Biblio

Botnet is one of the threats to internet network security-Botmaster in carrying out attacks on the network by relying on communication on network traffic. Internet of Things (IoT) network infrastructure consists of devices that are inexpensive, low-power, always-on, always connected to the network, and are inconspicuous and have ubiquity and inconspicuousness characteristics so that these characteristics make IoT devices an attractive target for botnet malware attacks. In identifying whether packet traffic is a malware attack or not, one can use machine learning classification methods. By using Weka and Scikit-learn analysis tools machine learning, this paper implements four machine learning algorithms, i.e.: AdaBoost, Decision Tree, Random Forest, and Naïve Bayes. Then experiments are conducted to measure the performance of the four algorithms in terms of accuracy, execution time, and false positive rate (FPR). Experiment results show that the Weka tool provides more accurate and efficient classification methods. However, in false positive rate, the use of Scikit-learn provides better results.

Recently, as the threat of Distributed Denial-of-Service attacks exploiting IoT devices has spread, source-side Denial-of-Service attack detection methods are being studied in order to quickly detect attacks and find their locations. Moreover, to mitigate the limitation of local view of source-side detection, a collaborative attack detection technique is required to share detection results on each source-side network. In this paper, a new collaborative source-side DDoS attack detection method is proposed for detecting DDoS attacks on multiple networks more correctly, by considering the detecting performance on different time zone. The results of individual attack detection on each network are weighted based on detection rate and false positive rate corresponding to the time zone of each network. By gathering the weighted detection results, the proposed method determines whether a DDoS attack happens. Through extensive evaluation with real network traffic data, it is confirmed that the proposed method reduces false positive rate by 35% while maintaining high detection rate.

Deep learning is a popular powerful machine learning solution to the computer vision tasks. The most criticized vulnerability of deep learning is its poor tolerance towards adversarial images obtained by deliberately adding imperceptibly small perturbations to the clean inputs. Such negatives can delude a classifier into wrong decision making. Previous defensive techniques mostly focused on refining the models or input transformation. They are either implemented only with small datasets or shown to have limited success. Furthermore, they are rarely scrutinized from the hardware perspective despite Artificial Intelligence (AI) on a chip is a roadmap for embedded intelligence everywhere. In this paper we propose a new discriminative noise injection strategy to adaptively select a few dominant layers and progressively discriminate adversarial from benign inputs. This is made possible by evaluating the differences in label change rate from both adversarial and natural images by injecting different amount of noise into the weights of individual layers in the model. The approach is evaluated on the ImageNet Dataset with 8-bit truncated models for the state-of-the-art DNN architectures. The results show a high detection rate of up to 88.00% with only approximately 5% of false positive rate for MobileNet. Both detection rate and false positive rate have been improved well above existing advanced defenses against the most practical noninvasive universal perturbation attack on deep learning based AI chip.

Anomaly detection on security logs is receiving more and more attention. Authentication events are an important component of security logs, and being able to produce trustful and accurate predictions minimizes the effort of cyber-experts to stop false attacks. Observed events are classified into Normal, for legitimate user behavior, and Malicious, for malevolent actions. These classes are consistently excessively imbalanced which makes the classification problem harder; in the commonly used Los Alamos dataset, the malicious class comprises only 0.00033% of the total. This work proposes a novel method to extract advanced composite features, and a supervised learning technique for classifying authentication logs trustfully; the models are Random Forest, LogitBoost, Logistic Regression, and ultimately Majority Voting which leverages the predictions of the previous models and gives the final prediction for each authentication event. We measure the performance of our experiments by using the False Negative Rate and False Positive Rate. In overall we achieve 0 False Negative Rate (i.e. no attack was missed), and on average a False Positive Rate of 0.0019.

