Types Of Attacks Research Articles

ENHANCING IIOT SECURITY WITH MACHINE LEARNING AND DEEP LEARNING FOR INTRUSION DETECTION

The rapid growth of the Internet of Things (IoT) and digital industrial devices has significantly impacted various aspects of life, underscoring the importance of the Industrial Internet of Things (IIoT). Given its importance in industrial contexts that affect human life, the IIoT represents a key subset of the broader IoT landscape. Due to the proliferation of sensors in smart devices, which are viewed as points of contact, as the gathering of data and information regarding the IIoT systems and devices operating on the IoT, there is an urgent requirement for developing effective security methods to counter such threats as well as protecting IIoT systems. In this study, we develop and evaluate a well-optimized intrusion detection system (IDS) based on deep learning (DL) and machine learning (ML) techniques to enhance IIoT security. Leveraging the Edge-IIoTset dataset, specifically designed for IIoT cybersecurity evaluations, we focus on detecting and mitigating 14 distinct attack types targeting IIoT and IoT protocols. These attacks are categorized into five threat groups: information collection, malware, DDoS, man-in-the-middle attacks, and injection attacks. We conducted experiments using machine learning algorithms (k-nearest neighbors, decision tree) and a neural network on the KNIME platform, achieving a remarkable 100% accuracy with the decision tree model. This high accuracy demonstrates the effectiveness of our approach in protecting industrial IoT networks.

Cyber–physical event reasoning for distributed energy resources

The widespread adoption of internet-connected and remotely controllable solar plants and energy storages renders coordinated cyber–physical attacks against distributed energy resources (DERs) an emerging risk for power systems. Effective incident response can be facilitated by online DER monitoring providing real-time information on event root causes and physical impacts. Such online event identification is challenged by the lack of historical attack observations, and emergence of new attack strategies. The Cyber-Physical Event Reasoning System CyPhERS provides real-time information on both known and unknown attack types in form of informative and interpretable event signatures, without need to be trained on historical attack samples. To date, CyPhERS has only been demonstrated on a laboratory water distribution testbed of limited complexity, considering human evaluation of event signatures. This work methodologically adapts CyPhERS to specificities of DER operation such as weather and consumer-induced volatility, and introduces an automated signature evaluation system. The feasibility of applying CyPhERS for automated DER monitoring is investigated on a dataset recorded from a real photovoltaic-battery system targeted by several cyber and cyber–physical attack types. The results demonstrate that the proposed methodological adaptations and signature evaluation system enable the application of CyPhERS for automated online identification of different attack types targeting DERs, while greatly reducing the false positive rate.

Digital Image Watermarking

Digital watermarking has become an essential method for encrypting multimedia files by adding undetectable data. In this work, we present a novel approach to digital watermarking that makes use of the Singular Value Decomposition (SVD) and Discrete Wavelet Transform (DWT) methods. By combining these methods, robustness against different types of attacks can be achieved while preserving excellent perceptual quality. Our approach ensures resilience and invisibility by embedding the watermark in the singular values that are obtained from the decomposition of DWT subbands. The outcomes of the experiments show that the suggested strategy is more efficient and effective than the current techniques when it comes to maintaining image quality and being resistant to common image processing threats.

Distributed Denial of Service Attack Detection in IoT Networks using Deep Learning and Feature Fusion: A Review

The explosive growth of Internet of Things (IoT) devices has led to escalating threats from distributed denial of service (DDoS) attacks. Moreover, the scale and heterogeneity of IoT environments pose unique security challenges, and intelligent solutions tailored for the IoT are needed to defend critical infrastructure. The deep learning technique shows great promise because automatic feature learning capabilities are well suited for the complex and high-dimensional data of IoT systems. Additionally, feature fusion approaches have gained traction in enhancing the performance of deep learning models by combining complementary feature sets extracted from multiple data sources. This paper aims to provide a comprehensive literature review focused specifically on deep learning techniques and feature fusion for DDoS attack detection in IoT networks. Studies employing diverse deep learning models and feature fusion techniques are analysed, highlighting key trends and developments in this crucial domain. This review provides several significant contributions, including an overview of various types of DDoS attacks, a comparison of existing surveys, and a thorough examination of recent applications of deep learning and feature fusion for detecting DDoS attacks in IoT networks. Importantly, it highlights the current challenges and limitations of these deep learning techniques based on the literature surveyed. This review concludes by suggesting promising areas for further research to enhance deep learning security solutions, which are specifically tailored to safeguarding the fast-growing IoT infrastructure against DDoS attacks.

