Multiobjective scheduling for semiconductor manufacturing plants

Scheduling of semiconductor wafer manufacturing system is identified as a complex problem, involving multiple and conflicting objectives (minimization of facility average utilization, minimization of waiting time and storage, for instance) to simultaneously satisfy. In this study, we propose an efficient approach based on an artificial neural network technique embedded into a multiobjective genetic algorithm for multi-decision scheduling problems in a semiconductor wafer fabrication environment

Artificial intelligence in the cyber domain: Offense and defense

Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41

Cryptic complexity in felid vertebral evolution: shape differentiation and allometry of the axial skeleton

Members of the mammalian family Felidae (extant and extinct cats) are grossly phenotypically similar, but display a 300-fold range in body size, from less than 1 kg to more than 300 kg. In addition to differences in body mass, felid species show dietary and locomotory specializations that correlate to skull and limb osteological measurements, such as shape or cross-sectional area. However, ecological correlates to the axial skeleton are yet untested. Here, we build on previous studies of the biomechanical and morphological evolution of the felid appendicular skeleton by conducting a quantitative analysis of morphology and allometry in the presacral vertebral column across extant cats. Our results demonstrate that vertebral columns of arboreal, scansorial and terrestrial felids significantly differ in morphology, specifically in the lumbar region, while no distinction based on dietary specialization was found. Body size significantly influences vertebral morphology, with clear regionalization of allometry along the vertebral column, suggesting that anterior (cervicals and thoracics) and posterior (lumbar) vertebrae may be independently subjected to distinct selection pressures

Insertion Detection System Employing Neural Network MLP and Detection Trees Using Different Techniques

by addressing intruder attacks, network security experts work to maintain services available at all times. The Intrusion Detection System (IDS) is one of the available mechanisms for detecting and classifying any abnormal behavior. As a result, the IDS must always be up to date with the most recent intruder attack signatures to maintain the confidentiality, integrity, and availability of the services. This paper shows how the NSL-KDD dataset may be used to test and evaluate various Machine Learning techniques. It focuses mostly on the NLS-KDD pre-processing step to create an acceptable and balanced experimental data set to improve accuracy and minimize false positives. For this study, the approaches J48 and MLP were employed. The Decision Trees classifier has been demonstrated to have the highest accuracy rate for detecting and categorizing all NSL-KDD dataset attacks

A Review on Cybersecurity based on Machine Learning and Deep Learning Algorithms

Machin learning (ML) and Deep Learning (DL) technique have been widely applied to areas like image processing and speech recognition so far. Likewise, ML and DL plays a critical role in detecting and preventing in the field of cybersecurity. In this review, we focus on recent ML and DL algorithms that have been proposed in cybersecurity, network intrusion detection, malware detection. We also discuss key elements of cybersecurity, main principle of information security and the most common methods used to threaten cybersecurity. Finally, concluding remarks are discussed including the possible research topics that can be taken into consideration to enhance various cyber security applications using DL and ML algorithms

Implementation and Analysis of Combined Machine Learning Method for Intrusion Detection System

As one of the security components in Network Security Monitoring System, Intrusion Detection System (IDS) is implemented by many organizations in their networks to detect and address the impact of network attacks. There are many machine-learning methods that have been widely developed and applied in the IDS. Selection of appropriate methods is necessary to improve the detection accuracy in the application of machine-learning in IDS. In this research we proposed an IDS that we developed based on machine learning approach. We use 28 features subset without content features of  Knowledge Data Discovery (KDD) dataset to build machine learning model. From our analysis and experiment we get 28 features subset of KDD dataset that are most likely to be applied for the IDS in the real network. The machine learning model based on this 28 features subset obtained 99.9% accuracy for both two-class and multiclass classification. From our experiments using the IDS we have developed show good performance in detecting attacks on real networks

