Biblio

Knowing malware types in every malware attacks is very helpful to the administrators to have proper defense policies for their system. It must be a massive benefit for the organization as well as the social if the automatic protection systems could themselves detect, classify an existence of new malware types in the whole network system with a few malware samples. This feature helps to prevent the spreading of malware as soon as any damage is caused to the networks. An approach introduced in this paper takes advantage of One-shot/few-shot learning algorithms in solving the malware classification problems by using some well-known models such as Matching Networks, Prototypical Networks. To demonstrate an efficiency of the approach, we run the experiments on the two malware datasets (namely, MalImg and Microsoft Malware Classification Challenge), and both experiments all give us very high accuracies. We confirm that if applying models correctly from the machine learning area could bring excellent performance compared to the other traditional methods, open a new area of malware research.

IoT devices can be used to fulfil many of our daily tasks. IoT could be wearable devices, home appliances, or even light bulbs. With the introduction of this new technology, however, vulnerabilities are being introduced and can be leveraged or exploited by malicious users. One common vehicle of exploitation is malicious software, or malware. Malware can be extremely harmful and compromise the confidentiality, integrity and availability (CIA triad) of information systems. This paper analyzes the types of malware attacks, introduce some mitigation approaches and discusses future challenges.

Cloud Computing as of large is evolving at a faster pace with an ever changing set of cloud services. The amenities in the cloud are all enabled with respect to the public cloud services in their own enormous domain aspects commercially, which tend to be more insecure. These cloud services should be thus protected and secured which is very vital to the cloud infrastructures. Therefore, in this research work, we have identified security features with a self-heal approach that could be rendered on the infrastructure as a service (IaaS) in a private cloud environment. We have investigated the attack model from the virtual machine snapshots and have analyzed based on the supervised machine learning techniques. The virtual machines memory snapshots API call sequences are considered as input for the supervised and unsupervised machine learning algorithms to classify the attacked and the un-attacked virtual machine memory snapshots. The obtained set of the attacked virtual machine memory snapshots are given as input to the self-heal algorithm which is enabled to retrieve back the functionality of the virtual machines. Our method of detecting the malware attains about 93% of accuracy with respect to the virtual machine snapshots.

Traditional anti-virus technologies have failed to keep pace with proliferation of malware due to slow process of their signatures and heuristics updates. Similarly, there are limitations of time and resources in order to perform manual analysis on each malware. There is a need to learn from this vast quantity of data, containing cyber attack pattern, in an automated manner to proactively adapt to ever-evolving threats. Machine learning offers unique advantages to learn from past cyber attacks to handle future cyber threats. The purpose of this research is to propose a framework for multi-classification of malware into well-known categories by applying different machine learning models over corpus of malware analysis reports. These reports are generated through an open source malware sandbox in an automated manner. We applied extensive pre-modeling techniques for data cleaning, features exploration and features engineering to prepare training and test datasets. Best possible hyper-parameters are selected to build machine learning models. These prepared datasets are then used to train the machine learning classifiers and to compare their prediction accuracy. Finally, these results are validated through a comprehensive 10-fold cross-validation methodology. The best results are achieved through Gaussian Naive Bayes classifier with random accuracy of 96% and 10-Fold Cross Validation accuracy of 91.2%. The said framework can be deployed in an operational environment to learn from malware attacks for proactively adapting matching counter measures.

Recently a huge trend on the internet of things (IoT) and an exponential increase in automated tools are helping malware producers to target IoT devices. The traditional security solutions against malware are infeasible due to low computing power for large-scale data in IoT environment. The number of malware and their variants are increasing due to continuous malware attacks. Consequently, the performance improvement in malware analysis is critical requirement to stop rapid expansion of malicious attacks in IoT environment. To solve this problem, the paper proposed a novel framework for classifying malware in IoT environment. To achieve flne-grained malware classification in suggested framework, the malware image classification system (MICS) is designed for representing malware image globally and locally. MICS first converts the suspicious program into the gray-scale image and then captures hybrid local and global malware features to perform malware family classification. Preliminary experimental outcomes of MICS are quite promising with 97.4% classification accuracy on 9342 windows suspicious programs of 25 families. The experimental results indicate that proposed framework is quite capable to process large-scale IoT malware.