A Novel Architecture for an Intrusion Detection System Utilizing Cross-Check Filters for In-Vehicle Networks.

The Controller Area Network (CAN), widely used for vehicular communication, is vulnerable to multiple types of cyber-threats. Attackers can inject malicious messages into the CAN bus through various channels, including wireless methods, entertainment systems, and on-board diagnostic ports. Therefore, it is crucial to develop a reliable intrusion detection system (IDS) capable of effectively distinguishing between legitimate and malicious CAN messages. In this paper, we propose a novel IDS architecture aimed at enhancing the cybersecurity of CAN bus systems in vehicles. Various machine learning (ML) models have been widely used to address similar problems; however, although existing ML-based IDS are computationally efficient, they suffer from suboptimal detection performance. To mitigate this shortcoming, our architecture incorporates specially designed rule-based filters that cross-check outputs from the traditional ML-based IDS. These filters scrutinize message ID and payload data to precisely capture the unique characteristics of three distinct types of cyberattacks: DoS attacks, spoofing attacks, and fuzzy attacks. Experimental evidence demonstrates that the proposed architecture leads to a significant improvement in detection performance across all utilized ML models. Specifically, all ML-based IDS achieved an accuracy exceeding 99% for every type of attack. This achievement highlights the robustness and effectiveness of our proposed solution in detecting potential threats.

Security risk models against attacks in smart grid using big data and artificial intelligence.

The need to update the electrical infrastructure led directly to the idea of smart grids (SG). Modern security technologies are almost perfect for detecting and preventing numerous attacks on the smart grid. They are unable to meet the challenging cyber security standards, nevertheless. We need many methods and techniques to effectively defend against cyber threats. Therefore, a more flexible approach is required to assess data sets and identify hidden risks. This is possible for vast amounts of data due to recent developments in artificial intelligence, machine learning, and deep learning. Due to adaptable base behavior models, machine learning can recognize new and unexpected attacks. Security will be significantly improved by combining new and previously released data sets with machine learning and predictive analytics. Artificial Intelligence (AI) and big data are used to learn more about the current situation and potential solutions for cybersecurity issues with smart grids. This article focuses on different types of attacks on the smart grid. Furthermore, it also focuses on the different challenges of AI in the smart grid. It also focuses on using big data in smart grids and other applications like healthcare. Finally, a solution to smart grid security issues using artificial intelligence and big data methods is discussed. In the end, some possible future directions are also discussed in this article. Researchers and graduate students are the audience of our article.

Recovery from Adversarial Attacks in Cyber-physical Systems: Shallow, Deep, and Exploratory Works

Cyber-physical systems (CPS) have experienced rapid growth in recent decades. However, like any other computer-based systems, malicious attacks evolve mutually, driving CPS to undesirable physical states, and potentially causing catastrophes. Although the current state-of-the-art is well aware of this issue, the majority of researchers have not focused on CPS recovery, the procedure we defined as restoring a CPS’s physical state back to a target condition under adversarial attacks. To call for attention on CPS recovery and identify existing efforts, we have surveyed a total of 30 relevant papers. We identify a major partition of the proposed recovery strategies: shallow recovery vs. deep recovery, where the former does not use a dedicated recovery controller while the latter does. Additionally, we surveyed exploratory research on topics that facilitate recovery. From these publications, we discuss the current state-of-the-art of CPS recovery, with respect to applications, attack type, attack surfaces, and system dynamics. Then, we identify untouched sub-domains in this field and suggest possible future directions for researchers.

