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A malicious attack may endanger human life or pollute environment on a cyber-physical system (CPS). However, successfully attacking a CPS needs not only the knowledge of information technology (IT) but also the domain knowledge of the system’s operation technology (OT). Therefore, it is critical to identify the vulnerabilities of a CPS. This paper...
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Models are used in both the Software Engineering (SE) and the Artificial Intelligence (AI) communities. In the former case, models of software, which may specify the software system architecture on different levels of abstraction could be used in various stages of the Software Development Life-Cycle (SDLC), from early conceptualization and design,...
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... Hence, several existing research explains Machine Learning (ML) algorithms for the cyber intrusion detection system to avoid such constraints and improve detection [16]. However, the majority of the classification models failed to detect unknown threats, and they must be retrained regularly to maintain high detection rates [17]. This is not feasible because obtaining tagged data is challenging. ...
Intrusion Detection System (IDS) is a network security mechanism that analyses all users’ and applications’ traffic and detects malicious activities in real-time. The existing IDS methods suffer from lower accuracy and lack the required level of security to prevent sophisticated attacks. This problem can result in the system being vulnerable to attacks, which can lead to the loss of sensitive data and potential system failure. Therefore, this paper proposes an Intrusion Detection System using Logistic Tanh-based Convolutional Neural Network Classification (LTH-CNN). Here, the Correlation Coefficient based Mayfly Optimization (CC-MA) algorithm is used to extract the input characteristics for the IDS from the input data. Then, the optimized features are utilized by the LTH-CNN, which returns the attacked and non-attacked data. After that, the attacked data is stored in the log file and non-attacked data is mapped to the cyber security and data security phases. To prevent the system from cyber-attack, the Source and Destination IP address is converted into a complex binary format named 1’s Complement Reverse Shift Right (CRSR), where, in the data security phase the sensed data is converted into an encrypted format using Senders Public key Exclusive OR Receivers Public Key-Elliptic Curve Cryptography (PXORP-ECC) Algorithm to improve the data security. The Network Security Laboratory–Knowledge Discovery in Databases (NSL-KDD) dataset and real-time sensor are used to train and evaluate the proposed LTH-CNN. The suggested model is evaluated based on accuracy, sensitivity, and specificity, which outperformed the existing IDS methods, according to the results of the experiments.
... 1 The identification and detection of prohibited items such as regulated firearms, knives, explosives, and hazardous chemicals are crucial measures to mitigate public risks in various public spaces. 2,3 Metal detectors are primarily utilized for detecting various types of metallic firearms, terrorist tools, and sharp-edged metal knives. 4 In fixed locations such as airports, railway stations, and important conference venues, metal detectors predominantly rely on X-ray technology, which is bulky, expensive, and involves imaging techniques, thereby posing challenges for on-site and timely detection. ...
Intelligent wearable devices for security detection integrate flexible electronic components into protective equipment, enabling the detection of metals, explosives, human physiological conditions, and hazardous chemicals. While numerous studies have investigated various security detection technologies, a systematic literature review providing a comprehensive overview of the preparation and applications of intelligent wearables for security detection is lacking. Therefore, this paper aims to fill this gap by conducting a comprehensive examination of the application of intelligent wearables for security detection in specialized and temporary disposal environments. Additionally, the paper addresses existing challenges and discusses future directions for achieving greater progress in this domain.
... As an intuitive model-checking technique, timed automata [8] (TA) have also found a wide range of applications in the modeling and verification of discrete-event systems (DESs). Several works have used timed automata and their toolbox UPPAAL [9][10][11] as a modeling and simulation environment [12][13][14][15][16]. However, with the significant development of technologies in recent reconfigurable DESs, state-transition systems such as TA and Petri nets [17] exhibit several shortcomings in the design of these modern systems. ...
... In the literature, an extensive amount of research has used timed automata to model and verify various kinds of real-time systems [12][13][14][15][16]. The automated consistency checks of the reconfiguration steps of dynamic software product lines (DSPLs) were facilitated in [25] by translating real-time requirements into TA. ...
Modern discrete-event systems (DESs) are often characterized by their dynamic structures enabling highly flexible behaviors that can respond in real time to volatile environments. On the other hand, timed automata (TA) are powerful tools used to design various DESs. However, they lack the ability to naturally describe dynamic-structure reconfigurable systems. Indeed, TA are characterized by their rigid structures, which cannot handle the complexity of dynamic structures. To overcome this limitation, we propose an extension to TA, called dynamic timed automata (DTA), enabling the modeling and verification of reconfigurable systems. Additionally, we present a new algorithm that transforms DTA into semantic-equivalent TA while preserving their behavior. We demonstrate the usefulness and applicability of this new modeling and verification technique using an illustrative example.
Cyber security is developing factor for protecting internet resources by handing various monitoring feature based support to improve the security. Increasing internet cries in the defined facts for need of advance met in cyber security. Most internet attacker’s theft the information through malicious activities, false data injection, hacking and make soon creating procedures. In most cases cyber sercuity failed to detect the malicious activities because the monitoring feature analyses improper to predict the result in previous machine learning algorithms. TO resolve this problem to propose an advance cyber security based on flow-based anomaly detection using Min max game theory optimized artificial neural network (MMGT-ANN). The reprocessing was carried out with KDD crime dataset. Then Data driven network model is applied to monitor the feature margins and defect scaling rate. Based on the feature scaling rate Transmission Flow defect rate is estimated and applied with Min max Game theory to select the feature limits. Then features are trained with optimized ANN to detect the crime rate. By the attention of the proposed system achieves higher performance in precision rate to attain higher detection accuracy with lower time complexity compared to the other system.
With the improvement of industrial informatization, various industrial control system network protocols have also been widely used. The reliability of these protocols will directly affect the safety of industrial control systems. As an effective method that can automatically analyze system reliability, model checking has been widely used in the verification of various safety‐critical systems. In this paper, we propose a modeling design method for industrial control network protocol based on time semantic reconstruction of time state transition matrix (TSTM). In addition, we provide a TSTM model checking method based on linear temporal logic (LTL). In order to effectively alleviate the state space explosion, the method adopts bounded model checking (BMC) technology. Furthermore, we implement a TSTM model verification tool called ICPV. Finally, we apply the above method to the modeling and verification of the industrial control network protocol Powerlink and through a comparison experiment with UPPAAL to illustrate the effectiveness of the method proposed in this paper. This paper reconstructs the time semantics of TSTM to make it more suitable for modeling and verification of industrial control network protocols. On this basis, the corresponding modeling design method, model verification method, and verification tool called ICPV are proposed. Finally, the effectiveness of the method is illustrated by a complete case study of an actual industrial control network protocol.
