Biblio

The increase in online activity during the COVID 19 pandemic has generated a surge in network traffic capable of expanding the scope of DDoS attacks. Cyber criminals can now afford to launch massive DDoS attacks capable of degrading the performances of conventional machine learning based IDS models. Hence, there is an urgent need for an effective DDoS attack detective model with the capacity to handle large magnitude of DDoS attack traffic. This study proposes a deep learning based DDoS attack detection system using Long Short Term Memory (LSTM). The proposed model was evaluated on UNSW-NB15 and NSL-KDD intrusion datasets, whereby twenty-three (23) and twenty (20) attack features were extracted from UNSW-NB15 and NSL-KDD, respectively using Singular Value Decomposition (SVD). The results from the proposed model show significant improvement when compared with results from some conventional machine learning techniques such as Naïve Bayes (NB), Decision Tree (DT), and Support Vector Machine (SVM) with accuracies of 94.28% and 90.59% on both datasets, respectively. Furthermore, comparative analysis of LSTM with other deep learning results reported in literature justified the choice of LSTM among its deep learning peers in detecting DDoS attacks over a network.

To reduce the risk of botnet malware, methods of detecting botnet malware using machine learning have received enormous attention in recent years. Most of the traditional methods are based on supervised learning that relies on static features with defined labels. However, recent studies show that supervised machine learning-based IoT malware botnet models are more vulnerable to intentional attacks, known as an adversarial attack. In this paper, we study the adversarial attack on PSI-graph based researches. To perform the efficient attack, we proposed a reinforcement learning based method with a trained target classifier to modify the structures of PSI-graphs. We show that PSI-graphs are vulnerable to such attack. We also discuss about defense method which uses adversarial training to train a defensive model. Experiment result achieves 94.1% accuracy on the adversarial dataset; thus, shows that our defensive model is much more robust than the previous target classifier.

Sound plays a key role in human life and therefore sound recognition system has a great future ahead. Sound classification and identification system has many applications such as system for personal security, critical surveillance, etc. The main aim of this paper is to detect and classify the security sound event using the surveillance camera systems with integrated microphone based on the generated spectrograms of the sounds. This will enable to track security events in cases of emergencies. The goal is to propose a security system to accurately detect sound events and make a better security sound event detection system. We propose to use a convolutional neural network (CNN) to design the security sound detection system to detect a security event with minimal sound. We used the spectrogram images to train the CNN. The neural network was trained using different security sounds data which was then used to detect security sound events during testing phase. We used two datasets for our experiment training and testing datasets. Both the datasets contain 3 different sound events (glass break, gun shots and smoke alarms) to train and test the model, respectively. The proposed system yields the good accuracy for the sound event detection even with minimum available sound data. The designed system achieved accuracy was 92% and 90% using CNN on training dataset and testing dataset. We conclude that the proposed sound classification framework which using the spectrogram images of sounds can be used efficiently to develop the sound classification and recognition systems.

Network intrusion detection is an important research direction of network security. The diversification of network intrusion mode and the increasing amount of network data make the traditional detection methods can not meet the requirements of the current network environment. The development of deep learning technology and its successful application in the field of artificial intelligence provide a new solution for network intrusion detection. In this paper, the convolutional neural network in deep learning is applied to network intrusion detection, and an intelligent detection model which can actively learn is established. The experiment on KDD99 data set shows that it can effectively improve the accuracy and adaptive ability of intrusion detection, and has certain effectiveness and advancement.

Machine learning methods are growing in relevance for biometrics and personal information processing in domains such as forensics, e-health, recruitment, and e-learning. In these domains, white-box (human-readable) explanations of systems built on machine learning methods can become crucial. Inductive Logic Programming (ILP) is a subfield of symbolic AI aimed to automatically learn declarative theories about the process of data. Learning from Interpretation Transition (LFIT) is an ILP technique that can learn a propositional logic theory equivalent to a given blackbox system (under certain conditions). The present work takes a first step to a general methodology to incorporate accurate declarative explanations to classic machine learning by checking the viability of LFIT in a specific AI application scenario: fair recruitment based on an automatic tool generated with machine learning methods for ranking Curricula Vitae that incorporates soft biometric information (gender and ethnicity). We show the expressiveness of LFIT for this specific problem and propose a scheme that can be applicable to other domains.