The effect of sodium-glucose transporter 2 inhibitors on stroke in patients with type 2 diabetes: A meta-analysis

ObjectivesTo provide an update on the effect of sodium-glucose cotransporter 2 (SGLT2) inhibitors on stroke in patients with type 2 diabetes (T2D). MethodsPubMed, Embase, and Cochrane Library were systematically searched for randomized controlled trials (RCTs) comparing SGLT2 inhibitors versus placebo or other therapies in patients with T2D and reporting stroke endpoint. We computed the risk ratios (RRs) to binary endpoints, with 95 % confidence intervals (CIs). ResultsA total of 71 RCTs and 105,914 patients were included, of whom 62,488 (59 %) were randomized to the SGLT2 inhibitors group. The follow-up ranged from 12 weeks to 4.2 years. There were no significant differences between groups in all types of stroke (RR 0.96; 95 % CI 0.89-1.04), ischemic stroke (RR 0.89; 95 % CI 0.76-1.04), and transient ischemic attack (RR 0.96; 95 % CI 0.79-1.16). Patients on SGLT2 inhibitors experienced lower rates of hemorrhagic stroke (RR 0.62; 95 % CI 0.39-0.98). In the subgroup analysis of the type of drug, sotagliflozin significantly reduced all types of stroke (RR 0.74; 95 % CI 0.56-0.97). ConclusionIn this meta-analysis of 71 RCTs comprising 105,914 patients with T2D, SGLT2 inhibitors were not associated with a reduced risk of stroke and transient ischemic attack compared to placebo or other therapies; however, there was a trend toward reduced risk of hemorrhagic stroke. Among all SGLT2 inhibitors, sotagliflozin significantly reduced the risk of stroke.

Two-stage multi-datasource machine learning for attack technique and lifecycle detection

Intrusion detection systems (IDS) have increasingly adopted machine learning (ML) techniques to enhance their ability to detect a wide range of attack variants. However, the traditional focus in current research primarily revolves around identifying specific attack types or techniques using a single data source. However, this approach lacks a holistic perspective on attacks, which can result in missed detections. To improve the effectiveness of responding to detected attacks, it is essential to identify them based on their lifecycles and incorporate information from multiple data sources. In this study, we present three distinct approaches for detecting attack lifecycles, each leveraging different ML methodologies: a single-stage ML model, a two-stage ML+ML approach, and ML with sequence matching (ML+SM). Simultaneously, we explore the benefits of utilizing multiple data sources, including network traffic, system logs, and host statistics, to enhance technique detection capabilities. Our evaluation of these methods reveals that on lifecycle detection, the two-stage ML+ML approach outperforms the others, achieving an impressive F1 score of 0.994. In contrast, the single-stage and ML+SM methods yield F1 scores of 0.887 and 0.189, respectively. Furthermore, the integration of multiple data sources proves highly advantageous, with the combination of all three sources yielding the highest F1 score of 0.922 on technique detection.

Exhaustive distributed intrusion detection system for UAVs attacks detection and security enforcement (E-DIDS)

Distributed intrusion detection systems (DIDS) are a specialized subset of conventional IDSs designed for implementation in distributed environments. Each IDS is integrated into distinct entities within a monitored network, potentially distributed across various locations. These participating IDSs can be configured to detect either a particular or multiple attack types. Although DIDS has found extensive application in diverse IoT systems, its utilization in unmanned aerial vehicles (UAVs) still needs to be explored. Consequently, it is imperative to devise a comprehensive framework tailored explicitly for UAVs. It combines multiple detection units to enhance security. Based on the insights gained from previous studies, we propose an exhaustive DIDS for UAVs security enforcement in this paper. Our proposed solution offers a robust and scalable security approach. Through distributing the workload across interconnected IDSs deployed on the UAV, our solution was optimized for UAVs attacks detection to achieve high detection performance while reducing the complexity. To the best of our insight, there is no recorded DIDS for UAVs security, and attack detection has been proposed and evaluated. Furthermore, our paper provides a detailed analysis, outlining the development basis and the achieved results. We performed multiple experiments over different cases using different datasets. The achieved experimental results demonstrate that the proposed IDS has significantly high accuracy detection and low loss rates. Our proposed E-DIDS efficiently detects multiple attacks on different UAVs subsets with good global accuracy that reached 98.6% and low resource consumption.