Malicious traffic has garnered more attention in recent years, owing to the rapid growth of information technology in today's world. In 2007 alone, an estimated loss of 13 billion dollars was made from malware attacks. Malware data in today's context is massive. To understand such information using primitive methods would be a tedious task. In this publication we demonstrate some of the most advanced deep learning techniques available, multilayer perceptron (MLP) and J48 (also known as C4.5 or ID3) on our selected dataset, Advanced Security Network Metrics & Non-Payload-Based Obfuscations (ASNM-NPBO) to show that the answer to managing cyber security threats lie in the fore-mentioned methodologies.

The growing prevalence of cyber threats in the world are affecting every network user. Numerous security monitoring systems are being employed to protect computer networks and resources from falling victim to cyber-attacks. There is a pressing need to have an efficient security monitoring system to monitor the large network datasets generated in this process. A large network datasets representing Malware attacks have been used in this work to establish an expert system. The characteristics of attacker's IP addresses can be extracted from our integrated datasets to generate statistical data. The cyber security expert provides to the weight of each attribute and forms a scoring system by annotating the log history. We adopted a special semi supervise method to classify cyber security log into attack, unsure and no attack by first breaking the data into 3 cluster using Fuzzy K mean (FKM), then manually label a small data (Analyst Intuition) and finally train the neural network classifier multilayer perceptron (MLP) base on the manually labelled data. By doing so, our results is very encouraging as compare to finding anomaly in a cyber security log, which generally results in creating huge amount of false detection. The method of including Artificial Intelligence (AI) and Analyst Intuition (AI) is also known as AI2. The classification results are encouraging in segregating the types of attacks.

The large number of malicious files that are produced daily outpaces the current capacity of malware analysis and detection. For example, Intel Security Labs reported that during the second quarter of 2016, their system found more than 40M of new malware [1]. The damage of malware attacks is also increasingly devastating, as witnessed by the recent Cryptowall malware that has reportedly generated more than \$325M in ransom payments to its perpetrators [2]. In terms of defense, it has been widely accepted that the traditional approach based on byte-string signatures is increasingly ineffective, especially for new malware samples and sophisticated variants of existing ones. New techniques are therefore needed for effective defense against malware. Motivated by this problem, the paper investigates a new defense technique against malware. The technique presented in this paper is utilized for automatic identification of malware packers that are used to obfuscate malware programs. Signatures of malware packers and obfuscators are extracted from the CFGs of malware samples. Unlike conventional byte signatures that can be evaded by simply modifying one or multiple bytes in malware samples, these signatures are more difficult to evade. For example, CFG-based signatures are shown to be resilient against instruction modifications and shuffling, as a single signature is sufficient for detecting mildly different versions of the same malware. Last but not least, the process for extracting CFG-based signatures is also made automatic.

Today the technology advancement in communication technology permits a malware author to introduce code obfuscation technique, for example, Application Programming Interface (API) hook, to make detecting the footprints of their code more difficult. A signature-based model such as Antivirus software is not effective against such attacks. In this paper, an API graph-based model is proposed with the objective of detecting hook attacks during malicious code execution. The proposed model incorporates techniques such as graph-generation, graph partition and graph comparison to distinguish a legitimate system call from malicious system call. The simulation results confirm that the proposed model outperforms than existing approaches.

Given a history of detected malware attacks, can we predict the number of malware infections in a country? Can we do this for different malware and countries? This is an important question which has numerous implications for cyber security, right from designing better anti-virus software, to designing and implementing targeted patches to more accurately measuring the economic impact of breaches. This problem is compounded by the fact that, as externals, we can only detect a fraction of actual malware infections. In this paper we address this problem using data from Symantec covering more than 1.4 million hosts and 50 malware spread across 2 years and multiple countries. We first carefully design domain-based features from both malware and machine-hosts perspectives. Secondly, inspired by epidemiological and information diffusion models, we design a novel temporal non-linear model for malware spread and detection. Finally we present ESM, an ensemble-based approach which combines both these methods to construct a more accurate algorithm. Using extensive experiments spanning multiple malware and countries, we show that ESM can effectively predict malware infection ratios over time (both the actual number and trend) upto 4 times better compared to several baselines on various metrics. Furthermore, ESM's performance is stable and robust even when the number of detected infections is low.

This paper presents a model to evaluate and select security countermeasures from a pool of candidates. The model performs industrial evaluation and simulations of the financial and technical impact associated to security countermeasures. The financial impact approach uses the Return On Response Investment (RORI) index to compare the expected impact of the attack when no response is enacted against the impact after applying security countermeasures. The technical impact approach evaluates the protection level against a threat, in terms of confidentiality, integrity, and availability. We provide a use case on malware attacks that shows the applicability of our model in selecting the best countermeasure against an Advanced Persistent Threat.