Network Intrusion Detection System using Deep Learning Technique

The rise in the usage of the internet in this recent time had led to tremendous development in computer networks with large volumes of information transported daily. This development has generated lots of security threats and privacy concerns on networks and data. To tackle these issues, several protective measures have been developed including the Intrusion Detection Systems (IDSs). IDS plays a major backbone in network security and provides an extra layer of security to other security defence mechanisms in a network. However, existing IDS built on a signature base such as snort and the likes are unable to detect unknown and novel threats. Anomaly detection-based IDSs that use Machine Learning (ML) approaches are not scalable when enormous data are presented, and during modelling, the runtime increases as the dataset size increases which needs high computational resources to fulfil the runtime requirements. This thesis proposes a Feedforward Deep Neural Network (FFDNN) for an intrusion detection system that performs a binary classification on the popular NSL-Knowledge discovery and data mining (NSL-KDD) dataset. The model was developed from Keras API integrated into TensorFlow in Google's colaboratory software environment. Three variants of FFDNNs were trained using the NSL-KDD dataset and the network architecture consisted of two hidden layers with 64 and 32; 32 and 16; 512 and 256 neurons respectively, and each with the ReLu activation function. The sigmoid activation function for binary classification was used in the output layer and the prediction loss function used was the binary cross-entropy. Regularization was set to a dropout rate of 0.2 and the Adam optimizer was used. The deep neural networks were trained for 16, 20, 20 epochs respectively for batch sizes of 256, 64, and 128. After evaluating the performances of the FFDNNs on the training data, the prediction was made on test data, and accuracies of 89%, 84%, and 87% were achieved. The experiment was also conducted on the same training dataset (NSL-KDD) using the conventional machine learning algorithms (Random Forest; K-nearest neighbor; Logistic regression; Decision tree; and Naïve Bayes) and predictions of each algorithm on the test data gave different performance accuracies of 81%, 76%, 77%, 77%, 77%, respectively. The performance results of the FFDNNs were calculated based on some important metrics (FPR, FAR, F1 Measure, Precision), and these were compared to the conventional ML algorithms and the outcome shows that the deep neural networks performed best due to their dense architecture that made it scalable with the large size of the dataset and also offered a faster run time during training in contrast to the slow run time of the Conventional ML. This implies that when the dataset is large and a faster computation is required, then FFDNN is a better choice for best performance accuracy

Adversarial Machine Learning in Network Intrusion Detection Systems

Adversarial examples are inputs to a machine learning system intentionally crafted by an attacker to fool the model into producing an incorrect output. These examples have achieved a great deal of success in several domains such as image recognition, speech recognition and spam detection. In this paper, we study the nature of the adversarial problem in Network Intrusion Detection Systems (NIDS). We focus on the attack perspective, which includes techniques to generate adversarial examples capable of evading a variety of machine learning models. More specifically, we explore the use of evolutionary computation (particle swarm optimization and genetic algorithm) and deep learning (generative adversarial networks) as tools for adversarial example generation. To assess the performance of these algorithms in evading a NIDS, we apply them to two publicly available data sets, namely the NSL-KDD and UNSW-NB15, and we contrast them to a baseline perturbation method: Monte Carlo simulation. The results show that our adversarial example generation techniques cause high misclassification rates in eleven different machine learning models, along with a voting classifier. Our work highlights the vulnerability of machine learning based NIDS in the face of adversarial perturbation.Comment: 25 pages, 6 figures, 4 table

EXPLAINABLE DEEP NEURAL NETWORK BASED ANALYSIS ON INTRUSION DETECTION SYSTEMS

The research on Intrusion Detection Systems (IDSs) have been increasing in recent years. Particularly, the research which are widely utilizing machine learning concepts, and it is proven that these concepts were effective with IDSs, particularly, deep neural network-based models enhanced the rate of detections of IDSs. At the same instance, the models are turning out to be very highly complex, users are unable to track down the explanations for the decisions made which indicates the necessity of identifying the explanations behind those decisions to ensure the interpretability of the framed model. In this aspect, the article deals with the proposed model that able to explain the obtained predictions. The proposed framework is a combination of a conventional intrusion detection system with the aid of a deep neural network and interpretability of the model predictions. The proposed model utilizes Shapley Additive Explanations (SHAP) that mixes with the local explainability as well as the global explainability for the enhancement of interpretations in the case of intrusion detection systems. The proposed model was implemented using the popular dataset, NSL-KDD, and the performance of the framework evaluated using accuracy, precision, recall, and F1-score. The accuracy of the framework is achieved by about 99.99%. The proposed framework able to identify the top 4 features using local explainability and the top 20 features using global explainability