the packet status is identified. Similarly, the similarity between the features is estimated. Then, the AG is done based on S3-Fuzzy. Besides, via TC-CSOA-based load balancing, the network collision is diminished. Next, Ping-based PSI and Attack Classification (AC) are carried out on the switch, followed by AG. As per the experimental findings, the proposed approach had higher supremacy with 98.63% accuracy

Network Attack Detection Using NeuroEvolution of Augmenting Topologies (NEAT) Algorithm

The imperfection of existing intrusion detection methods and the changing nature of malicious actions on the attacker's part led to the Internet of Things (IoT) network interaction in an unsafe state. The actual problem of improving the technology of the IOT is counteracting malicious network impacts. In this regard, research and development aimed at creating effective tools for solving applied problems within the framework of this problem are becoming increasingly important. This study seeks to develop tools for detecting anomalous network conditions resulting from malicious attacks. In particular, the accuracy of the identification of DoS and DDoS attacks is sufficient for operational use. This study analyzes various multi-level architectures, relevant communication protocols, and different types of network attacks. The presented research was conducted on open datasets TON_IOT DATASETS, which include multiple data sources collected from IoT sensors. The modified HyperNEAT algorithm was used as the basis for the development. The NEAT methodology used in the study allows you to combine various network nodes. Results of the study: a neuro-evolutionary algorithm for identifying DoS and DDoS attacks was implemented, integrated, and real-tested based on a multi-level analysis of network traffic combined with various adaptive modules. The accuracy of identifying DoS and DDoS attacks is 0.9242 in the Accuracy metric. The study implies that the proposed approach can be recommended for network intrusion detection, ensuring security when interacting with the IoT.