The most representative method of deep learning is data-driven learning. These methods are often data-dependent, and lack of data leads to poor learning. There is a GAN method that creates a likely image as a way to solve a problem that lacks data. The GAN determines that the discriminator is fake/real with respect to the image created so that the generator learns. However, overfitting problems when the discriminator becomes overly dependent on the learning data. In this paper, we explain overfitting problem when the discriminator decides to fake/real using xAI. Depending on the area of the described image, it is possible to limit the learning of the discriminator to avoid overfitting. By doing so, the generator can produce similar but more diverse images.

Electromagnetic (EM) radiation is an inherent phenomenon in the operation of electronic information equipment. The side-channel attack, malicious hardware and software implantation attack by using the EM radiation are implemented to steal information. This form of attacks can be used in air-gap information equipment, which bring great danger for information security. The malicious implantation hidden in circuits are difficult to detect. How to detect the implantation is a challenging problem. In this paper, a malicious hardware implantation is analyzed. A method that leverages EM signals for Trojan-embedded computer video cable detection is proposed. The method neither needs activating the Trojan nor requires near-field probe approaching at close. It utilizes recognizable patterns in the spectrum of EM to predict potential risks. This paper focuses on the extraction of feature vectors via the empirical mode decomposition (EMD) algorithm. Intrinsic mode functions (IMFs) are analyzed and selected to be eigenvectors. Using a common classification technique, we can achieve both effective and reliable detection results.

Physically Unclonable Functions (PUFs) have been considered as promising lightweight primitives for random number generation and device authentication. Thanks to the imperfections occurring during the fabrication process of integrated circuits, each PUF generates a unique signature which can be used for chip identification. Although supposed to be unclonable, PUFs have been shown to be vulnerable to modeling attacks where a set of collected challenge response pairs are used for training a machine learning model to predict the PUF response to unseen challenges. Challenge obfuscation has been proposed to tackle the modeling attacks in recent years. However, knowing the obfuscation algorithm can help the adversary to model the PUF. This paper proposes a modeling-resilient arbiter-PUF architecture that benefits from the randomness provided by PUFs in concealing the obfuscation scheme. The experimental results confirm the effectiveness of the proposed structure in countering PUF modeling attacks.

Differentially private (DP) machine learning allows us to train models on private data while limiting data leakage. DP formalizes this data leakage through a cryptographic game, where an adversary must predict if a model was trained on a dataset D, or a dataset D′ that differs in just one example. If observing the training algorithm does not meaningfully increase the adversary's odds of successfully guessing which dataset the model was trained on, then the algorithm is said to be differentially private. Hence, the purpose of privacy analysis is to upper bound the probability that any adversary could successfully guess which dataset the model was trained on.In our paper, we instantiate this hypothetical adversary in order to establish lower bounds on the probability that this distinguishing game can be won. We use this adversary to evaluate the importance of the adversary capabilities allowed in the privacy analysis of DP training algorithms.For DP-SGD, the most common method for training neural networks with differential privacy, our lower bounds are tight and match the theoretical upper bound. This implies that in order to prove better upper bounds, it will be necessary to make use of additional assumptions. Fortunately, we find that our attacks are significantly weaker when additional (realistic) restrictions are put in place on the adversary's capabilities. Thus, in the practical setting common to many real-world deployments, there is a gap between our lower bounds and the upper bounds provided by the analysis: differential privacy is conservative and adversaries may not be able to leak as much information as suggested by the theoretical bound.

The paper considers the issue of recurrent neural networks applicability for detecting industrial process anomalies to detect intrusion in Industrial Control Systems. Cyberattack on Industrial Control Systems often leads to appearing of anomalies in industrial process. Thus, it is proposed to detect such anomalies by forecasting the state of an industrial process using a recurrent neural network and comparing the predicted state with actual process' state. In the course of experimental research, a recurrent neural network with one-dimensional convolutional layer was implemented. The Secure Water Treatment dataset was used to train model and assess its quality. The obtained results indicate the possibility of using the proposed method in practice. The proposed method is characterized by the absence of the need to use anomaly data for training. Also, the method has significant interpretability and allows to localize an anomaly by pointing to a sensor or actuator whose signal does not match the model's prediction.