An efficient provably secure authentication and key agreement protocol for satellite communication networks

AbstractThe leverage of satellite resources to establish a communication network offers a wide range of benefits, including the capability to support news gathering, broadcasting, and aeronautical and maritime tracking. The unique aspects of the satellite‐linked terrestrial network provide high‐speed services, dependable and consistent network quality, and comprehensive geographic coverage of remote regions. Unfortunately, these structural features also render the network vulnerable to unauthorized intrusion, potentially leading to significant disruptions. Consequently, the implementation of authentication measures presents an appealing solution for enhancing the overall service quality of this network. Recently, Kumar et al. presented an authentication and key agreement scheme for satellite communications. Strikingly, their scheme proves inadequate in safeguarding against several malicious attacks and reveals certain design weaknesses. In this article, we present a reliable and secure authentication protocol that takes advantage of the complexities inherent in the elliptic curve discrete logarithm problem. We assess the protocol's effectiveness against various types of attacks using formal proof, Burrows–Abadi–Needham logic, informal descriptive proof analysis, and the verification tool SCYTHER. Furthermore, we compare the computational, communication, and storage overhead of our proposed protocol to an existing one, demonstrating its efficiency and superiority.

DRL-GAN: A Hybrid Approach for Binary and Multiclass Network Intrusion Detection.

Our increasingly connected world continues to face an ever-growing number of network-based attacks. An Intrusion Detection System (IDS) is an essential security technology used for detecting these attacks. Although numerous Machine Learning-based IDSs have been proposed for the detection of malicious network traffic, the majority have difficulty properly detecting and classifying the more uncommon attack types. In this paper, we implement a novel hybrid technique using synthetic data produced by a Generative Adversarial Network (GAN) to use as input for training a Deep Reinforcement Learning (DRL) model. Our GAN model is trained on the NSL-KDD dataset, a publicly available collection of labeled network traffic data specifically designed to support the evaluation and benchmarking of IDSs. Ultimately, our findings demonstrate that training the DRL model on synthetic datasets generated by specific GAN models can result in better performance in correctly classifying minority classes over training on the true imbalanced dataset.

An innovative GPT-based open-source intelligence using historical cyber incident reports

In contemporary discourse, the pervasive influences of Generative Pre-Trained (GPT) and Large Language Models (LLM) are evident, showcasing diverse applications. GPT-based technologies, transcending mere summarization, exhibit adeptness in discerning critical information from extensive textual corpuses. Through prudent extraction of semantically meaningful content from textual representations, GPT technologies engender automated feature extraction, a departure from the fallible manual extraction methodologies. This study posits an innovative paradigm for extracting multidimensional cyber threat-related features from textual depictions of cyber events, leveraging the prowess of GPT. These extracted features serve as inputs for artificial intelligence (AI) and deep learning algorithms, including Convolutional Neural Network (CNN), Decomposition analysis, and Natural Language Processing (NLP)-based modalities tailored for non-technical cyber strategists. The proposed framework empowers cyber strategists or analysts to articulate inquiries regarding historical cyber incidents in plain English, with the NLP-based interaction facet of the system proffering cogent AI-driven insights in natural language. Furthermore, salient insights, often elusive in dynamic visualizations, are succinctly presented in plain language. Empirical validation of the entire system ensued through autonomous acquisition of semantically enriched contextual information concerning 214 major cyber incidents spanning from 2016 to 2023. GPT-based responses on Actor Type, Target, Attack Source (i.e., Country Originating Attack), Attack Destination (i.e., Targeted Country), Attack Level, Attack Type, and Attack Timeline, underwent critical AI-driven analysis. This comprehensive 7-dimensional information gleaned from the corpus of 214 incidents yielded a corpus of 1498 informative outputs, attaining a commendable precision of 96%, a recall rate of 98%, and an F1-Score of 97%.

