Invesitigation of Malware and Forensic Tools on Internet

Kumar, Tarun; Sharma, Sanjeev; Dhaundiyal, Ravi; Jain, Parag

doi:10.11591/ijece.v8i5.pp3179-3186

Cited by 4 publications

(3 citation statements)

References 28 publications

(40 reference statements)

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“…Malware, or more precisely, malicious software, stands for software or program which infiltrates and damages computer systems with no prior consent to the system owner. Over the years, malware is indicated as a security threat by cyber security standards [1]. Malware includes viruses, Trojan horses, worms, exploits, botnets, and retroviruses [2].…”

Section: Introductionmentioning

confidence: 99%

X-ware: a proof of concept malware utilizing artificial intelligence

Truong

Diep

Plucar

2022

IJECE

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<p>Recent years have witnessed a dramatic growth in utilizing computational intelligence techniques for various domains. Coherently, malicious actors are expected to utilize these techniques against current security solutions. Despite the importance of these new potential threats, there remains a paucity of evidence on leveraging these research literature techniques. This article investigates the possibility of combining artificial neural networks and swarm intelligence to generate a new type of malware. We successfully created a proof of concept malware named X-ware, which we tested against the Windows-based systems. Developing this proof of concept may allow us to identify this potential threat’s characteristics for developing mitigation methods in the future. Furthermore, a method for recording the virus’s behavior and propagation throughout a file system is presented. The proposed virus prototype acts as a swarm system with a neural network-integrated for operations. The virus’s behavioral data is recorded and shown under a complex network format to describe the behavior and communication of the swarm. This paper has demonstrated that malware strengthened with computational intelligence is a credible threat. We envisage that our study can be utilized to assist current and future security researchers to help in implementing more effective countermeasures</p>

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Section: Introductionmentioning

confidence: 99%

X-ware: a proof of concept malware utilizing artificial intelligence

Truong

Diep

Plucar

2022

IJECE

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“…Nowadays the cybercriminals implement different ways to obtain the profit from the legitimate businesses, which have become theirs target. Malware are one of the most powerful cybercriminals' tools for attaining such goals [1][2]. One the type of the malicious action against the users' computer systems, cloud infrastructure the distributed denial-of-service (DDoS) attacks-the attempt to disrupt normal traffic of a targeted server, service or network by overwhelming the target or its surrounding infrastructure with a flood of Internet traffic [3].…”

Section: Introductionmentioning

confidence: 99%

Detection of the botnets’ low-rate DDoS attacks based on self-similarity

Lysenko¹,

Bobrovnikova²,

Matiukh³

et al. 2020

IJECE

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An article presents the approach for the botnets’ low-rate a DDoS-attacks detection based on the botnet’s behavior in the network. Detection process involves the analysis of the network traffic, generated by the botnets’ low-rate DDoS attack. Proposed technique is the part of botnets detection system – BotGRABBER system. The novelty of the paper is that the low-rate DDoS-attacks detection involves not only the network features, inherent to the botnets, but also network traffic self-similarity analysis, which is defined with the use of Hurst coefficient. Detection process consists of the knowledge formation based on the features that may indicate low-rate DDoS attack performed by a botnet; network monitoring, which analyzes information obtained from the network and making conclusion about possible DDoS attack in the network; and the appliance of the security scenario for the corporate area network’s infrastructure in the situation of low-rate attacks.

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“…With data from a single credit card being sold for up to $45 on underground markets, just 10 credit cards stolen from compromised websites could result in a yield of up to $2.2 million for cyber criminals each month. Cyberattacks primarily exploit software vulnerabilities [3,4]. Thus, it is essential to find and manage such vulnerabilities at an early stage to prevent various types of damage, e.g., revealing confidential information or financial harm to both corporate enterprises and general users.…”

Section: Introductionmentioning

confidence: 99%

Countermeasures against darknet localisation attacks with packet sampling

Narita

Kamada

Ogura

et al. 2020

IJEECS

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<p>The darknet monitoring system consists of network sensors widely deployed on the Internet to capture incoming unsolicited packets. A goal of this system is to analyse captured malicious packets and provide effective information to protect regular nonmalicious Internet users from malicious activities. To provide effective and reliable information, the location of sensors must be concealed. However, attackers launch localisation attacks to detect sensors in order to evade them. If the actual location of sensors is revealed, it is almost impossible to identify the latest tactics used by attackers. Thus, in a previous study, we proposed a packet sampling method, which samples incoming packets based on an attribute of the packet sender, to increase tolerance to a localisation attack and maintain the quality of information publicised by the system. We were successful in countering localisation attacks, which generate spikes on the publicised graph to detect a sensor. However, in some cases, with the previously proposed sampling method, spikes were clearly evident on the graph. Therefore, in this paper, we propose advanced sampling methods such that incoming packets are sampled based on multiple attributes of the packet sender. We present our improved methods and show promising evaluation results obtained via a simulation.</p>

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Invesitigation of Malware and Forensic Tools on Internet

Cited by 4 publications

References 28 publications

X-ware: a proof of concept malware utilizing artificial intelligence

X-ware: a proof of concept malware utilizing artificial intelligence

Detection of the botnets’ low-rate DDoS attacks based on self-similarity

Countermeasures against darknet localisation attacks with packet sampling

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