Most of existing audio fingerprinting systems have limitations to be used for high-specific audio retrieval at scale. In this work, we generate a low-dimensional representation from a short unit segment of audio, and couple this fingerprint with a fast maximum inner-product search. To this end, we present a contrastive learning framework that derives from the segment-level search objective. Each update in training uses a batch consisting of a set of pseudo labels, randomly selected original samples, and their augmented replicas. These replicas can simulate the degrading effects on original audio signals by applying small time offsets and various types of distortions, such as background noise and room/microphone impulse responses. In the segment-level search task, where the conventional audio fingerprinting systems used to fail, our system using 10x smaller storage has shown promising results. Our code and dataset are available at https://mimbres.github.io/neural-audio-fp/.

Large enterprises offer thousands of micro-services applications to support their daily business activities by using Application Programming Interfaces (APIs). These applications generate huge amounts of traffic via millions of API calls every day, which is difficult to analyze for detecting any potential abnormal behaviour and application outage. This phenomenon makes Machine Learning (ML) a natural choice to leverage and analyze the API traffic and obtain intelligent predictions. This paper proposes an ML-based technique to detect and classify API traffic based on specific features like bandwidth and number of requests per token. We employ a Support Vector Machine (SVM) as a binary classifier to classify the abnormal API traffic using its linear kernel. Due to the scarcity of the API dataset, we created a synthetic dataset inspired by the real-world API dataset. Then we used the Gaussian distribution outlier detection technique to create a training labeled dataset simulating real-world API logs data which we used to train the SVM classifier. Furthermore, to find a trade-off between accuracy and false positives, we aim at finding the optimal value of the error term (C) of the classifier. The proposed anomaly detection method can be used in a plug and play manner, and fits into the existing micro-service architecture with little adjustments in order to provide accurate results in a fast and reliable way. Our results demonstrate that the proposed method achieves an F1-score of 0.964 in detecting anomalies in API traffic with a 7.3% of false positives rate.

Application programming interfaces (APIs) are a vital part of every online business. APIs are responsible for transferring data across systems within a company or to the users through the web or mobile applications. Security is a concern for any public-facing application. The objective of this study is to analyze incoming requests to a target API and flag any malicious activity. This paper proposes a solution using sequence models to identify whether or not an API request has SQL, XML, JSON, and other types of malicious injections. We also propose a novel heuristic procedure that minimizes the number of false positives. False positives are the valid API requests that are misclassified as malicious by the model.

Smart meters (SMs) can pose privacy threats for consumers, an issue that has received significant attention in recent years. This paper studies the impact of Side Information (SI) on the performance of possible attacks to real-time privacy-preserving algorithms for SMs. In particular, we consider a deep adversarial learning framework, in which the desired releaser, which is a Recurrent Neural Network (RNN), is trained by fighting against an adversary network until convergence. To define the objective for training, two different approaches are considered: the Causal Adversarial Learning (CAL) and the Directed Information (DI)-based learning. The main difference between these approaches relies on how the privacy term is measured during the training process. The releaser in the CAL method, disposing of supervision from the actual values of the private variables and feedback from the adversary performance, tries to minimize the adversary log-likelihood. On the other hand, the releaser in the DI approach completely relies on the feedback received from the adversary and is optimized to maximize its uncertainty. The performance of these two algorithms is evaluated empirically using real-world SMs data, considering an attacker with access to SI (e.g., the day of the week) that tries to infer the occupancy status from the released SMs data. The results show that, although they perform similarly when the attacker does not exploit the SI, in general, the CAL method is less sensitive to the inclusion of SI. However, in both cases, privacy levels are significantly affected, particularly when multiple sources of SI are included.

For the task of natural language processing (NLP), Word embedding technology has a certain impact on the accuracy of deep neural network algorithms. Considering that the current word embedding method cannot realize the coexistence of words and phrases in the same vector space. Therefore, we propose an enhanced word embedding (EWE) method. Before completing the word embedding, this method introduces a unique sentence reorganization technology to rewrite all the sentences in the original training corpus. Then, all the original corpus and the reorganized corpus are merged together as the training corpus of the distributed word embedding model, so as to realize the coexistence problem of words and phrases in the same vector space. We carried out experiment to demonstrate the effectiveness of the EWE algorithm on three classic benchmark datasets. The results show that the EWE method can significantly improve the classification performance of the CNN model.