An improved transformer‐based model for detecting phishing, spam and ham emails: A large language model approach

AbstractPhishing and spam have been a cybersecurity threat with the majority of breaches resulting from these types of social engineering attacks. Therefore, detection has been a long‐standing challenge for both academic and industry researcher. New and innovative approaches are required to keep up with the growing sophistication of threat actors. One such illumination which has vast potential are large language models (LLM). LLM emerged and already demonstrated their potential to transform society and provide new and innovative approaches to solve well‐established challenges. Phishing and spam have caused financial hardships and lost time and resources to email users all over the world and frequently serve as an entry point for ransomware threat actors. While detection approaches exist, especially heuristic‐based approaches, LLMs offer the potential to venture into a new unexplored area for understanding and solving this challenge. LLMs have rapidly altered the landscape from business, consumers, and throughout academia and demonstrate transformational potential to profoundly impact the society. Based on this, applying these new and innovative approaches to email detection is a rational next step in academic research. In this work, we present IPSDM, an improved phishing spam detection model based on fine‐tuning the BERT family of models to specifically detect phishing and spam emails. We demonstrate our fine‐tuned version, IPSDM, is able to better classify emails in both unbalanced and balanced datasets. Moreover, IPSDM consistently outperforms the baseline models in terms of classification accuracy, precision, recall, and F1‐score, while concurrently mitigating overfitting concerns.

SECURITY IN SOCIAL MEDIA: AWARNESS OF PHISHING ATTACKS TECHNIQUES AND COUNTER MEASURES

As time and technology progresses, the social media security is one of the basic and most important aspects to consider when browsing the internet.Nowadays all people using social media mostly, using social media is widely spread among different age groups to meet several purposes including education, entertainment, or even pursue a research. With the increase of using smartphones and social media, the cyber security threats also increased. The most widely- perpetrated form of social media attacks is phishing; it is used to gather sensitive personal and professional information such as social security numbers, credit card numbers, bank account numbers, user logins and passwords, as well as other information entered through a web site or online sources. Furthermore, many users of different age groups and mainly the old age people have been exposed to social media security violations most times due to a lack of awareness about phishing attacks and how to address with them. Our paper focuses primarily to make people of all age group aware of exposure to this type of attack while using social media networks. Key Words: Cyber security, Social media, Phishing attack

RobEns: Robust Ensemble Adversarial Machine Learning Framework for Securing IoT Traffic.

Recently, Machine Learning (ML)-based solutions have been widely adopted to tackle the wide range of security challenges that have affected the progress of the Internet of Things (IoT) in various domains. Despite the reported promising results, the ML-based Intrusion Detection System (IDS) proved to be vulnerable to adversarial examples, which pose an increasing threat. In fact, attackers employ Adversarial Machine Learning (AML) to cause severe performance degradation and thereby evade detection systems. This promoted the need for reliable defense strategies to handle performance and ensure secure networks. This work introduces RobEns, a robust ensemble framework that aims at: (i) exploiting state-of-the-art ML-based models alongside ensemble models for IDSs in the IoT network; (ii) investigating the impact of evasion AML attacks against the provided models within a black-box scenario; and (iii) evaluating the robustness of the considered models after deploying relevant defense methods. In particular, four typical AML attacks are considered to investigate six ML-based IDSs using three benchmarking datasets. Moreover, multi-class classification scenarios are designed to assess the performance of each attack type. The experiments indicated a drastic drop in detection accuracy for some attempts. To harden the IDS even further, two defense mechanisms were derived from both data-based and model-based methods. Specifically, these methods relied on feature squeezing as well as adversarial training defense strategies. They yielded promising results, enhanced robustness, and maintained standard accuracy in the presence or absence of adversaries. The obtained results proved the efficiency of the proposed framework in robustifying IDS performance within the IoT context. In particular, the accuracy reached 100% for black-box attack scenarios while preserving the accuracy in the absence of attacks as well.

3D medical image encryption algorithm using biometric key and cubic S-box

Considering the scarcity of research on 3D medical image encryption, this paper proposes a novel 3D medical image encryption scheme based on biometric key and cubic S-box. To enhance the data security, biometric keys are utilized to overcome the limitations of traditional methods where secret keys with no practical meaning, fixed length, and finite key space, while cubic S-box is constructed to increase the nonlinearity of image cryptosystem. The proposed cryptosystem mainly consists of four phases: pseudo-random sequence generation, confusion, substitution, and diffusion. Firstly, the stepwise iterative algorithm based on coupled chaotic systems is utilized for generating pseudo-random sequences for confusion and diffusion. Secondly, the confusion algorithm based on multiple sorting can scramble pixel positions in 3D images. Thirdly, guided by the designed cubic S-box, pixel substitution is executed sequentially. Lastly, the diffusion algorithm based on ECA and finite field multiplication is capable of increasing the plaintext sensitivity of cryptosystem by concealing the statistical characteristics of plaintext. Simulation experiments performed on multiple 3D medical images demonstrate that the proposed encryption scheme exhibits favorable statistical performance, sufficiently large key space, strong system sensitivity and robustness, and can resist various typical cryptographic attacks.