With the increase in the popularity of computerized online applications, the analysis, and detection of a growing number of newly discovered stealthy malware poses a significant challenge to the security community. Signature-based and behavior-based detection techniques are becoming inefficient in detecting new unknown malware. Machine learning solutions are employed to counter such intelligent malware and allow performing more comprehensive malware detection. This capability leads to an automatic analysis of malware behavior. The proposed oblique random forest ensemble learning technique is efficient for malware classification. The effectiveness of the proposed method is demonstrated with three malware classification datasets from various sources. The results are compared with other variants of decision tree learning models. The proposed system performs better than the existing system in terms of classification accuracy and false positive rate.

Due to the unavailability of signatures for previously unknown malware, non-signature malware detection schemes typically rely on analyzing program behavior. Prior behavior based non-signature malware detection schemes are either easily evadable by obfuscation or are very inefficient in terms of storage space and detection time. In this paper, we propose GZero, a graph theoretic approach fast and accurate non-signature malware detection at end hosts. GZero it is effective while being efficient in terms of both storage space and detection time. We conducted experiments on a large set of both benign software and malware. Our results show that GZero achieves more than 99% detection rate and a false positive rate of less than 1%, with less than 1 second of average scan time per program and is relatively robust to obfuscation attacks. Due to its low overheads, GZero can complement existing malware detection solutions at end hosts.

We propose a new Bloom filter structure for searchable encryption schemes in which a large Bloom filter is treated as (replaced with) two smaller ones for the search index. False positive is one inherent drawback of Bloom filter. We formulate the false positive rates for one regular large Bloom filter, and then derive the false positive rate for the two smaller ones. With examples, we show how the new scheme cuts down the false positive rate and the size of Bloom filter to a balanced point that fulfills the user requirements and increases the efficiency of the structure.

With the ubiquitous computing of providing services and applications at anywhere and anytime, cloud computing is the best option as it offers flexible and pay-per-use based services to its customers. Nevertheless, security and privacy are the main challenges to its success due to its dynamic and distributed architecture, resulting in generating big data that should be carefully analysed for detecting network's vulnerabilities. In this paper, we propose a Collaborative Anomaly Detection Framework (CADF) for detecting cyber attacks from cloud computing environments. We provide the technical functions and deployment of the framework to illustrate its methodology of implementation and installation. The framework is evaluated on the UNSW-NB15 dataset to check its credibility while deploying it in cloud computing environments. The experimental results showed that this framework can easily handle large-scale systems as its implementation requires only estimating statistical measures from network observations. Moreover, the evaluation performance of the framework outperforms three state-of-the-art techniques in terms of false positive rate and detection rate.

Online Social Networks (OSNs) are continuously suffering from the negative impact of Cross-Site Scripting (XSS) vulnerabilities. This paper describes a novel framework for mitigating XSS attack on OSN-based platforms. It is completely based on the request authentication and view isolation approach. It detects XSS attack through validating string value extracted from the vulnerable checkpoint present in the web page by implementing string examination algorithm with the help of XSS attack vector repository. Any similarity (i.e. string is not validated) indicates the presence of malicious code injected by the attacker and finally it removes the script code to mitigate XSS attack. To assess the defending ability of our designed model, we have tested it on OSN-based web application i.e. Humhub. The experimental results revealed that our model discovers the XSS attack vectors with low false negatives and false positive rate tolerable performance overhead.

Volume anomaly such as distributed denial-of-service (DDoS) has been around for ages but with advancement in technologies, they have become stronger, shorter and weapon of choice for attackers. Digital forensic analysis of intrusions using alerts generated by existing intrusion detection system (IDS) faces major challenges, especially for IDS deployed in large networks. In this paper, the concept of automatically sifting through a huge volume of alerts to distinguish the different stages of a DDoS attack is developed. The proposed novel framework is purpose-built to analyze multiple logs from the network for proactive forecast and timely detection of DDoS attacks, through a combined approach of Shannon-entropy concept and clustering algorithm of relevant feature variables. Experimental studies on a cyber-range simulation dataset from the project industrial partners show that the technique is able to distinguish precursor alerts for DDoS attacks, as well as the attack itself with a very low false positive rate (FPR) of 22.5%. Application of this technique greatly assists security experts in network analysis to combat DDoS attacks.