Hierarchical Text classification has recently become increasingly challenging with the growing number of classification labels. In this paper, we propose a hierarchical fine-tuning based approach for hierarchical text classification. We use the ordered neurons LSTM (ONLSTM) model by combining the embedding of text and parent category for hierarchical text classification with a large number of categories, which makes full use of the connection between the upper-level and lower-level labels. Extensive experiments show that our model outperforms the state-of-the-art hierarchical model at a lower computation cost.

This paper presents an approach for the identification of vulnerable IoT applications using deep learning algorithms. The approach focuses on a category of vulnerabilities that leads to sensitive information leakage which can be identified using taint flow analysis. First, we analyze the source code of IoT apps in order to recover tokens along their frequencies and tainted flows. Second, we develop, Token2Vec, which transforms the source code tokens into vectors. We have also developed Flow2Vec, which transforms the identified tainted flows into vectors. Third, we use the recovered vectors to train a deep learning algorithm to build a model for the identification of tainted apps. We have evaluated the approach on two datasets and the experiments show that the proposed approach of combining tainted flows features with the base benchmark that uses token frequencies only, has improved the accuracy of the prediction models from 77.78% to 92.59% for Corpus1 and 61.11% to 87.03% for Corpus2.

In recent years, we have faced a series of online threats. The continuous malicious attacks on the network have directly caused a huge threat to the user's spirit and property. In order to deal with the complex security situation in today's network environment, an intelligent network situational awareness model based on deep neural networks is proposed. Use the nonlinear characteristics of the deep neural network to solve the nonlinear fitting problem, establish a network security situation assessment system, take the situation indicators output by the situation assessment system as a guide, and collect on the main data features according to the characteristics of the network attack method, the main data features are collected and the data is preprocessed. This model designs and trains a 4-layer neural network model, and then use the trained deep neural network model to understand and analyze the network situation data, so as to build the network situation perception model based on deep neural network. The deep neural network situational awareness model designed in this paper is used as a network situational awareness simulation attack prediction experiment. At the same time, it is compared with the perception model using gray theory and Support Vector Machine(SVM). The experiments show that this model can make perception according to the changes of state characteristics of network situation data, establish understanding through learning, and finally achieve accurate prediction of network attacks. Through comparison experiments, datatypized neural network deep neural network situation perception model is proved to be effective, accurate and superior.

Domain Generation Algorithms (DGAs) are used by adversaries to establish Command and Control (C&C) server communications during cyber attacks. Blacklists of known/identified C&C domains are used as one of the defense mechanisms. However, static blacklists generated by signature-based approaches can neither keep up nor detect never-seen-before malicious domain names. To address this weakness, we applied a DGA-based malicious domain classifier using the Long Short-Term Memory (LSTM) method with a novel feature engineering technique. Our model's performance shows a greater accuracy compared to a previously reported model. Additionally, we propose a new adversarial machine learning-based method to generate never-before-seen malware-related domain families. We augment the training dataset with new samples to make the training of the models more effective in detecting never-before-seen malicious domain names. To protect blacklists of malicious domain names against adversarial access and modifications, we devise secure data containers to store and transfer blacklists.

The identification of spectrum opportunities is a pivotal requirement for efficient spectrum utilization in cognitive radio systems. Spectrum prediction offers a convenient means for revealing such opportunities based on the previously obtained occupancies. As spectrum occupancy states are correlated over time, spectrum prediction is often cast as a predictable time-series process using classical or deep learning-based models. However, this variety of methods exploits time-domain correlation and overlooks the existing correlation over frequency. In this paper, differently from previous works, we investigate a more realistic scenario by exploiting correlation over time and frequency through a 2D-long short-term memory (LSTM) model. Extensive experimental results show a performance improvement over conventional spectrum prediction methods in terms of accuracy and computational complexity. These observations are validated over the real-world spectrum measurements, assuming a frequency range between 832-862 MHz where most of the telecom operators in Turkey have private uplink bands.