Secure deep learning-based energy efficient routing with intrusion detection system for wireless sensor networks

In general, wireless sensor networks are used in various industries, including environmental monitoring, military applications, and queue tracking. To support vital applications, it is crucial to ensure effectiveness and security. To prolong the network lifetime, most current works either introduce energy-preserving and dynamic clustering strategies to maintain the optimal energy level or attempt to address intrusion detection to fix attacks. In addition, some strategies use routing algorithms to secure the network from one or two attacks to meet this requirement, but many fewer solutions can withstand multiple types of attacks. So, this paper proposes a secure deep learning-based energy-efficient routing (SDLEER) mechanism for WSNs that comes with an intrusion detection system for detecting attacks in the network. The proposed system overcomes the existing solutions’ drawbacks by including energy-efficient intrusion detection and prevention mechanisms in a single network. The system transfers the network’s data in an energy-aware manner and detects various kinds of network attacks in WSNs. The proposed system mainly comprises two phases, such as optimal cluster-based energy-aware routing and deep learning-based intrusion detection system. Initially, the cluster of sensor nodes is formed using the density peak k-mean clustering algorithm. After that, the proposed system applies an improved pelican optimization approach to select the cluster heads optimally. The data are transmitted to the base station via the chosen optimal cluster heads. Next, in the attack detection phase, the preprocessing operations, such as missing value imputation and normalization, are done on the gathered dataset. Next, the proposed system applies principal component analysis to reduce the dimensionality of the dataset. Finally, intrusion classification is performed by Smish activation included recurrent neural networks. The proposed system uses the NSL-KDD dataset to train and test it. The proposed one consumes a minimum energy of 49.67 mJ, achieves a better delivery rate of 99.92%, takes less lifetime of 5902 rounds, 0.057 s delay, and achieves a higher throughput of 0.99 Mbps when considering a maximum of 500 nodes in the network. Also, the proposed one achieves 99.76% accuracy for the intrusion detection. Thus, the simulation outcomes prove the superiority of the proposed SDLEER system over the existing schemes for routing and attack detection.

Impulsive synchronization control for dynamic networks subject to double deception attacks

This paper investigates the problem of secure synchronization for coupled dynamic networks subject to two types of deception attacks. Deception attacks are considered in both the controller-to-actuator and sensor-to-controller communication channels and modeled as Bernoulli distributions. The same and different attack probabilities of each node in dynamic networks are considered, respectively. A novel secure impulsive controller is introduced, mitigating malicious information from deception attacks and ensuring bounded synchronization. The average impulsive interval (AII) method is employed to relax the constraints on the upper and lower bounds of consecutive impulses, which is not adequately addressed by the previous secure impulsive control methods. Additionally, the paper establishes an upper bound for synchronization errors and proposes a method to design the AII based on attack probabilities. Finally, two numerical examples are provided to show the validity of our control approach.

A Comprehensive Review of Phishing Attacks Techniques, Types and Solutions

Phishing attacks continue to pose significant threats to individuals, businesses, and organizations worldwide. This paper presents an in-depth analysis of phishing attacks, focusing on their techniques, and highlighting their methods, consequences, and strategies for prevention. Through an extensive analysis of existing literature, this review aims to provide insights into the evolving nature of phishing attacks and effective strategies for detection, prevention, and mitigation. Scammers employ phishing, a con game, to get personal information from unwary users. False emails can have a startlingly authentic appearance, and even websites requesting personal information from consumers may seem genuine. Phishing is like lake fishing; however, instead of aiming to catch fish, phishers strive to obtain personal data. This article provides readers with a quick overview of phishing, its assaults, and precautions they may take to protect their private information. Phishing is an ever-expanding cyber threat that costs internet users billions of dollars in damages annually. It is an illegal operation that gathers private information from Internet users by using social engineering and other technologies.