The continuous change of communication frequency brings difficulties to the reconnaissance and prediction of non-cooperative communication. The core of this communication process is the frequency-hopping (FH) sequence with pseudo-random characteristics, which controls carrier frequency hopping. However, FH sequence is always generated by a certain model and is a kind of time sequence with certain regularity. Long Short-Term Memory (LSTM) neural network in deep learning has been proved to have strong ability to solve time series problems. Therefore, in this paper, we establish LSTM model to implement FH sequence prediction. The simulation results show that LSTM-based scheme can effectively predict frequency point by point based on historical HF frequency data. Further, we achieve frequency interval prediction based on frequency point prediction.

Smart cities and societies are driving unprecedented technological and socioeconomic growth in everyday life albeit making us increasingly vulnerable to infinitely and incomprehensibly diverse threats. Short Message Service (SMS) spam is one such threat that can affect mobile security by propagating malware on mobile devices. A security breach could also cause a mobile device to send spam messages. Many works have focused on classifying incoming SMS messages. This paper proposes a tool to detect spam from outgoing SMS messages, although the work can be applied to both incoming and outgoing SMS messages. Specifically, we develop a system that comprises multiple machine learning (ML) based classifiers built by us using three classification methods – Naïve Bayes (NB), Support Vector Machine (SVM), and Naïve Bayes Multinomial (NBM)- and five preprocessing and feature extraction methods. The system is built to allow its execution in cloud, fog or edge layers, and is evaluated using 15 datasets built by 4 widely-used public SMS datasets. The system detects spam SMSs and gives recommendations on the spam filters and classifiers to be used based on user preferences including classification accuracy, True Negatives (TN), and computational resource requirements.

Recommender system helps people in decision making by asking their preferences about various items and recommends other items that have not been rated yet and are similar to their taste. A traditional recommendation system aims at generating a set of recommendations based on inter-user similarity that will satisfy the target user. Positive preferences as well as negative preferences of the users are taken into account so as to find strongly related users. Weighted entropy is usedz as a similarity measure to determine the similar taste users. The target user is asked to fill in the ratings so as to identify the closely related users from the knowledge base and top N recommendations are produced accordingly. Results show a considerable amount of improvement in accuracy after using weighted entropy and opposite preferences as a similarity measure.

Machine learning and Artificial Intelligent techniques are the most used techniques. It gives opportunity to online sharing market where sharing and adopting model is being popular. It gives attackers many new opportunities. Deep neural network is the most used approached for artificial techniques. In this paper we are presenting a Proof of Concept method to detect Trojan attacks on the Deep Neural Network. Deploying trojan models can be dangerous in normal human lives (Application like Automated vehicle). First inverse the neuron network to create general trojan triggers, and then retrain the model with external datasets to inject Trojan trigger to the model. The malicious behaviors are only activated with the trojan trigger Input. In attack, original datasets are not required to train the model. In practice, usually datasets are not shared due to privacy or copyright concerns. We use five different applications to demonstrate the attack, and perform an analysis on the factors that affect the attack. The behavior of a trojan modification can be triggered without affecting the test accuracy for normal input datasets. After generating the trojan trigger and performing an attack. It's applying SHAP as defense against such attacks. SHAP is known for its unique explanation for model predictions.

The security of integrated circuits (IC) plays a very significant role on military, economy, communication and other industries. Due to the globalization of the integrated circuit (IC) from design to manufacturing process, the IC chip is vulnerable to be implanted malicious circuit, which is known as hardware Trojan (HT). When the HT is activated, it will modify the functionality, reduce the reliability of IC, and even leak confidential information about the system and seriously threatens national security. The HT detection theory and method is hotspot in the security of integrated circuit. However, most methods are focusing on the simulated data. Moreover, the measurement data of the real circuit are greatly affected by the measurement noise and process disturbances and few methods are available with small size of the Trojan circuit. In this paper, the problem of detection was cast as signal representation among multiple linear regression and sparse representation-based classifier (SRC) were first applied for Trojan detection. We assume that the training samples from a single class do lie on a subspace, and the test samples can be represented by the single class. The proposed SRC HT detection method on real integrated circuit shows high accuracy and efficiency.